Integrated Project 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V4</b>\n",
    "\n",
    "The project is accepted! Good luck on the next sprint! \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Review**\n",
    "\n",
    "Hi, my name is Dmitry and I will be reviewing your project.\n",
    "  \n",
    "You can find my comments in colored markdown cells:\n",
    "  \n",
    "<div class=\"alert alert-success\">\n",
    "  If everything is done successfully.\n",
    "</div>\n",
    "  \n",
    "<div class=\"alert alert-warning\">\n",
    "  If I have some (optional) suggestions, or questions to think about, or general comments.\n",
    "</div>\n",
    "  \n",
    "<div class=\"alert alert-danger\">\n",
    "  If a section requires some corrections. Work can't be accepted with red comments.\n",
    "</div>\n",
    "  \n",
    "Please don't remove my comments, as it will make further review iterations much harder for me.\n",
    "  \n",
    "Feel free to reply to my comments or ask questions using the following template:\n",
    "  \n",
    "<div class=\"alert alert-info\">\n",
    "  For your comments and questions.\n",
    "</div>\n",
    "  \n",
    "First of all, thank you for turning in the project! You did a great job overall, but there are some problems that need to be fixed before the project is accepted. Let me know if you have any questions!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Model for Zyfra\n",
    "I have been tasked to prepare a prototype of a machine learning model for Zyfra, a company which develops efficiency solutions for heavy industry.\n",
    "\n",
    "The model should predict the amount of gold recovered from gold ore. I have the data on extraction and purification.\n",
    "\n",
    "The model will help to optimize the production and eliminate unprofitable parameters.\n",
    "Steps for my process:\n",
    "- Prepare the data;\n",
    "- Perform data analysis;\n",
    "- Develop and train a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will import what I need to complete this task and then import the data taking my first looks at the condition of the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project libraries has been successfully been imported\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "pd.options.mode.chained_assignment = None \n",
    "from sklearn.dummy import DummyRegressor \n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import *\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print('Project libraries has been successfully been imported')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has been read correctly\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    gold_recovery_train = pd.read_csv('/datasets/gold_recovery_train.csv')\n",
    "    gold_recovery_test = pd.read_csv('/datasets/gold_recovery_test.csv')\n",
    "    gold_recovery_full = pd.read_csv('/datasets/gold_recovery_full.csv')\n",
    "except:\n",
    "    gold_recovery_train = pd.read_csv('C:/Users/hotty/Desktop/Practicum by Yandex/Projects/Integrated Project 2/gold_recovery_train.csv')\n",
    "    gold_recovery_test = pd.read_csv('C:/Users/hotty/Desktop/Practicum by Yandex/Projects/Integrated Project 2/gold_recovery_test.csv')\n",
    "    gold_recovery_full = pd.read_csv('C:/Users/hotty/Desktop/Practicum by Yandex/Projects/Integrated Project 2/gold_recovery_full.csv')\n",
    "print('Data has been read correctly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_percent_of_na(df, num):\n",
    "    count = 0\n",
    "    df = df.copy()\n",
    "    s = (df.isna().sum() / df.shape[0])\n",
    "    for column, percent in zip(s.index, s.values):\n",
    "        num_of_nulls = df[column].isna().sum()\n",
    "        if num_of_nulls == 0:\n",
    "            continue\n",
    "        else:\n",
    "            count += 1\n",
    "        print('Column {} has {:.{}%} percent of Nulls, and {} of nulls'.format(column, percent, num, num_of_nulls))\n",
    "    if count != 0:\n",
    "        print(\"\\033[1m\" + 'There are {} columns with NA.'.format(count) + \"\\033[0m\")\n",
    "    else:\n",
    "        print()\n",
    "        print(\"\\033[1m\" + 'There are no columns with NA.' + \"\\033[0m\")\n",
    "        \n",
    "def get_info(df):\n",
    "    \"\"\"\n",
    "    This function uses the head(), info(), describe(), shape() and duplicated() \n",
    "    methods to display the general information about the dataset.\n",
    "    \"\"\"\n",
    "    print(\"\\033[1m\" + '-'*100 + \"\\033[0m\")\n",
    "    print('Head:')\n",
    "    print()\n",
    "    display(df.head())\n",
    "    print('-'*100)\n",
    "    print('Info:')\n",
    "    print()\n",
    "    display(df.info())\n",
    "    print('-'*100)\n",
    "    print('Describe:')\n",
    "    print()\n",
    "    display(df.describe())\n",
    "    print('-'*100)\n",
    "    display(df.describe(include='object'))\n",
    "    print()\n",
    "    print('Columns with nulls:')\n",
    "    display(get_percent_of_na(df, 4))  \n",
    "    print('-'*100)\n",
    "    print('Shape:')\n",
    "    print(df.shape)\n",
    "    print('-'*100)\n",
    "    print('Duplicated:')\n",
    "    print(\"\\033[1m\" + 'We have {} duplicated rows.\\n'.format(df.duplicated().sum()) + \"\\033[0m\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General information about the dataframe\n",
      "\u001b[1m----------------------------------------------------------------------------------------------------\u001b[0m\n",
      "Head:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "      <td>6.055403</td>\n",
       "      <td>9.889648</td>\n",
       "      <td>5.507324</td>\n",
       "      <td>42.192020</td>\n",
       "      <td>70.541216</td>\n",
       "      <td>10.411962</td>\n",
       "      <td>0.895447</td>\n",
       "      <td>16.904297</td>\n",
       "      <td>2.143149</td>\n",
       "      <td>...</td>\n",
       "      <td>14.016835</td>\n",
       "      <td>-502.488007</td>\n",
       "      <td>12.099931</td>\n",
       "      <td>-504.715942</td>\n",
       "      <td>9.925633</td>\n",
       "      <td>-498.310211</td>\n",
       "      <td>8.079666</td>\n",
       "      <td>-500.470978</td>\n",
       "      <td>14.151341</td>\n",
       "      <td>-605.841980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>6.029369</td>\n",
       "      <td>9.968944</td>\n",
       "      <td>5.257781</td>\n",
       "      <td>42.701629</td>\n",
       "      <td>69.266198</td>\n",
       "      <td>10.462676</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>16.634514</td>\n",
       "      <td>2.224930</td>\n",
       "      <td>...</td>\n",
       "      <td>13.992281</td>\n",
       "      <td>-505.503262</td>\n",
       "      <td>11.950531</td>\n",
       "      <td>-501.331529</td>\n",
       "      <td>10.039245</td>\n",
       "      <td>-500.169983</td>\n",
       "      <td>7.984757</td>\n",
       "      <td>-500.582168</td>\n",
       "      <td>13.998353</td>\n",
       "      <td>-599.787184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>6.055926</td>\n",
       "      <td>10.213995</td>\n",
       "      <td>5.383759</td>\n",
       "      <td>42.657501</td>\n",
       "      <td>68.116445</td>\n",
       "      <td>10.507046</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>16.208849</td>\n",
       "      <td>2.257889</td>\n",
       "      <td>...</td>\n",
       "      <td>14.015015</td>\n",
       "      <td>-502.520901</td>\n",
       "      <td>11.912783</td>\n",
       "      <td>-501.133383</td>\n",
       "      <td>10.070913</td>\n",
       "      <td>-500.129135</td>\n",
       "      <td>8.013877</td>\n",
       "      <td>-500.517572</td>\n",
       "      <td>14.028663</td>\n",
       "      <td>-601.427363</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>6.047977</td>\n",
       "      <td>9.977019</td>\n",
       "      <td>4.858634</td>\n",
       "      <td>42.689819</td>\n",
       "      <td>68.347543</td>\n",
       "      <td>10.422762</td>\n",
       "      <td>0.883763</td>\n",
       "      <td>16.532835</td>\n",
       "      <td>2.146849</td>\n",
       "      <td>...</td>\n",
       "      <td>14.036510</td>\n",
       "      <td>-500.857308</td>\n",
       "      <td>11.999550</td>\n",
       "      <td>-501.193686</td>\n",
       "      <td>9.970366</td>\n",
       "      <td>-499.201640</td>\n",
       "      <td>7.977324</td>\n",
       "      <td>-500.255908</td>\n",
       "      <td>14.005551</td>\n",
       "      <td>-599.996129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>6.148599</td>\n",
       "      <td>10.142511</td>\n",
       "      <td>4.939416</td>\n",
       "      <td>42.774141</td>\n",
       "      <td>66.927016</td>\n",
       "      <td>10.360302</td>\n",
       "      <td>0.792826</td>\n",
       "      <td>16.525686</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>...</td>\n",
       "      <td>14.027298</td>\n",
       "      <td>-499.838632</td>\n",
       "      <td>11.953070</td>\n",
       "      <td>-501.053894</td>\n",
       "      <td>9.925709</td>\n",
       "      <td>-501.686727</td>\n",
       "      <td>7.894242</td>\n",
       "      <td>-500.356035</td>\n",
       "      <td>13.996647</td>\n",
       "      <td>-601.496691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 87 columns</p>\n",
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      "text/plain": [
       "                  date  final.output.concentrate_ag  \\\n",
       "0  2016-01-15 00:00:00                     6.055403   \n",
       "1  2016-01-15 01:00:00                     6.029369   \n",
       "2  2016-01-15 02:00:00                     6.055926   \n",
       "3  2016-01-15 03:00:00                     6.047977   \n",
       "4  2016-01-15 04:00:00                     6.148599   \n",
       "\n",
       "   final.output.concentrate_pb  final.output.concentrate_sol  \\\n",
       "0                     9.889648                      5.507324   \n",
       "1                     9.968944                      5.257781   \n",
       "2                    10.213995                      5.383759   \n",
       "3                     9.977019                      4.858634   \n",
       "4                    10.142511                      4.939416   \n",
       "\n",
       "   final.output.concentrate_au  final.output.recovery  final.output.tail_ag  \\\n",
       "0                    42.192020              70.541216             10.411962   \n",
       "1                    42.701629              69.266198             10.462676   \n",
       "2                    42.657501              68.116445             10.507046   \n",
       "3                    42.689819              68.347543             10.422762   \n",
       "4                    42.774141              66.927016             10.360302   \n",
       "\n",
       "   final.output.tail_pb  final.output.tail_sol  final.output.tail_au  ...  \\\n",
       "0              0.895447              16.904297              2.143149  ...   \n",
       "1              0.927452              16.634514              2.224930  ...   \n",
       "2              0.953716              16.208849              2.257889  ...   \n",
       "3              0.883763              16.532835              2.146849  ...   \n",
       "4              0.792826              16.525686              2.055292  ...   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_air  \\\n",
       "0                                 14.016835   \n",
       "1                                 13.992281   \n",
       "2                                 14.015015   \n",
       "3                                 14.036510   \n",
       "4                                 14.027298   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_level  \\\n",
       "0                                 -502.488007   \n",
       "1                                 -505.503262   \n",
       "2                                 -502.520901   \n",
       "3                                 -500.857308   \n",
       "4                                 -499.838632   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_air  \\\n",
       "0                                 12.099931   \n",
       "1                                 11.950531   \n",
       "2                                 11.912783   \n",
       "3                                 11.999550   \n",
       "4                                 11.953070   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_level  \\\n",
       "0                                 -504.715942   \n",
       "1                                 -501.331529   \n",
       "2                                 -501.133383   \n",
       "3                                 -501.193686   \n",
       "4                                 -501.053894   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_air  \\\n",
       "0                                  9.925633   \n",
       "1                                 10.039245   \n",
       "2                                 10.070913   \n",
       "3                                  9.970366   \n",
       "4                                  9.925709   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_level  \\\n",
       "0                                 -498.310211   \n",
       "1                                 -500.169983   \n",
       "2                                 -500.129135   \n",
       "3                                 -499.201640   \n",
       "4                                 -501.686727   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_air  \\\n",
       "0                                  8.079666   \n",
       "1                                  7.984757   \n",
       "2                                  8.013877   \n",
       "3                                  7.977324   \n",
       "4                                  7.894242   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_level  \\\n",
       "0                                 -500.470978   \n",
       "1                                 -500.582168   \n",
       "2                                 -500.517572   \n",
       "3                                 -500.255908   \n",
       "4                                 -500.356035   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_air  \\\n",
       "0                                 14.151341   \n",
       "1                                 13.998353   \n",
       "2                                 14.028663   \n",
       "3                                 14.005551   \n",
       "4                                 13.996647   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_level  \n",
       "0                                 -605.841980  \n",
       "1                                 -599.787184  \n",
       "2                                 -601.427363  \n",
       "3                                 -599.996129  \n",
       "4                                 -601.496691  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
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     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Info:\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16860 entries, 0 to 16859\n",
      "Data columns (total 87 columns):\n",
      " #   Column                                              Non-Null Count  Dtype  \n",
      "---  ------                                              --------------  -----  \n",
      " 0   date                                                16860 non-null  object \n",
      " 1   final.output.concentrate_ag                         16788 non-null  float64\n",
      " 2   final.output.concentrate_pb                         16788 non-null  float64\n",
      " 3   final.output.concentrate_sol                        16490 non-null  float64\n",
      " 4   final.output.concentrate_au                         16789 non-null  float64\n",
      " 5   final.output.recovery                               15339 non-null  float64\n",
      " 6   final.output.tail_ag                                16794 non-null  float64\n",
      " 7   final.output.tail_pb                                16677 non-null  float64\n",
      " 8   final.output.tail_sol                               16715 non-null  float64\n",
      " 9   final.output.tail_au                                16794 non-null  float64\n",
      " 10  primary_cleaner.input.sulfate                       15553 non-null  float64\n",
      " 11  primary_cleaner.input.depressant                    15598 non-null  float64\n",
      " 12  primary_cleaner.input.feed_size                     16860 non-null  float64\n",
      " 13  primary_cleaner.input.xanthate                      15875 non-null  float64\n",
      " 14  primary_cleaner.output.concentrate_ag               16778 non-null  float64\n",
      " 15  primary_cleaner.output.concentrate_pb               16502 non-null  float64\n",
      " 16  primary_cleaner.output.concentrate_sol              16224 non-null  float64\n",
      " 17  primary_cleaner.output.concentrate_au               16778 non-null  float64\n",
      " 18  primary_cleaner.output.tail_ag                      16777 non-null  float64\n",
      " 19  primary_cleaner.output.tail_pb                      16761 non-null  float64\n",
      " 20  primary_cleaner.output.tail_sol                     16579 non-null  float64\n",
      " 21  primary_cleaner.output.tail_au                      16777 non-null  float64\n",
      " 22  primary_cleaner.state.floatbank8_a_air              16820 non-null  float64\n",
      " 23  primary_cleaner.state.floatbank8_a_level            16827 non-null  float64\n",
      " 24  primary_cleaner.state.floatbank8_b_air              16820 non-null  float64\n",
      " 25  primary_cleaner.state.floatbank8_b_level            16833 non-null  float64\n",
      " 26  primary_cleaner.state.floatbank8_c_air              16822 non-null  float64\n",
      " 27  primary_cleaner.state.floatbank8_c_level            16833 non-null  float64\n",
      " 28  primary_cleaner.state.floatbank8_d_air              16821 non-null  float64\n",
      " 29  primary_cleaner.state.floatbank8_d_level            16833 non-null  float64\n",
      " 30  rougher.calculation.sulfate_to_au_concentrate       16833 non-null  float64\n",
      " 31  rougher.calculation.floatbank10_sulfate_to_au_feed  16833 non-null  float64\n",
      " 32  rougher.calculation.floatbank11_sulfate_to_au_feed  16833 non-null  float64\n",
      " 33  rougher.calculation.au_pb_ratio                     15618 non-null  float64\n",
      " 34  rougher.input.feed_ag                               16778 non-null  float64\n",
      " 35  rougher.input.feed_pb                               16632 non-null  float64\n",
      " 36  rougher.input.feed_rate                             16347 non-null  float64\n",
      " 37  rougher.input.feed_size                             16443 non-null  float64\n",
      " 38  rougher.input.feed_sol                              16568 non-null  float64\n",
      " 39  rougher.input.feed_au                               16777 non-null  float64\n",
      " 40  rougher.input.floatbank10_sulfate                   15816 non-null  float64\n",
      " 41  rougher.input.floatbank10_xanthate                  16514 non-null  float64\n",
      " 42  rougher.input.floatbank11_sulfate                   16237 non-null  float64\n",
      " 43  rougher.input.floatbank11_xanthate                  14956 non-null  float64\n",
      " 44  rougher.output.concentrate_ag                       16778 non-null  float64\n",
      " 45  rougher.output.concentrate_pb                       16778 non-null  float64\n",
      " 46  rougher.output.concentrate_sol                      16698 non-null  float64\n",
      " 47  rougher.output.concentrate_au                       16778 non-null  float64\n",
      " 48  rougher.output.recovery                             14287 non-null  float64\n",
      " 49  rougher.output.tail_ag                              14610 non-null  float64\n",
      " 50  rougher.output.tail_pb                              16778 non-null  float64\n",
      " 51  rougher.output.tail_sol                             14611 non-null  float64\n",
      " 52  rougher.output.tail_au                              14611 non-null  float64\n",
      " 53  rougher.state.floatbank10_a_air                     16807 non-null  float64\n",
      " 54  rougher.state.floatbank10_a_level                   16807 non-null  float64\n",
      " 55  rougher.state.floatbank10_b_air                     16807 non-null  float64\n",
      " 56  rougher.state.floatbank10_b_level                   16807 non-null  float64\n",
      " 57  rougher.state.floatbank10_c_air                     16807 non-null  float64\n",
      " 58  rougher.state.floatbank10_c_level                   16814 non-null  float64\n",
      " 59  rougher.state.floatbank10_d_air                     16802 non-null  float64\n",
      " 60  rougher.state.floatbank10_d_level                   16809 non-null  float64\n",
      " 61  rougher.state.floatbank10_e_air                     16257 non-null  float64\n",
      " 62  rougher.state.floatbank10_e_level                   16809 non-null  float64\n",
      " 63  rougher.state.floatbank10_f_air                     16802 non-null  float64\n",
      " 64  rougher.state.floatbank10_f_level                   16802 non-null  float64\n",
      " 65  secondary_cleaner.output.tail_ag                    16776 non-null  float64\n",
      " 66  secondary_cleaner.output.tail_pb                    16764 non-null  float64\n",
      " 67  secondary_cleaner.output.tail_sol                   14874 non-null  float64\n",
      " 68  secondary_cleaner.output.tail_au                    16778 non-null  float64\n",
      " 69  secondary_cleaner.state.floatbank2_a_air            16497 non-null  float64\n",
      " 70  secondary_cleaner.state.floatbank2_a_level          16751 non-null  float64\n",
      " 71  secondary_cleaner.state.floatbank2_b_air            16705 non-null  float64\n",
      " 72  secondary_cleaner.state.floatbank2_b_level          16748 non-null  float64\n",
      " 73  secondary_cleaner.state.floatbank3_a_air            16763 non-null  float64\n",
      " 74  secondary_cleaner.state.floatbank3_a_level          16747 non-null  float64\n",
      " 75  secondary_cleaner.state.floatbank3_b_air            16752 non-null  float64\n",
      " 76  secondary_cleaner.state.floatbank3_b_level          16750 non-null  float64\n",
      " 77  secondary_cleaner.state.floatbank4_a_air            16731 non-null  float64\n",
      " 78  secondary_cleaner.state.floatbank4_a_level          16747 non-null  float64\n",
      " 79  secondary_cleaner.state.floatbank4_b_air            16768 non-null  float64\n",
      " 80  secondary_cleaner.state.floatbank4_b_level          16767 non-null  float64\n",
      " 81  secondary_cleaner.state.floatbank5_a_air            16775 non-null  float64\n",
      " 82  secondary_cleaner.state.floatbank5_a_level          16775 non-null  float64\n",
      " 83  secondary_cleaner.state.floatbank5_b_air            16775 non-null  float64\n",
      " 84  secondary_cleaner.state.floatbank5_b_level          16776 non-null  float64\n",
      " 85  secondary_cleaner.state.floatbank6_a_air            16757 non-null  float64\n",
      " 86  secondary_cleaner.state.floatbank6_a_level          16775 non-null  float64\n",
      "dtypes: float64(86), object(1)\n",
      "memory usage: 11.2+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Describe:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16788.000000</td>\n",
       "      <td>16788.000000</td>\n",
       "      <td>16490.000000</td>\n",
       "      <td>16789.000000</td>\n",
       "      <td>15339.000000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>16677.000000</td>\n",
       "      <td>16715.000000</td>\n",
       "      <td>16794.000000</td>\n",
       "      <td>15553.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16731.000000</td>\n",
       "      <td>16747.000000</td>\n",
       "      <td>16768.000000</td>\n",
       "      <td>16767.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "      <td>16776.000000</td>\n",
       "      <td>16757.000000</td>\n",
       "      <td>16775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.716907</td>\n",
       "      <td>9.113559</td>\n",
       "      <td>8.301123</td>\n",
       "      <td>39.467217</td>\n",
       "      <td>67.213166</td>\n",
       "      <td>8.757048</td>\n",
       "      <td>2.360327</td>\n",
       "      <td>9.303932</td>\n",
       "      <td>2.687512</td>\n",
       "      <td>129.479789</td>\n",
       "      <td>...</td>\n",
       "      <td>19.101874</td>\n",
       "      <td>-494.164481</td>\n",
       "      <td>14.778164</td>\n",
       "      <td>-476.600082</td>\n",
       "      <td>15.779488</td>\n",
       "      <td>-500.230146</td>\n",
       "      <td>12.377241</td>\n",
       "      <td>-498.956257</td>\n",
       "      <td>18.429208</td>\n",
       "      <td>-521.801826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.096718</td>\n",
       "      <td>3.389495</td>\n",
       "      <td>3.825760</td>\n",
       "      <td>13.917227</td>\n",
       "      <td>11.960446</td>\n",
       "      <td>3.634103</td>\n",
       "      <td>1.215576</td>\n",
       "      <td>4.263208</td>\n",
       "      <td>1.272757</td>\n",
       "      <td>45.386931</td>\n",
       "      <td>...</td>\n",
       "      <td>6.883163</td>\n",
       "      <td>84.803334</td>\n",
       "      <td>5.999149</td>\n",
       "      <td>89.381172</td>\n",
       "      <td>6.834703</td>\n",
       "      <td>76.983542</td>\n",
       "      <td>6.219989</td>\n",
       "      <td>82.146207</td>\n",
       "      <td>6.958294</td>\n",
       "      <td>77.170888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.920713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.021781</td>\n",
       "      <td>-0.423260</td>\n",
       "      <td>-799.741097</td>\n",
       "      <td>0.427084</td>\n",
       "      <td>-800.258209</td>\n",
       "      <td>0.024270</td>\n",
       "      <td>-810.473526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.971262</td>\n",
       "      <td>8.825748</td>\n",
       "      <td>6.939185</td>\n",
       "      <td>42.055722</td>\n",
       "      <td>62.625685</td>\n",
       "      <td>7.610544</td>\n",
       "      <td>1.641604</td>\n",
       "      <td>7.870275</td>\n",
       "      <td>2.172953</td>\n",
       "      <td>103.064021</td>\n",
       "      <td>...</td>\n",
       "      <td>14.508299</td>\n",
       "      <td>-500.837689</td>\n",
       "      <td>10.741388</td>\n",
       "      <td>-500.269182</td>\n",
       "      <td>10.977713</td>\n",
       "      <td>-500.530594</td>\n",
       "      <td>8.925586</td>\n",
       "      <td>-500.147603</td>\n",
       "      <td>13.977626</td>\n",
       "      <td>-501.080595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.869346</td>\n",
       "      <td>10.065316</td>\n",
       "      <td>8.557228</td>\n",
       "      <td>44.498874</td>\n",
       "      <td>67.644601</td>\n",
       "      <td>9.220393</td>\n",
       "      <td>2.453690</td>\n",
       "      <td>10.021968</td>\n",
       "      <td>2.781132</td>\n",
       "      <td>131.783108</td>\n",
       "      <td>...</td>\n",
       "      <td>19.986958</td>\n",
       "      <td>-499.778379</td>\n",
       "      <td>14.943933</td>\n",
       "      <td>-499.593286</td>\n",
       "      <td>15.998340</td>\n",
       "      <td>-499.784231</td>\n",
       "      <td>11.092839</td>\n",
       "      <td>-499.933330</td>\n",
       "      <td>18.034960</td>\n",
       "      <td>-500.109898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.821176</td>\n",
       "      <td>11.054809</td>\n",
       "      <td>10.289741</td>\n",
       "      <td>45.976222</td>\n",
       "      <td>72.824595</td>\n",
       "      <td>10.971110</td>\n",
       "      <td>3.192404</td>\n",
       "      <td>11.648573</td>\n",
       "      <td>3.416936</td>\n",
       "      <td>159.539839</td>\n",
       "      <td>...</td>\n",
       "      <td>24.983961</td>\n",
       "      <td>-494.648754</td>\n",
       "      <td>20.023751</td>\n",
       "      <td>-400.137948</td>\n",
       "      <td>20.000701</td>\n",
       "      <td>-496.531781</td>\n",
       "      <td>15.979467</td>\n",
       "      <td>-498.418000</td>\n",
       "      <td>24.984992</td>\n",
       "      <td>-499.565540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.001945</td>\n",
       "      <td>17.031899</td>\n",
       "      <td>18.124851</td>\n",
       "      <td>53.611374</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>19.552149</td>\n",
       "      <td>6.086532</td>\n",
       "      <td>22.317730</td>\n",
       "      <td>9.789625</td>\n",
       "      <td>251.999948</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.692333</td>\n",
       "      <td>28.003828</td>\n",
       "      <td>-71.472472</td>\n",
       "      <td>63.116298</td>\n",
       "      <td>-275.073125</td>\n",
       "      <td>39.846228</td>\n",
       "      <td>-120.190931</td>\n",
       "      <td>54.876806</td>\n",
       "      <td>-39.784927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "count                 16788.000000                 16788.000000   \n",
       "mean                      4.716907                     9.113559   \n",
       "std                       2.096718                     3.389495   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       3.971262                     8.825748   \n",
       "50%                       4.869346                    10.065316   \n",
       "75%                       5.821176                    11.054809   \n",
       "max                      16.001945                    17.031899   \n",
       "\n",
       "       final.output.concentrate_sol  final.output.concentrate_au  \\\n",
       "count                  16490.000000                 16789.000000   \n",
       "mean                       8.301123                    39.467217   \n",
       "std                        3.825760                    13.917227   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                        6.939185                    42.055722   \n",
       "50%                        8.557228                    44.498874   \n",
       "75%                       10.289741                    45.976222   \n",
       "max                       18.124851                    53.611374   \n",
       "\n",
       "       final.output.recovery  final.output.tail_ag  final.output.tail_pb  \\\n",
       "count           15339.000000          16794.000000          16677.000000   \n",
       "mean               67.213166              8.757048              2.360327   \n",
       "std                11.960446              3.634103              1.215576   \n",
       "min                 0.000000              0.000000              0.000000   \n",
       "25%                62.625685              7.610544              1.641604   \n",
       "50%                67.644601              9.220393              2.453690   \n",
       "75%                72.824595             10.971110              3.192404   \n",
       "max               100.000000             19.552149              6.086532   \n",
       "\n",
       "       final.output.tail_sol  final.output.tail_au  \\\n",
       "count           16715.000000          16794.000000   \n",
       "mean                9.303932              2.687512   \n",
       "std                 4.263208              1.272757   \n",
       "min                 0.000000              0.000000   \n",
       "25%                 7.870275              2.172953   \n",
       "50%                10.021968              2.781132   \n",
       "75%                11.648573              3.416936   \n",
       "max                22.317730              9.789625   \n",
       "\n",
       "       primary_cleaner.input.sulfate  ...  \\\n",
       "count                   15553.000000  ...   \n",
       "mean                      129.479789  ...   \n",
       "std                        45.386931  ...   \n",
       "min                         0.000003  ...   \n",
       "25%                       103.064021  ...   \n",
       "50%                       131.783108  ...   \n",
       "75%                       159.539839  ...   \n",
       "max                       251.999948  ...   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                              16731.000000   \n",
       "mean                                  19.101874   \n",
       "std                                    6.883163   \n",
       "min                                    0.000000   \n",
       "25%                                   14.508299   \n",
       "50%                                   19.986958   \n",
       "75%                                   24.983961   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                16747.000000   \n",
       "mean                                  -494.164481   \n",
       "std                                     84.803334   \n",
       "min                                   -799.920713   \n",
       "25%                                   -500.837689   \n",
       "50%                                   -499.778379   \n",
       "75%                                   -494.648754   \n",
       "max                                   -127.692333   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                              16768.000000   \n",
       "mean                                  14.778164   \n",
       "std                                    5.999149   \n",
       "min                                    0.000000   \n",
       "25%                                   10.741388   \n",
       "50%                                   14.943933   \n",
       "75%                                   20.023751   \n",
       "max                                   28.003828   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                16767.000000   \n",
       "mean                                  -476.600082   \n",
       "std                                     89.381172   \n",
       "min                                   -800.021781   \n",
       "25%                                   -500.269182   \n",
       "50%                                   -499.593286   \n",
       "75%                                   -400.137948   \n",
       "max                                    -71.472472   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                              16775.000000   \n",
       "mean                                  15.779488   \n",
       "std                                    6.834703   \n",
       "min                                   -0.423260   \n",
       "25%                                   10.977713   \n",
       "50%                                   15.998340   \n",
       "75%                                   20.000701   \n",
       "max                                   63.116298   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                16775.000000   \n",
       "mean                                  -500.230146   \n",
       "std                                     76.983542   \n",
       "min                                   -799.741097   \n",
       "25%                                   -500.530594   \n",
       "50%                                   -499.784231   \n",
       "75%                                   -496.531781   \n",
       "max                                   -275.073125   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                              16775.000000   \n",
       "mean                                  12.377241   \n",
       "std                                    6.219989   \n",
       "min                                    0.427084   \n",
       "25%                                    8.925586   \n",
       "50%                                   11.092839   \n",
       "75%                                   15.979467   \n",
       "max                                   39.846228   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                16776.000000   \n",
       "mean                                  -498.956257   \n",
       "std                                     82.146207   \n",
       "min                                   -800.258209   \n",
       "25%                                   -500.147603   \n",
       "50%                                   -499.933330   \n",
       "75%                                   -498.418000   \n",
       "max                                   -120.190931   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                              16757.000000   \n",
       "mean                                  18.429208   \n",
       "std                                    6.958294   \n",
       "min                                    0.024270   \n",
       "25%                                   13.977626   \n",
       "50%                                   18.034960   \n",
       "75%                                   24.984992   \n",
       "max                                   54.876806   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                16775.000000  \n",
       "mean                                  -521.801826  \n",
       "std                                     77.170888  \n",
       "min                                   -810.473526  \n",
       "25%                                   -501.080595  \n",
       "50%                                   -500.109898  \n",
       "75%                                   -499.565540  \n",
       "max                                    -39.784927  \n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>16860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-05-11 18:59:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
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       "                       date\n",
       "count                 16860\n",
       "unique                16860\n",
       "top     2017-05-11 18:59:59\n",
       "freq                      1"
      ]
     },
     "metadata": {},
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     "text": [
      "\n",
      "Columns with nulls:\n",
      "Column final.output.concentrate_ag has 0.4270% percent of Nulls, and 72 of nulls\n",
      "Column final.output.concentrate_pb has 0.4270% percent of Nulls, and 72 of nulls\n",
      "Column final.output.concentrate_sol has 2.1945% percent of Nulls, and 370 of nulls\n",
      "Column final.output.concentrate_au has 0.4211% percent of Nulls, and 71 of nulls\n",
      "Column final.output.recovery has 9.0214% percent of Nulls, and 1521 of nulls\n",
      "Column final.output.tail_ag has 0.3915% percent of Nulls, and 66 of nulls\n",
      "Column final.output.tail_pb has 1.0854% percent of Nulls, and 183 of nulls\n",
      "Column final.output.tail_sol has 0.8600% percent of Nulls, and 145 of nulls\n",
      "Column final.output.tail_au has 0.3915% percent of Nulls, and 66 of nulls\n",
      "Column primary_cleaner.input.sulfate has 7.7521% percent of Nulls, and 1307 of nulls\n",
      "Column primary_cleaner.input.depressant has 7.4852% percent of Nulls, and 1262 of nulls\n",
      "Column primary_cleaner.input.xanthate has 5.8422% percent of Nulls, and 985 of nulls\n",
      "Column primary_cleaner.output.concentrate_ag has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column primary_cleaner.output.concentrate_pb has 2.1234% percent of Nulls, and 358 of nulls\n",
      "Column primary_cleaner.output.concentrate_sol has 3.7722% percent of Nulls, and 636 of nulls\n",
      "Column primary_cleaner.output.concentrate_au has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column primary_cleaner.output.tail_ag has 0.4923% percent of Nulls, and 83 of nulls\n",
      "Column primary_cleaner.output.tail_pb has 0.5872% percent of Nulls, and 99 of nulls\n",
      "Column primary_cleaner.output.tail_sol has 1.6667% percent of Nulls, and 281 of nulls\n",
      "Column primary_cleaner.output.tail_au has 0.4923% percent of Nulls, and 83 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_air has 0.2372% percent of Nulls, and 40 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_level has 0.1957% percent of Nulls, and 33 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_air has 0.2372% percent of Nulls, and 40 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_level has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_air has 0.2254% percent of Nulls, and 38 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_level has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_air has 0.2313% percent of Nulls, and 39 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_level has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column rougher.calculation.sulfate_to_au_concentrate has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column rougher.calculation.floatbank10_sulfate_to_au_feed has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column rougher.calculation.floatbank11_sulfate_to_au_feed has 0.1601% percent of Nulls, and 27 of nulls\n",
      "Column rougher.calculation.au_pb_ratio has 7.3665% percent of Nulls, and 1242 of nulls\n",
      "Column rougher.input.feed_ag has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column rougher.input.feed_pb has 1.3523% percent of Nulls, and 228 of nulls\n",
      "Column rougher.input.feed_rate has 3.0427% percent of Nulls, and 513 of nulls\n",
      "Column rougher.input.feed_size has 2.4733% percent of Nulls, and 417 of nulls\n",
      "Column rougher.input.feed_sol has 1.7319% percent of Nulls, and 292 of nulls\n",
      "Column rougher.input.feed_au has 0.4923% percent of Nulls, and 83 of nulls\n",
      "Column rougher.input.floatbank10_sulfate has 6.1922% percent of Nulls, and 1044 of nulls\n",
      "Column rougher.input.floatbank10_xanthate has 2.0522% percent of Nulls, and 346 of nulls\n",
      "Column rougher.input.floatbank11_sulfate has 3.6951% percent of Nulls, and 623 of nulls\n",
      "Column rougher.input.floatbank11_xanthate has 11.2930% percent of Nulls, and 1904 of nulls\n",
      "Column rougher.output.concentrate_ag has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column rougher.output.concentrate_pb has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column rougher.output.concentrate_sol has 0.9609% percent of Nulls, and 162 of nulls\n",
      "Column rougher.output.concentrate_au has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column rougher.output.recovery has 15.2610% percent of Nulls, and 2573 of nulls\n",
      "Column rougher.output.tail_ag has 13.3452% percent of Nulls, and 2250 of nulls\n",
      "Column rougher.output.tail_pb has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column rougher.output.tail_sol has 13.3393% percent of Nulls, and 2249 of nulls\n",
      "Column rougher.output.tail_au has 13.3393% percent of Nulls, and 2249 of nulls\n",
      "Column rougher.state.floatbank10_a_air has 0.3144% percent of Nulls, and 53 of nulls\n",
      "Column rougher.state.floatbank10_a_level has 0.3144% percent of Nulls, and 53 of nulls\n",
      "Column rougher.state.floatbank10_b_air has 0.3144% percent of Nulls, and 53 of nulls\n",
      "Column rougher.state.floatbank10_b_level has 0.3144% percent of Nulls, and 53 of nulls\n",
      "Column rougher.state.floatbank10_c_air has 0.3144% percent of Nulls, and 53 of nulls\n",
      "Column rougher.state.floatbank10_c_level has 0.2728% percent of Nulls, and 46 of nulls\n",
      "Column rougher.state.floatbank10_d_air has 0.3440% percent of Nulls, and 58 of nulls\n",
      "Column rougher.state.floatbank10_d_level has 0.3025% percent of Nulls, and 51 of nulls\n",
      "Column rougher.state.floatbank10_e_air has 3.5765% percent of Nulls, and 603 of nulls\n",
      "Column rougher.state.floatbank10_e_level has 0.3025% percent of Nulls, and 51 of nulls\n",
      "Column rougher.state.floatbank10_f_air has 0.3440% percent of Nulls, and 58 of nulls\n",
      "Column rougher.state.floatbank10_f_level has 0.3440% percent of Nulls, and 58 of nulls\n",
      "Column secondary_cleaner.output.tail_ag has 0.4982% percent of Nulls, and 84 of nulls\n",
      "Column secondary_cleaner.output.tail_pb has 0.5694% percent of Nulls, and 96 of nulls\n",
      "Column secondary_cleaner.output.tail_sol has 11.7794% percent of Nulls, and 1986 of nulls\n",
      "Column secondary_cleaner.output.tail_au has 0.4864% percent of Nulls, and 82 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_air has 2.1530% percent of Nulls, and 363 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_level has 0.6465% percent of Nulls, and 109 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_air has 0.9193% percent of Nulls, and 155 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_level has 0.6643% percent of Nulls, and 112 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_air has 0.5753% percent of Nulls, and 97 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_level has 0.6702% percent of Nulls, and 113 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_air has 0.6406% percent of Nulls, and 108 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_level has 0.6524% percent of Nulls, and 110 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_air has 0.7651% percent of Nulls, and 129 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_level has 0.6702% percent of Nulls, and 113 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_air has 0.5457% percent of Nulls, and 92 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_level has 0.5516% percent of Nulls, and 93 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_air has 0.5042% percent of Nulls, and 85 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_level has 0.5042% percent of Nulls, and 85 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_air has 0.5042% percent of Nulls, and 85 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_level has 0.4982% percent of Nulls, and 84 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_air has 0.6109% percent of Nulls, and 103 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_level has 0.5042% percent of Nulls, and 85 of nulls\n",
      "\u001b[1mThere are 85 columns with NA.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Shape:\n",
      "(16860, 87)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Duplicated:\n",
      "\u001b[1mWe have 0 duplicated rows.\n",
      "\u001b[0m\n",
      "\n",
      "\u001b[1m----------------------------------------------------------------------------------------------------\u001b[0m\n",
      "Head:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-01 00:59:59</td>\n",
       "      <td>210.800909</td>\n",
       "      <td>14.993118</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>1.005021</td>\n",
       "      <td>1398.981301</td>\n",
       "      <td>-500.225577</td>\n",
       "      <td>1399.144926</td>\n",
       "      <td>-499.919735</td>\n",
       "      <td>1400.102998</td>\n",
       "      <td>...</td>\n",
       "      <td>12.023554</td>\n",
       "      <td>-497.795834</td>\n",
       "      <td>8.016656</td>\n",
       "      <td>-501.289139</td>\n",
       "      <td>7.946562</td>\n",
       "      <td>-432.317850</td>\n",
       "      <td>4.872511</td>\n",
       "      <td>-500.037437</td>\n",
       "      <td>26.705889</td>\n",
       "      <td>-499.709414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-01 01:59:59</td>\n",
       "      <td>215.392455</td>\n",
       "      <td>14.987471</td>\n",
       "      <td>8.080000</td>\n",
       "      <td>0.990469</td>\n",
       "      <td>1398.777912</td>\n",
       "      <td>-500.057435</td>\n",
       "      <td>1398.055362</td>\n",
       "      <td>-499.778182</td>\n",
       "      <td>1396.151033</td>\n",
       "      <td>...</td>\n",
       "      <td>12.058140</td>\n",
       "      <td>-498.695773</td>\n",
       "      <td>8.130979</td>\n",
       "      <td>-499.634209</td>\n",
       "      <td>7.958270</td>\n",
       "      <td>-525.839648</td>\n",
       "      <td>4.878850</td>\n",
       "      <td>-500.162375</td>\n",
       "      <td>25.019940</td>\n",
       "      <td>-499.819438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-01 02:59:59</td>\n",
       "      <td>215.259946</td>\n",
       "      <td>12.884934</td>\n",
       "      <td>7.786667</td>\n",
       "      <td>0.996043</td>\n",
       "      <td>1398.493666</td>\n",
       "      <td>-500.868360</td>\n",
       "      <td>1398.860436</td>\n",
       "      <td>-499.764529</td>\n",
       "      <td>1398.075709</td>\n",
       "      <td>...</td>\n",
       "      <td>11.962366</td>\n",
       "      <td>-498.767484</td>\n",
       "      <td>8.096893</td>\n",
       "      <td>-500.827423</td>\n",
       "      <td>8.071056</td>\n",
       "      <td>-500.801673</td>\n",
       "      <td>4.905125</td>\n",
       "      <td>-499.828510</td>\n",
       "      <td>24.994862</td>\n",
       "      <td>-500.622559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-01 03:59:59</td>\n",
       "      <td>215.336236</td>\n",
       "      <td>12.006805</td>\n",
       "      <td>7.640000</td>\n",
       "      <td>0.863514</td>\n",
       "      <td>1399.618111</td>\n",
       "      <td>-498.863574</td>\n",
       "      <td>1397.440120</td>\n",
       "      <td>-499.211024</td>\n",
       "      <td>1400.129303</td>\n",
       "      <td>...</td>\n",
       "      <td>12.033091</td>\n",
       "      <td>-498.350935</td>\n",
       "      <td>8.074946</td>\n",
       "      <td>-499.474407</td>\n",
       "      <td>7.897085</td>\n",
       "      <td>-500.868509</td>\n",
       "      <td>4.931400</td>\n",
       "      <td>-499.963623</td>\n",
       "      <td>24.948919</td>\n",
       "      <td>-498.709987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-01 04:59:59</td>\n",
       "      <td>199.099327</td>\n",
       "      <td>10.682530</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>0.805575</td>\n",
       "      <td>1401.268123</td>\n",
       "      <td>-500.808305</td>\n",
       "      <td>1398.128818</td>\n",
       "      <td>-499.504543</td>\n",
       "      <td>1402.172226</td>\n",
       "      <td>...</td>\n",
       "      <td>12.025367</td>\n",
       "      <td>-500.786497</td>\n",
       "      <td>8.054678</td>\n",
       "      <td>-500.397500</td>\n",
       "      <td>8.107890</td>\n",
       "      <td>-509.526725</td>\n",
       "      <td>4.957674</td>\n",
       "      <td>-500.360026</td>\n",
       "      <td>25.003331</td>\n",
       "      <td>-500.856333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  primary_cleaner.input.sulfate  \\\n",
       "0  2016-09-01 00:59:59                     210.800909   \n",
       "1  2016-09-01 01:59:59                     215.392455   \n",
       "2  2016-09-01 02:59:59                     215.259946   \n",
       "3  2016-09-01 03:59:59                     215.336236   \n",
       "4  2016-09-01 04:59:59                     199.099327   \n",
       "\n",
       "   primary_cleaner.input.depressant  primary_cleaner.input.feed_size  \\\n",
       "0                         14.993118                         8.080000   \n",
       "1                         14.987471                         8.080000   \n",
       "2                         12.884934                         7.786667   \n",
       "3                         12.006805                         7.640000   \n",
       "4                         10.682530                         7.530000   \n",
       "\n",
       "   primary_cleaner.input.xanthate  primary_cleaner.state.floatbank8_a_air  \\\n",
       "0                        1.005021                             1398.981301   \n",
       "1                        0.990469                             1398.777912   \n",
       "2                        0.996043                             1398.493666   \n",
       "3                        0.863514                             1399.618111   \n",
       "4                        0.805575                             1401.268123   \n",
       "\n",
       "   primary_cleaner.state.floatbank8_a_level  \\\n",
       "0                               -500.225577   \n",
       "1                               -500.057435   \n",
       "2                               -500.868360   \n",
       "3                               -498.863574   \n",
       "4                               -500.808305   \n",
       "\n",
       "   primary_cleaner.state.floatbank8_b_air  \\\n",
       "0                             1399.144926   \n",
       "1                             1398.055362   \n",
       "2                             1398.860436   \n",
       "3                             1397.440120   \n",
       "4                             1398.128818   \n",
       "\n",
       "   primary_cleaner.state.floatbank8_b_level  \\\n",
       "0                               -499.919735   \n",
       "1                               -499.778182   \n",
       "2                               -499.764529   \n",
       "3                               -499.211024   \n",
       "4                               -499.504543   \n",
       "\n",
       "   primary_cleaner.state.floatbank8_c_air  ...  \\\n",
       "0                             1400.102998  ...   \n",
       "1                             1396.151033  ...   \n",
       "2                             1398.075709  ...   \n",
       "3                             1400.129303  ...   \n",
       "4                             1402.172226  ...   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_air  \\\n",
       "0                                 12.023554   \n",
       "1                                 12.058140   \n",
       "2                                 11.962366   \n",
       "3                                 12.033091   \n",
       "4                                 12.025367   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_level  \\\n",
       "0                                 -497.795834   \n",
       "1                                 -498.695773   \n",
       "2                                 -498.767484   \n",
       "3                                 -498.350935   \n",
       "4                                 -500.786497   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_air  \\\n",
       "0                                  8.016656   \n",
       "1                                  8.130979   \n",
       "2                                  8.096893   \n",
       "3                                  8.074946   \n",
       "4                                  8.054678   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_level  \\\n",
       "0                                 -501.289139   \n",
       "1                                 -499.634209   \n",
       "2                                 -500.827423   \n",
       "3                                 -499.474407   \n",
       "4                                 -500.397500   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_air  \\\n",
       "0                                  7.946562   \n",
       "1                                  7.958270   \n",
       "2                                  8.071056   \n",
       "3                                  7.897085   \n",
       "4                                  8.107890   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_level  \\\n",
       "0                                 -432.317850   \n",
       "1                                 -525.839648   \n",
       "2                                 -500.801673   \n",
       "3                                 -500.868509   \n",
       "4                                 -509.526725   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_air  \\\n",
       "0                                  4.872511   \n",
       "1                                  4.878850   \n",
       "2                                  4.905125   \n",
       "3                                  4.931400   \n",
       "4                                  4.957674   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_level  \\\n",
       "0                                 -500.037437   \n",
       "1                                 -500.162375   \n",
       "2                                 -499.828510   \n",
       "3                                 -499.963623   \n",
       "4                                 -500.360026   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_air  \\\n",
       "0                                 26.705889   \n",
       "1                                 25.019940   \n",
       "2                                 24.994862   \n",
       "3                                 24.948919   \n",
       "4                                 25.003331   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_level  \n",
       "0                                 -499.709414  \n",
       "1                                 -499.819438  \n",
       "2                                 -500.622559  \n",
       "3                                 -498.709987  \n",
       "4                                 -500.856333  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Info:\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5856 entries, 0 to 5855\n",
      "Data columns (total 53 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   date                                        5856 non-null   object \n",
      " 1   primary_cleaner.input.sulfate               5554 non-null   float64\n",
      " 2   primary_cleaner.input.depressant            5572 non-null   float64\n",
      " 3   primary_cleaner.input.feed_size             5856 non-null   float64\n",
      " 4   primary_cleaner.input.xanthate              5690 non-null   float64\n",
      " 5   primary_cleaner.state.floatbank8_a_air      5840 non-null   float64\n",
      " 6   primary_cleaner.state.floatbank8_a_level    5840 non-null   float64\n",
      " 7   primary_cleaner.state.floatbank8_b_air      5840 non-null   float64\n",
      " 8   primary_cleaner.state.floatbank8_b_level    5840 non-null   float64\n",
      " 9   primary_cleaner.state.floatbank8_c_air      5840 non-null   float64\n",
      " 10  primary_cleaner.state.floatbank8_c_level    5840 non-null   float64\n",
      " 11  primary_cleaner.state.floatbank8_d_air      5840 non-null   float64\n",
      " 12  primary_cleaner.state.floatbank8_d_level    5840 non-null   float64\n",
      " 13  rougher.input.feed_ag                       5840 non-null   float64\n",
      " 14  rougher.input.feed_pb                       5840 non-null   float64\n",
      " 15  rougher.input.feed_rate                     5816 non-null   float64\n",
      " 16  rougher.input.feed_size                     5834 non-null   float64\n",
      " 17  rougher.input.feed_sol                      5789 non-null   float64\n",
      " 18  rougher.input.feed_au                       5840 non-null   float64\n",
      " 19  rougher.input.floatbank10_sulfate           5599 non-null   float64\n",
      " 20  rougher.input.floatbank10_xanthate          5733 non-null   float64\n",
      " 21  rougher.input.floatbank11_sulfate           5801 non-null   float64\n",
      " 22  rougher.input.floatbank11_xanthate          5503 non-null   float64\n",
      " 23  rougher.state.floatbank10_a_air             5839 non-null   float64\n",
      " 24  rougher.state.floatbank10_a_level           5840 non-null   float64\n",
      " 25  rougher.state.floatbank10_b_air             5839 non-null   float64\n",
      " 26  rougher.state.floatbank10_b_level           5840 non-null   float64\n",
      " 27  rougher.state.floatbank10_c_air             5839 non-null   float64\n",
      " 28  rougher.state.floatbank10_c_level           5840 non-null   float64\n",
      " 29  rougher.state.floatbank10_d_air             5839 non-null   float64\n",
      " 30  rougher.state.floatbank10_d_level           5840 non-null   float64\n",
      " 31  rougher.state.floatbank10_e_air             5839 non-null   float64\n",
      " 32  rougher.state.floatbank10_e_level           5840 non-null   float64\n",
      " 33  rougher.state.floatbank10_f_air             5839 non-null   float64\n",
      " 34  rougher.state.floatbank10_f_level           5840 non-null   float64\n",
      " 35  secondary_cleaner.state.floatbank2_a_air    5836 non-null   float64\n",
      " 36  secondary_cleaner.state.floatbank2_a_level  5840 non-null   float64\n",
      " 37  secondary_cleaner.state.floatbank2_b_air    5833 non-null   float64\n",
      " 38  secondary_cleaner.state.floatbank2_b_level  5840 non-null   float64\n",
      " 39  secondary_cleaner.state.floatbank3_a_air    5822 non-null   float64\n",
      " 40  secondary_cleaner.state.floatbank3_a_level  5840 non-null   float64\n",
      " 41  secondary_cleaner.state.floatbank3_b_air    5840 non-null   float64\n",
      " 42  secondary_cleaner.state.floatbank3_b_level  5840 non-null   float64\n",
      " 43  secondary_cleaner.state.floatbank4_a_air    5840 non-null   float64\n",
      " 44  secondary_cleaner.state.floatbank4_a_level  5840 non-null   float64\n",
      " 45  secondary_cleaner.state.floatbank4_b_air    5840 non-null   float64\n",
      " 46  secondary_cleaner.state.floatbank4_b_level  5840 non-null   float64\n",
      " 47  secondary_cleaner.state.floatbank5_a_air    5840 non-null   float64\n",
      " 48  secondary_cleaner.state.floatbank5_a_level  5840 non-null   float64\n",
      " 49  secondary_cleaner.state.floatbank5_b_air    5840 non-null   float64\n",
      " 50  secondary_cleaner.state.floatbank5_b_level  5840 non-null   float64\n",
      " 51  secondary_cleaner.state.floatbank6_a_air    5840 non-null   float64\n",
      " 52  secondary_cleaner.state.floatbank6_a_level  5840 non-null   float64\n",
      "dtypes: float64(52), object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Describe:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <th>primary_cleaner.input.feed_size</th>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5554.000000</td>\n",
       "      <td>5572.000000</td>\n",
       "      <td>5856.000000</td>\n",
       "      <td>5690.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "      <td>5840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>170.515243</td>\n",
       "      <td>8.482873</td>\n",
       "      <td>7.264651</td>\n",
       "      <td>1.321420</td>\n",
       "      <td>1481.990241</td>\n",
       "      <td>-509.057796</td>\n",
       "      <td>1486.908670</td>\n",
       "      <td>-511.743956</td>\n",
       "      <td>1468.495216</td>\n",
       "      <td>-509.741212</td>\n",
       "      <td>...</td>\n",
       "      <td>15.636031</td>\n",
       "      <td>-516.266074</td>\n",
       "      <td>13.145702</td>\n",
       "      <td>-476.338907</td>\n",
       "      <td>12.308967</td>\n",
       "      <td>-512.208126</td>\n",
       "      <td>9.470986</td>\n",
       "      <td>-505.017827</td>\n",
       "      <td>16.678722</td>\n",
       "      <td>-512.351694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.608602</td>\n",
       "      <td>3.353105</td>\n",
       "      <td>0.611526</td>\n",
       "      <td>0.693246</td>\n",
       "      <td>310.453166</td>\n",
       "      <td>61.339256</td>\n",
       "      <td>313.224286</td>\n",
       "      <td>67.139074</td>\n",
       "      <td>309.980748</td>\n",
       "      <td>62.671873</td>\n",
       "      <td>...</td>\n",
       "      <td>4.660835</td>\n",
       "      <td>62.756748</td>\n",
       "      <td>4.304086</td>\n",
       "      <td>105.549424</td>\n",
       "      <td>3.762827</td>\n",
       "      <td>58.864651</td>\n",
       "      <td>3.312471</td>\n",
       "      <td>68.785898</td>\n",
       "      <td>5.404514</td>\n",
       "      <td>69.919839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.773788</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.029078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.995127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.798523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.836914</td>\n",
       "      <td>-0.223393</td>\n",
       "      <td>-799.661076</td>\n",
       "      <td>0.528083</td>\n",
       "      <td>-800.220337</td>\n",
       "      <td>-0.079426</td>\n",
       "      <td>-809.859706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>143.340022</td>\n",
       "      <td>6.411500</td>\n",
       "      <td>6.885625</td>\n",
       "      <td>0.888769</td>\n",
       "      <td>1497.190681</td>\n",
       "      <td>-500.455211</td>\n",
       "      <td>1497.150234</td>\n",
       "      <td>-500.936639</td>\n",
       "      <td>1437.050321</td>\n",
       "      <td>-501.300441</td>\n",
       "      <td>...</td>\n",
       "      <td>12.057838</td>\n",
       "      <td>-501.054741</td>\n",
       "      <td>11.880119</td>\n",
       "      <td>-500.419113</td>\n",
       "      <td>10.123459</td>\n",
       "      <td>-500.879383</td>\n",
       "      <td>7.991208</td>\n",
       "      <td>-500.223089</td>\n",
       "      <td>13.012422</td>\n",
       "      <td>-500.833821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>176.103893</td>\n",
       "      <td>8.023252</td>\n",
       "      <td>7.259333</td>\n",
       "      <td>1.183362</td>\n",
       "      <td>1554.659783</td>\n",
       "      <td>-499.997402</td>\n",
       "      <td>1553.268084</td>\n",
       "      <td>-500.066588</td>\n",
       "      <td>1546.160672</td>\n",
       "      <td>-500.079537</td>\n",
       "      <td>...</td>\n",
       "      <td>17.001867</td>\n",
       "      <td>-500.160145</td>\n",
       "      <td>14.952102</td>\n",
       "      <td>-499.644328</td>\n",
       "      <td>12.062877</td>\n",
       "      <td>-500.047621</td>\n",
       "      <td>9.980774</td>\n",
       "      <td>-500.001338</td>\n",
       "      <td>16.007242</td>\n",
       "      <td>-500.041085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>207.240761</td>\n",
       "      <td>10.017725</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>1.763797</td>\n",
       "      <td>1601.681656</td>\n",
       "      <td>-499.575313</td>\n",
       "      <td>1601.784707</td>\n",
       "      <td>-499.323361</td>\n",
       "      <td>1600.785573</td>\n",
       "      <td>-499.009545</td>\n",
       "      <td>...</td>\n",
       "      <td>18.030985</td>\n",
       "      <td>-499.441529</td>\n",
       "      <td>15.940011</td>\n",
       "      <td>-401.523664</td>\n",
       "      <td>15.017881</td>\n",
       "      <td>-499.297033</td>\n",
       "      <td>11.992176</td>\n",
       "      <td>-499.722835</td>\n",
       "      <td>21.009076</td>\n",
       "      <td>-499.395621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>274.409626</td>\n",
       "      <td>40.024582</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>5.433169</td>\n",
       "      <td>2212.432090</td>\n",
       "      <td>-57.195404</td>\n",
       "      <td>1975.147923</td>\n",
       "      <td>-142.527229</td>\n",
       "      <td>1715.053773</td>\n",
       "      <td>-150.937035</td>\n",
       "      <td>...</td>\n",
       "      <td>30.051797</td>\n",
       "      <td>-401.565212</td>\n",
       "      <td>31.269706</td>\n",
       "      <td>-6.506986</td>\n",
       "      <td>25.258848</td>\n",
       "      <td>-244.483566</td>\n",
       "      <td>14.090194</td>\n",
       "      <td>-126.463446</td>\n",
       "      <td>26.705889</td>\n",
       "      <td>-29.093593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       primary_cleaner.input.sulfate  primary_cleaner.input.depressant  \\\n",
       "count                    5554.000000                       5572.000000   \n",
       "mean                      170.515243                          8.482873   \n",
       "std                        49.608602                          3.353105   \n",
       "min                         0.000103                          0.000031   \n",
       "25%                       143.340022                          6.411500   \n",
       "50%                       176.103893                          8.023252   \n",
       "75%                       207.240761                         10.017725   \n",
       "max                       274.409626                         40.024582   \n",
       "\n",
       "       primary_cleaner.input.feed_size  primary_cleaner.input.xanthate  \\\n",
       "count                      5856.000000                     5690.000000   \n",
       "mean                          7.264651                        1.321420   \n",
       "std                           0.611526                        0.693246   \n",
       "min                           5.650000                        0.000003   \n",
       "25%                           6.885625                        0.888769   \n",
       "50%                           7.259333                        1.183362   \n",
       "75%                           7.650000                        1.763797   \n",
       "max                          15.500000                        5.433169   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1481.990241   \n",
       "std                                310.453166   \n",
       "min                                  0.000000   \n",
       "25%                               1497.190681   \n",
       "50%                               1554.659783   \n",
       "75%                               1601.681656   \n",
       "max                               2212.432090   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_a_level  \\\n",
       "count                               5840.000000   \n",
       "mean                                -509.057796   \n",
       "std                                   61.339256   \n",
       "min                                 -799.773788   \n",
       "25%                                 -500.455211   \n",
       "50%                                 -499.997402   \n",
       "75%                                 -499.575313   \n",
       "max                                  -57.195404   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1486.908670   \n",
       "std                                313.224286   \n",
       "min                                  0.000000   \n",
       "25%                               1497.150234   \n",
       "50%                               1553.268084   \n",
       "75%                               1601.784707   \n",
       "max                               1975.147923   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_b_level  \\\n",
       "count                               5840.000000   \n",
       "mean                                -511.743956   \n",
       "std                                   67.139074   \n",
       "min                                 -800.029078   \n",
       "25%                                 -500.936639   \n",
       "50%                                 -500.066588   \n",
       "75%                                 -499.323361   \n",
       "max                                 -142.527229   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_air  \\\n",
       "count                             5840.000000   \n",
       "mean                              1468.495216   \n",
       "std                                309.980748   \n",
       "min                                  0.000000   \n",
       "25%                               1437.050321   \n",
       "50%                               1546.160672   \n",
       "75%                               1600.785573   \n",
       "max                               1715.053773   \n",
       "\n",
       "       primary_cleaner.state.floatbank8_c_level  ...  \\\n",
       "count                               5840.000000  ...   \n",
       "mean                                -509.741212  ...   \n",
       "std                                   62.671873  ...   \n",
       "min                                 -799.995127  ...   \n",
       "25%                                 -501.300441  ...   \n",
       "50%                                 -500.079537  ...   \n",
       "75%                                 -499.009545  ...   \n",
       "max                                 -150.937035  ...   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  15.636031   \n",
       "std                                    4.660835   \n",
       "min                                    0.000000   \n",
       "25%                                   12.057838   \n",
       "50%                                   17.001867   \n",
       "75%                                   18.030985   \n",
       "max                                   30.051797   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -516.266074   \n",
       "std                                     62.756748   \n",
       "min                                   -799.798523   \n",
       "25%                                   -501.054741   \n",
       "50%                                   -500.160145   \n",
       "75%                                   -499.441529   \n",
       "max                                   -401.565212   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  13.145702   \n",
       "std                                    4.304086   \n",
       "min                                    0.000000   \n",
       "25%                                   11.880119   \n",
       "50%                                   14.952102   \n",
       "75%                                   15.940011   \n",
       "max                                   31.269706   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -476.338907   \n",
       "std                                    105.549424   \n",
       "min                                   -800.836914   \n",
       "25%                                   -500.419113   \n",
       "50%                                   -499.644328   \n",
       "75%                                   -401.523664   \n",
       "max                                     -6.506986   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  12.308967   \n",
       "std                                    3.762827   \n",
       "min                                   -0.223393   \n",
       "25%                                   10.123459   \n",
       "50%                                   12.062877   \n",
       "75%                                   15.017881   \n",
       "max                                   25.258848   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -512.208126   \n",
       "std                                     58.864651   \n",
       "min                                   -799.661076   \n",
       "25%                                   -500.879383   \n",
       "50%                                   -500.047621   \n",
       "75%                                   -499.297033   \n",
       "max                                   -244.483566   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                   9.470986   \n",
       "std                                    3.312471   \n",
       "min                                    0.528083   \n",
       "25%                                    7.991208   \n",
       "50%                                    9.980774   \n",
       "75%                                   11.992176   \n",
       "max                                   14.090194   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                 5840.000000   \n",
       "mean                                  -505.017827   \n",
       "std                                     68.785898   \n",
       "min                                   -800.220337   \n",
       "25%                                   -500.223089   \n",
       "50%                                   -500.001338   \n",
       "75%                                   -499.722835   \n",
       "max                                   -126.463446   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                               5840.000000   \n",
       "mean                                  16.678722   \n",
       "std                                    5.404514   \n",
       "min                                   -0.079426   \n",
       "25%                                   13.012422   \n",
       "50%                                   16.007242   \n",
       "75%                                   21.009076   \n",
       "max                                   26.705889   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                 5840.000000  \n",
       "mean                                  -512.351694  \n",
       "std                                     69.919839  \n",
       "min                                   -809.859706  \n",
       "25%                                   -500.833821  \n",
       "50%                                   -500.041085  \n",
       "75%                                   -499.395621  \n",
       "max                                    -29.093593  \n",
       "\n",
       "[8 rows x 52 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2016-12-11 04:59:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
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       "                       date\n",
       "count                  5856\n",
       "unique                 5856\n",
       "top     2016-12-11 04:59:59\n",
       "freq                      1"
      ]
     },
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     "text": [
      "\n",
      "Columns with nulls:\n",
      "Column primary_cleaner.input.sulfate has 5.1571% percent of Nulls, and 302 of nulls\n",
      "Column primary_cleaner.input.depressant has 4.8497% percent of Nulls, and 284 of nulls\n",
      "Column primary_cleaner.input.xanthate has 2.8347% percent of Nulls, and 166 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.input.feed_ag has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.input.feed_pb has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.input.feed_rate has 0.6831% percent of Nulls, and 40 of nulls\n",
      "Column rougher.input.feed_size has 0.3757% percent of Nulls, and 22 of nulls\n",
      "Column rougher.input.feed_sol has 1.1441% percent of Nulls, and 67 of nulls\n",
      "Column rougher.input.feed_au has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.input.floatbank10_sulfate has 4.3887% percent of Nulls, and 257 of nulls\n",
      "Column rougher.input.floatbank10_xanthate has 2.1004% percent of Nulls, and 123 of nulls\n",
      "Column rougher.input.floatbank11_sulfate has 0.9392% percent of Nulls, and 55 of nulls\n",
      "Column rougher.input.floatbank11_xanthate has 6.0280% percent of Nulls, and 353 of nulls\n",
      "Column rougher.state.floatbank10_a_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.state.floatbank10_b_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.state.floatbank10_c_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_c_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.state.floatbank10_d_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_d_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.state.floatbank10_e_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_e_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column rougher.state.floatbank10_f_air has 0.2903% percent of Nulls, and 17 of nulls\n",
      "Column rougher.state.floatbank10_f_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_air has 0.3415% percent of Nulls, and 20 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_air has 0.3928% percent of Nulls, and 23 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_air has 0.5806% percent of Nulls, and 34 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_air has 0.2732% percent of Nulls, and 16 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_level has 0.2732% percent of Nulls, and 16 of nulls\n",
      "\u001b[1mThere are 51 columns with NA.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Shape:\n",
      "(5856, 53)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Duplicated:\n",
      "\u001b[1mWe have 0 duplicated rows.\n",
      "\u001b[0m\n",
      "\n",
      "\u001b[1m----------------------------------------------------------------------------------------------------\u001b[0m\n",
      "Head:\n",
      "\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "      <td>6.055403</td>\n",
       "      <td>9.889648</td>\n",
       "      <td>5.507324</td>\n",
       "      <td>42.192020</td>\n",
       "      <td>70.541216</td>\n",
       "      <td>10.411962</td>\n",
       "      <td>0.895447</td>\n",
       "      <td>16.904297</td>\n",
       "      <td>2.143149</td>\n",
       "      <td>...</td>\n",
       "      <td>14.016835</td>\n",
       "      <td>-502.488007</td>\n",
       "      <td>12.099931</td>\n",
       "      <td>-504.715942</td>\n",
       "      <td>9.925633</td>\n",
       "      <td>-498.310211</td>\n",
       "      <td>8.079666</td>\n",
       "      <td>-500.470978</td>\n",
       "      <td>14.151341</td>\n",
       "      <td>-605.841980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>6.029369</td>\n",
       "      <td>9.968944</td>\n",
       "      <td>5.257781</td>\n",
       "      <td>42.701629</td>\n",
       "      <td>69.266198</td>\n",
       "      <td>10.462676</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>16.634514</td>\n",
       "      <td>2.224930</td>\n",
       "      <td>...</td>\n",
       "      <td>13.992281</td>\n",
       "      <td>-505.503262</td>\n",
       "      <td>11.950531</td>\n",
       "      <td>-501.331529</td>\n",
       "      <td>10.039245</td>\n",
       "      <td>-500.169983</td>\n",
       "      <td>7.984757</td>\n",
       "      <td>-500.582168</td>\n",
       "      <td>13.998353</td>\n",
       "      <td>-599.787184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>6.055926</td>\n",
       "      <td>10.213995</td>\n",
       "      <td>5.383759</td>\n",
       "      <td>42.657501</td>\n",
       "      <td>68.116445</td>\n",
       "      <td>10.507046</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>16.208849</td>\n",
       "      <td>2.257889</td>\n",
       "      <td>...</td>\n",
       "      <td>14.015015</td>\n",
       "      <td>-502.520901</td>\n",
       "      <td>11.912783</td>\n",
       "      <td>-501.133383</td>\n",
       "      <td>10.070913</td>\n",
       "      <td>-500.129135</td>\n",
       "      <td>8.013877</td>\n",
       "      <td>-500.517572</td>\n",
       "      <td>14.028663</td>\n",
       "      <td>-601.427363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>6.047977</td>\n",
       "      <td>9.977019</td>\n",
       "      <td>4.858634</td>\n",
       "      <td>42.689819</td>\n",
       "      <td>68.347543</td>\n",
       "      <td>10.422762</td>\n",
       "      <td>0.883763</td>\n",
       "      <td>16.532835</td>\n",
       "      <td>2.146849</td>\n",
       "      <td>...</td>\n",
       "      <td>14.036510</td>\n",
       "      <td>-500.857308</td>\n",
       "      <td>11.999550</td>\n",
       "      <td>-501.193686</td>\n",
       "      <td>9.970366</td>\n",
       "      <td>-499.201640</td>\n",
       "      <td>7.977324</td>\n",
       "      <td>-500.255908</td>\n",
       "      <td>14.005551</td>\n",
       "      <td>-599.996129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>6.148599</td>\n",
       "      <td>10.142511</td>\n",
       "      <td>4.939416</td>\n",
       "      <td>42.774141</td>\n",
       "      <td>66.927016</td>\n",
       "      <td>10.360302</td>\n",
       "      <td>0.792826</td>\n",
       "      <td>16.525686</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>...</td>\n",
       "      <td>14.027298</td>\n",
       "      <td>-499.838632</td>\n",
       "      <td>11.953070</td>\n",
       "      <td>-501.053894</td>\n",
       "      <td>9.925709</td>\n",
       "      <td>-501.686727</td>\n",
       "      <td>7.894242</td>\n",
       "      <td>-500.356035</td>\n",
       "      <td>13.996647</td>\n",
       "      <td>-601.496691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  final.output.concentrate_ag  \\\n",
       "0  2016-01-15 00:00:00                     6.055403   \n",
       "1  2016-01-15 01:00:00                     6.029369   \n",
       "2  2016-01-15 02:00:00                     6.055926   \n",
       "3  2016-01-15 03:00:00                     6.047977   \n",
       "4  2016-01-15 04:00:00                     6.148599   \n",
       "\n",
       "   final.output.concentrate_pb  final.output.concentrate_sol  \\\n",
       "0                     9.889648                      5.507324   \n",
       "1                     9.968944                      5.257781   \n",
       "2                    10.213995                      5.383759   \n",
       "3                     9.977019                      4.858634   \n",
       "4                    10.142511                      4.939416   \n",
       "\n",
       "   final.output.concentrate_au  final.output.recovery  final.output.tail_ag  \\\n",
       "0                    42.192020              70.541216             10.411962   \n",
       "1                    42.701629              69.266198             10.462676   \n",
       "2                    42.657501              68.116445             10.507046   \n",
       "3                    42.689819              68.347543             10.422762   \n",
       "4                    42.774141              66.927016             10.360302   \n",
       "\n",
       "   final.output.tail_pb  final.output.tail_sol  final.output.tail_au  ...  \\\n",
       "0              0.895447              16.904297              2.143149  ...   \n",
       "1              0.927452              16.634514              2.224930  ...   \n",
       "2              0.953716              16.208849              2.257889  ...   \n",
       "3              0.883763              16.532835              2.146849  ...   \n",
       "4              0.792826              16.525686              2.055292  ...   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_air  \\\n",
       "0                                 14.016835   \n",
       "1                                 13.992281   \n",
       "2                                 14.015015   \n",
       "3                                 14.036510   \n",
       "4                                 14.027298   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_a_level  \\\n",
       "0                                 -502.488007   \n",
       "1                                 -505.503262   \n",
       "2                                 -502.520901   \n",
       "3                                 -500.857308   \n",
       "4                                 -499.838632   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_air  \\\n",
       "0                                 12.099931   \n",
       "1                                 11.950531   \n",
       "2                                 11.912783   \n",
       "3                                 11.999550   \n",
       "4                                 11.953070   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_level  \\\n",
       "0                                 -504.715942   \n",
       "1                                 -501.331529   \n",
       "2                                 -501.133383   \n",
       "3                                 -501.193686   \n",
       "4                                 -501.053894   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_air  \\\n",
       "0                                  9.925633   \n",
       "1                                 10.039245   \n",
       "2                                 10.070913   \n",
       "3                                  9.970366   \n",
       "4                                  9.925709   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_level  \\\n",
       "0                                 -498.310211   \n",
       "1                                 -500.169983   \n",
       "2                                 -500.129135   \n",
       "3                                 -499.201640   \n",
       "4                                 -501.686727   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_air  \\\n",
       "0                                  8.079666   \n",
       "1                                  7.984757   \n",
       "2                                  8.013877   \n",
       "3                                  7.977324   \n",
       "4                                  7.894242   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_level  \\\n",
       "0                                 -500.470978   \n",
       "1                                 -500.582168   \n",
       "2                                 -500.517572   \n",
       "3                                 -500.255908   \n",
       "4                                 -500.356035   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_air  \\\n",
       "0                                 14.151341   \n",
       "1                                 13.998353   \n",
       "2                                 14.028663   \n",
       "3                                 14.005551   \n",
       "4                                 13.996647   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_level  \n",
       "0                                 -605.841980  \n",
       "1                                 -599.787184  \n",
       "2                                 -601.427363  \n",
       "3                                 -599.996129  \n",
       "4                                 -601.496691  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Info:\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22716 entries, 0 to 22715\n",
      "Data columns (total 87 columns):\n",
      " #   Column                                              Non-Null Count  Dtype  \n",
      "---  ------                                              --------------  -----  \n",
      " 0   date                                                22716 non-null  object \n",
      " 1   final.output.concentrate_ag                         22627 non-null  float64\n",
      " 2   final.output.concentrate_pb                         22629 non-null  float64\n",
      " 3   final.output.concentrate_sol                        22331 non-null  float64\n",
      " 4   final.output.concentrate_au                         22630 non-null  float64\n",
      " 5   final.output.recovery                               20753 non-null  float64\n",
      " 6   final.output.tail_ag                                22633 non-null  float64\n",
      " 7   final.output.tail_pb                                22516 non-null  float64\n",
      " 8   final.output.tail_sol                               22445 non-null  float64\n",
      " 9   final.output.tail_au                                22635 non-null  float64\n",
      " 10  primary_cleaner.input.sulfate                       21107 non-null  float64\n",
      " 11  primary_cleaner.input.depressant                    21170 non-null  float64\n",
      " 12  primary_cleaner.input.feed_size                     22716 non-null  float64\n",
      " 13  primary_cleaner.input.xanthate                      21565 non-null  float64\n",
      " 14  primary_cleaner.output.concentrate_ag               22618 non-null  float64\n",
      " 15  primary_cleaner.output.concentrate_pb               22268 non-null  float64\n",
      " 16  primary_cleaner.output.concentrate_sol              21918 non-null  float64\n",
      " 17  primary_cleaner.output.concentrate_au               22618 non-null  float64\n",
      " 18  primary_cleaner.output.tail_ag                      22614 non-null  float64\n",
      " 19  primary_cleaner.output.tail_pb                      22594 non-null  float64\n",
      " 20  primary_cleaner.output.tail_sol                     22365 non-null  float64\n",
      " 21  primary_cleaner.output.tail_au                      22617 non-null  float64\n",
      " 22  primary_cleaner.state.floatbank8_a_air              22660 non-null  float64\n",
      " 23  primary_cleaner.state.floatbank8_a_level            22667 non-null  float64\n",
      " 24  primary_cleaner.state.floatbank8_b_air              22660 non-null  float64\n",
      " 25  primary_cleaner.state.floatbank8_b_level            22673 non-null  float64\n",
      " 26  primary_cleaner.state.floatbank8_c_air              22662 non-null  float64\n",
      " 27  primary_cleaner.state.floatbank8_c_level            22673 non-null  float64\n",
      " 28  primary_cleaner.state.floatbank8_d_air              22661 non-null  float64\n",
      " 29  primary_cleaner.state.floatbank8_d_level            22673 non-null  float64\n",
      " 30  rougher.calculation.sulfate_to_au_concentrate       22672 non-null  float64\n",
      " 31  rougher.calculation.floatbank10_sulfate_to_au_feed  22672 non-null  float64\n",
      " 32  rougher.calculation.floatbank11_sulfate_to_au_feed  22672 non-null  float64\n",
      " 33  rougher.calculation.au_pb_ratio                     21089 non-null  float64\n",
      " 34  rougher.input.feed_ag                               22618 non-null  float64\n",
      " 35  rougher.input.feed_pb                               22472 non-null  float64\n",
      " 36  rougher.input.feed_rate                             22163 non-null  float64\n",
      " 37  rougher.input.feed_size                             22277 non-null  float64\n",
      " 38  rougher.input.feed_sol                              22357 non-null  float64\n",
      " 39  rougher.input.feed_au                               22617 non-null  float64\n",
      " 40  rougher.input.floatbank10_sulfate                   21415 non-null  float64\n",
      " 41  rougher.input.floatbank10_xanthate                  22247 non-null  float64\n",
      " 42  rougher.input.floatbank11_sulfate                   22038 non-null  float64\n",
      " 43  rougher.input.floatbank11_xanthate                  20459 non-null  float64\n",
      " 44  rougher.output.concentrate_ag                       22618 non-null  float64\n",
      " 45  rougher.output.concentrate_pb                       22618 non-null  float64\n",
      " 46  rougher.output.concentrate_sol                      22526 non-null  float64\n",
      " 47  rougher.output.concentrate_au                       22618 non-null  float64\n",
      " 48  rougher.output.recovery                             19597 non-null  float64\n",
      " 49  rougher.output.tail_ag                              19979 non-null  float64\n",
      " 50  rougher.output.tail_pb                              22618 non-null  float64\n",
      " 51  rougher.output.tail_sol                             19980 non-null  float64\n",
      " 52  rougher.output.tail_au                              19980 non-null  float64\n",
      " 53  rougher.state.floatbank10_a_air                     22646 non-null  float64\n",
      " 54  rougher.state.floatbank10_a_level                   22647 non-null  float64\n",
      " 55  rougher.state.floatbank10_b_air                     22646 non-null  float64\n",
      " 56  rougher.state.floatbank10_b_level                   22647 non-null  float64\n",
      " 57  rougher.state.floatbank10_c_air                     22646 non-null  float64\n",
      " 58  rougher.state.floatbank10_c_level                   22654 non-null  float64\n",
      " 59  rougher.state.floatbank10_d_air                     22641 non-null  float64\n",
      " 60  rougher.state.floatbank10_d_level                   22649 non-null  float64\n",
      " 61  rougher.state.floatbank10_e_air                     22096 non-null  float64\n",
      " 62  rougher.state.floatbank10_e_level                   22649 non-null  float64\n",
      " 63  rougher.state.floatbank10_f_air                     22641 non-null  float64\n",
      " 64  rougher.state.floatbank10_f_level                   22642 non-null  float64\n",
      " 65  secondary_cleaner.output.tail_ag                    22616 non-null  float64\n",
      " 66  secondary_cleaner.output.tail_pb                    22600 non-null  float64\n",
      " 67  secondary_cleaner.output.tail_sol                   20501 non-null  float64\n",
      " 68  secondary_cleaner.output.tail_au                    22618 non-null  float64\n",
      " 69  secondary_cleaner.state.floatbank2_a_air            22333 non-null  float64\n",
      " 70  secondary_cleaner.state.floatbank2_a_level          22591 non-null  float64\n",
      " 71  secondary_cleaner.state.floatbank2_b_air            22538 non-null  float64\n",
      " 72  secondary_cleaner.state.floatbank2_b_level          22588 non-null  float64\n",
      " 73  secondary_cleaner.state.floatbank3_a_air            22585 non-null  float64\n",
      " 74  secondary_cleaner.state.floatbank3_a_level          22587 non-null  float64\n",
      " 75  secondary_cleaner.state.floatbank3_b_air            22592 non-null  float64\n",
      " 76  secondary_cleaner.state.floatbank3_b_level          22590 non-null  float64\n",
      " 77  secondary_cleaner.state.floatbank4_a_air            22571 non-null  float64\n",
      " 78  secondary_cleaner.state.floatbank4_a_level          22587 non-null  float64\n",
      " 79  secondary_cleaner.state.floatbank4_b_air            22608 non-null  float64\n",
      " 80  secondary_cleaner.state.floatbank4_b_level          22607 non-null  float64\n",
      " 81  secondary_cleaner.state.floatbank5_a_air            22615 non-null  float64\n",
      " 82  secondary_cleaner.state.floatbank5_a_level          22615 non-null  float64\n",
      " 83  secondary_cleaner.state.floatbank5_b_air            22615 non-null  float64\n",
      " 84  secondary_cleaner.state.floatbank5_b_level          22616 non-null  float64\n",
      " 85  secondary_cleaner.state.floatbank6_a_air            22597 non-null  float64\n",
      " 86  secondary_cleaner.state.floatbank6_a_level          22615 non-null  float64\n",
      "dtypes: float64(86), object(1)\n",
      "memory usage: 15.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Describe:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22627.000000</td>\n",
       "      <td>22629.000000</td>\n",
       "      <td>22331.000000</td>\n",
       "      <td>22630.000000</td>\n",
       "      <td>20753.000000</td>\n",
       "      <td>22633.000000</td>\n",
       "      <td>22516.000000</td>\n",
       "      <td>22445.000000</td>\n",
       "      <td>22635.000000</td>\n",
       "      <td>21107.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>22571.000000</td>\n",
       "      <td>22587.000000</td>\n",
       "      <td>22608.000000</td>\n",
       "      <td>22607.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "      <td>22616.000000</td>\n",
       "      <td>22597.000000</td>\n",
       "      <td>22615.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.781559</td>\n",
       "      <td>9.095308</td>\n",
       "      <td>8.640317</td>\n",
       "      <td>40.001172</td>\n",
       "      <td>67.447488</td>\n",
       "      <td>8.923690</td>\n",
       "      <td>2.488252</td>\n",
       "      <td>9.523632</td>\n",
       "      <td>2.827459</td>\n",
       "      <td>140.277672</td>\n",
       "      <td>...</td>\n",
       "      <td>18.205125</td>\n",
       "      <td>-499.878977</td>\n",
       "      <td>14.356474</td>\n",
       "      <td>-476.532613</td>\n",
       "      <td>14.883276</td>\n",
       "      <td>-503.323288</td>\n",
       "      <td>11.626743</td>\n",
       "      <td>-500.521502</td>\n",
       "      <td>17.976810</td>\n",
       "      <td>-519.361465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.030128</td>\n",
       "      <td>3.230797</td>\n",
       "      <td>3.785035</td>\n",
       "      <td>13.398062</td>\n",
       "      <td>11.616034</td>\n",
       "      <td>3.517917</td>\n",
       "      <td>1.189407</td>\n",
       "      <td>4.079739</td>\n",
       "      <td>1.262834</td>\n",
       "      <td>49.919004</td>\n",
       "      <td>...</td>\n",
       "      <td>6.560700</td>\n",
       "      <td>80.273964</td>\n",
       "      <td>5.655791</td>\n",
       "      <td>93.822791</td>\n",
       "      <td>6.372811</td>\n",
       "      <td>72.925589</td>\n",
       "      <td>5.757449</td>\n",
       "      <td>78.956292</td>\n",
       "      <td>6.636203</td>\n",
       "      <td>75.477151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-799.920713</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-800.836914</td>\n",
       "      <td>-0.423260</td>\n",
       "      <td>-799.741097</td>\n",
       "      <td>0.427084</td>\n",
       "      <td>-800.258209</td>\n",
       "      <td>-0.079426</td>\n",
       "      <td>-810.473526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.018525</td>\n",
       "      <td>8.750171</td>\n",
       "      <td>7.116799</td>\n",
       "      <td>42.383721</td>\n",
       "      <td>63.282393</td>\n",
       "      <td>7.684016</td>\n",
       "      <td>1.805376</td>\n",
       "      <td>8.143576</td>\n",
       "      <td>2.303108</td>\n",
       "      <td>110.177081</td>\n",
       "      <td>...</td>\n",
       "      <td>14.095940</td>\n",
       "      <td>-500.896232</td>\n",
       "      <td>10.882675</td>\n",
       "      <td>-500.309169</td>\n",
       "      <td>10.941299</td>\n",
       "      <td>-500.628697</td>\n",
       "      <td>8.037533</td>\n",
       "      <td>-500.167897</td>\n",
       "      <td>13.968418</td>\n",
       "      <td>-500.981671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.953729</td>\n",
       "      <td>9.914519</td>\n",
       "      <td>8.908792</td>\n",
       "      <td>44.653436</td>\n",
       "      <td>68.322258</td>\n",
       "      <td>9.484369</td>\n",
       "      <td>2.653001</td>\n",
       "      <td>10.212998</td>\n",
       "      <td>2.913794</td>\n",
       "      <td>141.330501</td>\n",
       "      <td>...</td>\n",
       "      <td>18.007326</td>\n",
       "      <td>-499.917108</td>\n",
       "      <td>14.947646</td>\n",
       "      <td>-499.612292</td>\n",
       "      <td>14.859117</td>\n",
       "      <td>-499.865158</td>\n",
       "      <td>10.989756</td>\n",
       "      <td>-499.951980</td>\n",
       "      <td>18.004215</td>\n",
       "      <td>-500.095463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.862593</td>\n",
       "      <td>10.929839</td>\n",
       "      <td>10.705824</td>\n",
       "      <td>46.111999</td>\n",
       "      <td>72.950836</td>\n",
       "      <td>11.084557</td>\n",
       "      <td>3.287790</td>\n",
       "      <td>11.860824</td>\n",
       "      <td>3.555077</td>\n",
       "      <td>174.049914</td>\n",
       "      <td>...</td>\n",
       "      <td>22.998194</td>\n",
       "      <td>-498.361545</td>\n",
       "      <td>17.977502</td>\n",
       "      <td>-400.224147</td>\n",
       "      <td>18.014914</td>\n",
       "      <td>-498.489381</td>\n",
       "      <td>14.001193</td>\n",
       "      <td>-499.492354</td>\n",
       "      <td>23.009704</td>\n",
       "      <td>-499.526388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.001945</td>\n",
       "      <td>17.031899</td>\n",
       "      <td>19.615720</td>\n",
       "      <td>53.611374</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>19.552149</td>\n",
       "      <td>6.086532</td>\n",
       "      <td>22.861749</td>\n",
       "      <td>9.789625</td>\n",
       "      <td>274.409626</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-127.692333</td>\n",
       "      <td>31.269706</td>\n",
       "      <td>-6.506986</td>\n",
       "      <td>63.116298</td>\n",
       "      <td>-244.483566</td>\n",
       "      <td>39.846228</td>\n",
       "      <td>-120.190931</td>\n",
       "      <td>54.876806</td>\n",
       "      <td>-29.093593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 86 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "count                 22627.000000                 22629.000000   \n",
       "mean                      4.781559                     9.095308   \n",
       "std                       2.030128                     3.230797   \n",
       "min                       0.000000                     0.000000   \n",
       "25%                       4.018525                     8.750171   \n",
       "50%                       4.953729                     9.914519   \n",
       "75%                       5.862593                    10.929839   \n",
       "max                      16.001945                    17.031899   \n",
       "\n",
       "       final.output.concentrate_sol  final.output.concentrate_au  \\\n",
       "count                  22331.000000                 22630.000000   \n",
       "mean                       8.640317                    40.001172   \n",
       "std                        3.785035                    13.398062   \n",
       "min                        0.000000                     0.000000   \n",
       "25%                        7.116799                    42.383721   \n",
       "50%                        8.908792                    44.653436   \n",
       "75%                       10.705824                    46.111999   \n",
       "max                       19.615720                    53.611374   \n",
       "\n",
       "       final.output.recovery  final.output.tail_ag  final.output.tail_pb  \\\n",
       "count           20753.000000          22633.000000          22516.000000   \n",
       "mean               67.447488              8.923690              2.488252   \n",
       "std                11.616034              3.517917              1.189407   \n",
       "min                 0.000000              0.000000              0.000000   \n",
       "25%                63.282393              7.684016              1.805376   \n",
       "50%                68.322258              9.484369              2.653001   \n",
       "75%                72.950836             11.084557              3.287790   \n",
       "max               100.000000             19.552149              6.086532   \n",
       "\n",
       "       final.output.tail_sol  final.output.tail_au  \\\n",
       "count           22445.000000          22635.000000   \n",
       "mean                9.523632              2.827459   \n",
       "std                 4.079739              1.262834   \n",
       "min                 0.000000              0.000000   \n",
       "25%                 8.143576              2.303108   \n",
       "50%                10.212998              2.913794   \n",
       "75%                11.860824              3.555077   \n",
       "max                22.861749              9.789625   \n",
       "\n",
       "       primary_cleaner.input.sulfate  ...  \\\n",
       "count                   21107.000000  ...   \n",
       "mean                      140.277672  ...   \n",
       "std                        49.919004  ...   \n",
       "min                         0.000003  ...   \n",
       "25%                       110.177081  ...   \n",
       "50%                       141.330501  ...   \n",
       "75%                       174.049914  ...   \n",
       "max                       274.409626  ...   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_air  \\\n",
       "count                              22571.000000   \n",
       "mean                                  18.205125   \n",
       "std                                    6.560700   \n",
       "min                                    0.000000   \n",
       "25%                                   14.095940   \n",
       "50%                                   18.007326   \n",
       "75%                                   22.998194   \n",
       "max                                   60.000000   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_a_level  \\\n",
       "count                                22587.000000   \n",
       "mean                                  -499.878977   \n",
       "std                                     80.273964   \n",
       "min                                   -799.920713   \n",
       "25%                                   -500.896232   \n",
       "50%                                   -499.917108   \n",
       "75%                                   -498.361545   \n",
       "max                                   -127.692333   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_air  \\\n",
       "count                              22608.000000   \n",
       "mean                                  14.356474   \n",
       "std                                    5.655791   \n",
       "min                                    0.000000   \n",
       "25%                                   10.882675   \n",
       "50%                                   14.947646   \n",
       "75%                                   17.977502   \n",
       "max                                   31.269706   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                22607.000000   \n",
       "mean                                  -476.532613   \n",
       "std                                     93.822791   \n",
       "min                                   -800.836914   \n",
       "25%                                   -500.309169   \n",
       "50%                                   -499.612292   \n",
       "75%                                   -400.224147   \n",
       "max                                     -6.506986   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                              22615.000000   \n",
       "mean                                  14.883276   \n",
       "std                                    6.372811   \n",
       "min                                   -0.423260   \n",
       "25%                                   10.941299   \n",
       "50%                                   14.859117   \n",
       "75%                                   18.014914   \n",
       "max                                   63.116298   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                22615.000000   \n",
       "mean                                  -503.323288   \n",
       "std                                     72.925589   \n",
       "min                                   -799.741097   \n",
       "25%                                   -500.628697   \n",
       "50%                                   -499.865158   \n",
       "75%                                   -498.489381   \n",
       "max                                   -244.483566   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                              22615.000000   \n",
       "mean                                  11.626743   \n",
       "std                                    5.757449   \n",
       "min                                    0.427084   \n",
       "25%                                    8.037533   \n",
       "50%                                   10.989756   \n",
       "75%                                   14.001193   \n",
       "max                                   39.846228   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                22616.000000   \n",
       "mean                                  -500.521502   \n",
       "std                                     78.956292   \n",
       "min                                   -800.258209   \n",
       "25%                                   -500.167897   \n",
       "50%                                   -499.951980   \n",
       "75%                                   -499.492354   \n",
       "max                                   -120.190931   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                              22597.000000   \n",
       "mean                                  17.976810   \n",
       "std                                    6.636203   \n",
       "min                                   -0.079426   \n",
       "25%                                   13.968418   \n",
       "50%                                   18.004215   \n",
       "75%                                   23.009704   \n",
       "max                                   54.876806   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \n",
       "count                                22615.000000  \n",
       "mean                                  -519.361465  \n",
       "std                                     75.477151  \n",
       "min                                   -810.473526  \n",
       "25%                                   -500.981671  \n",
       "50%                                   -500.095463  \n",
       "75%                                   -499.526388  \n",
       "max                                    -29.093593  \n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>22716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2017-05-11 18:59:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                       date\n",
       "count                 22716\n",
       "unique                22716\n",
       "top     2017-05-11 18:59:59\n",
       "freq                      1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Columns with nulls:\n",
      "Column final.output.concentrate_ag has 0.3918% percent of Nulls, and 89 of nulls\n",
      "Column final.output.concentrate_pb has 0.3830% percent of Nulls, and 87 of nulls\n",
      "Column final.output.concentrate_sol has 1.6948% percent of Nulls, and 385 of nulls\n",
      "Column final.output.concentrate_au has 0.3786% percent of Nulls, and 86 of nulls\n",
      "Column final.output.recovery has 8.6415% percent of Nulls, and 1963 of nulls\n",
      "Column final.output.tail_ag has 0.3654% percent of Nulls, and 83 of nulls\n",
      "Column final.output.tail_pb has 0.8804% percent of Nulls, and 200 of nulls\n",
      "Column final.output.tail_sol has 1.1930% percent of Nulls, and 271 of nulls\n",
      "Column final.output.tail_au has 0.3566% percent of Nulls, and 81 of nulls\n",
      "Column primary_cleaner.input.sulfate has 7.0831% percent of Nulls, and 1609 of nulls\n",
      "Column primary_cleaner.input.depressant has 6.8058% percent of Nulls, and 1546 of nulls\n",
      "Column primary_cleaner.input.xanthate has 5.0669% percent of Nulls, and 1151 of nulls\n",
      "Column primary_cleaner.output.concentrate_ag has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column primary_cleaner.output.concentrate_pb has 1.9722% percent of Nulls, and 448 of nulls\n",
      "Column primary_cleaner.output.concentrate_sol has 3.5129% percent of Nulls, and 798 of nulls\n",
      "Column primary_cleaner.output.concentrate_au has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column primary_cleaner.output.tail_ag has 0.4490% percent of Nulls, and 102 of nulls\n",
      "Column primary_cleaner.output.tail_pb has 0.5371% percent of Nulls, and 122 of nulls\n",
      "Column primary_cleaner.output.tail_sol has 1.5452% percent of Nulls, and 351 of nulls\n",
      "Column primary_cleaner.output.tail_au has 0.4358% percent of Nulls, and 99 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_air has 0.2465% percent of Nulls, and 56 of nulls\n",
      "Column primary_cleaner.state.floatbank8_a_level has 0.2157% percent of Nulls, and 49 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_air has 0.2465% percent of Nulls, and 56 of nulls\n",
      "Column primary_cleaner.state.floatbank8_b_level has 0.1893% percent of Nulls, and 43 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_air has 0.2377% percent of Nulls, and 54 of nulls\n",
      "Column primary_cleaner.state.floatbank8_c_level has 0.1893% percent of Nulls, and 43 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_air has 0.2421% percent of Nulls, and 55 of nulls\n",
      "Column primary_cleaner.state.floatbank8_d_level has 0.1893% percent of Nulls, and 43 of nulls\n",
      "Column rougher.calculation.sulfate_to_au_concentrate has 0.1937% percent of Nulls, and 44 of nulls\n",
      "Column rougher.calculation.floatbank10_sulfate_to_au_feed has 0.1937% percent of Nulls, and 44 of nulls\n",
      "Column rougher.calculation.floatbank11_sulfate_to_au_feed has 0.1937% percent of Nulls, and 44 of nulls\n",
      "Column rougher.calculation.au_pb_ratio has 7.1624% percent of Nulls, and 1627 of nulls\n",
      "Column rougher.input.feed_ag has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column rougher.input.feed_pb has 1.0741% percent of Nulls, and 244 of nulls\n",
      "Column rougher.input.feed_rate has 2.4344% percent of Nulls, and 553 of nulls\n",
      "Column rougher.input.feed_size has 1.9326% percent of Nulls, and 439 of nulls\n",
      "Column rougher.input.feed_sol has 1.5804% percent of Nulls, and 359 of nulls\n",
      "Column rougher.input.feed_au has 0.4358% percent of Nulls, and 99 of nulls\n",
      "Column rougher.input.floatbank10_sulfate has 5.7272% percent of Nulls, and 1301 of nulls\n",
      "Column rougher.input.floatbank10_xanthate has 2.0646% percent of Nulls, and 469 of nulls\n",
      "Column rougher.input.floatbank11_sulfate has 2.9847% percent of Nulls, and 678 of nulls\n",
      "Column rougher.input.floatbank11_xanthate has 9.9357% percent of Nulls, and 2257 of nulls\n",
      "Column rougher.output.concentrate_ag has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column rougher.output.concentrate_pb has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column rougher.output.concentrate_sol has 0.8364% percent of Nulls, and 190 of nulls\n",
      "Column rougher.output.concentrate_au has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column rougher.output.recovery has 13.7304% percent of Nulls, and 3119 of nulls\n",
      "Column rougher.output.tail_ag has 12.0488% percent of Nulls, and 2737 of nulls\n",
      "Column rougher.output.tail_pb has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column rougher.output.tail_sol has 12.0444% percent of Nulls, and 2736 of nulls\n",
      "Column rougher.output.tail_au has 12.0444% percent of Nulls, and 2736 of nulls\n",
      "Column rougher.state.floatbank10_a_air has 0.3082% percent of Nulls, and 70 of nulls\n",
      "Column rougher.state.floatbank10_a_level has 0.3038% percent of Nulls, and 69 of nulls\n",
      "Column rougher.state.floatbank10_b_air has 0.3082% percent of Nulls, and 70 of nulls\n",
      "Column rougher.state.floatbank10_b_level has 0.3038% percent of Nulls, and 69 of nulls\n",
      "Column rougher.state.floatbank10_c_air has 0.3082% percent of Nulls, and 70 of nulls\n",
      "Column rougher.state.floatbank10_c_level has 0.2729% percent of Nulls, and 62 of nulls\n",
      "Column rougher.state.floatbank10_d_air has 0.3302% percent of Nulls, and 75 of nulls\n",
      "Column rougher.state.floatbank10_d_level has 0.2949% percent of Nulls, and 67 of nulls\n",
      "Column rougher.state.floatbank10_e_air has 2.7294% percent of Nulls, and 620 of nulls\n",
      "Column rougher.state.floatbank10_e_level has 0.2949% percent of Nulls, and 67 of nulls\n",
      "Column rougher.state.floatbank10_f_air has 0.3302% percent of Nulls, and 75 of nulls\n",
      "Column rougher.state.floatbank10_f_level has 0.3258% percent of Nulls, and 74 of nulls\n",
      "Column secondary_cleaner.output.tail_ag has 0.4402% percent of Nulls, and 100 of nulls\n",
      "Column secondary_cleaner.output.tail_pb has 0.5107% percent of Nulls, and 116 of nulls\n",
      "Column secondary_cleaner.output.tail_sol has 9.7508% percent of Nulls, and 2215 of nulls\n",
      "Column secondary_cleaner.output.tail_au has 0.4314% percent of Nulls, and 98 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_air has 1.6860% percent of Nulls, and 383 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_a_level has 0.5503% percent of Nulls, and 125 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_air has 0.7836% percent of Nulls, and 178 of nulls\n",
      "Column secondary_cleaner.state.floatbank2_b_level has 0.5635% percent of Nulls, and 128 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_air has 0.5767% percent of Nulls, and 131 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_a_level has 0.5679% percent of Nulls, and 129 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_air has 0.5459% percent of Nulls, and 124 of nulls\n",
      "Column secondary_cleaner.state.floatbank3_b_level has 0.5547% percent of Nulls, and 126 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_air has 0.6383% percent of Nulls, and 145 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_a_level has 0.5679% percent of Nulls, and 129 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_air has 0.4754% percent of Nulls, and 108 of nulls\n",
      "Column secondary_cleaner.state.floatbank4_b_level has 0.4798% percent of Nulls, and 109 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_air has 0.4446% percent of Nulls, and 101 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_a_level has 0.4446% percent of Nulls, and 101 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_air has 0.4446% percent of Nulls, and 101 of nulls\n",
      "Column secondary_cleaner.state.floatbank5_b_level has 0.4402% percent of Nulls, and 100 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_air has 0.5239% percent of Nulls, and 119 of nulls\n",
      "Column secondary_cleaner.state.floatbank6_a_level has 0.4446% percent of Nulls, and 101 of nulls\n",
      "\u001b[1mThere are 85 columns with NA.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Shape:\n",
      "(22716, 87)\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Duplicated:\n",
      "\u001b[1mWe have 0 duplicated rows.\n",
      "\u001b[0m\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('General information about the dataframe')\n",
    "get_info(gold_recovery_train)\n",
    "get_info(gold_recovery_test)\n",
    "get_info(gold_recovery_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary \n",
    "\n",
    "In the gold_recovery_train.csv dataset, we have 16,860 rows, 86 features, and 85 columns with NA's.\n",
    "In the gold_recovery_test.csv dataset, we have 5,856 rows, 52 features, and 51 columns with NA's.\n",
    "In the gold_recovery_full.csv dataset, we have 22,716 rows, 86 features, and 85 columns with NA's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "The data was loaded and inspected\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate recovery and determine MAE between calculated recovery and feature values recovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        87.107763\n",
       "1        86.843261\n",
       "2        86.842308\n",
       "3        87.226430\n",
       "4        86.688794\n",
       "           ...    \n",
       "16855    89.574376\n",
       "16856    87.724007\n",
       "16857    88.890579\n",
       "16858    89.858126\n",
       "16859    89.514960\n",
       "Length: 16860, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = gold_recovery_train['rougher.output.concentrate_au']\n",
    "F = gold_recovery_train['rougher.input.feed_au']\n",
    "T = gold_recovery_train['rougher.output.tail_au']\n",
    "\n",
    "calculated_recovery = (C * (F - T)) / (F * (C - T)) * 100\n",
    "calculated_recovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MAE calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MAE score is: 0.00\n"
     ]
    }
   ],
   "source": [
    "mae_calc = pd.DataFrame({'calculated_recovery': calculated_recovery, 'output_recovery': gold_recovery_train['rougher.output.recovery']}).dropna()\n",
    "mae = mean_absolute_error(mae_calc.calculated_recovery, mae_calc.output_recovery)\n",
    "print('The MAE score is: {:.2f}'.format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now Ill check sample of calculated recovery and output recovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calculated_recovery</th>\n",
       "      <th>output_recovery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5713</th>\n",
       "      <td>86.118713</td>\n",
       "      <td>86.118713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14387</th>\n",
       "      <td>87.462479</td>\n",
       "      <td>87.462479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15561</th>\n",
       "      <td>92.630778</td>\n",
       "      <td>92.630778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>92.492118</td>\n",
       "      <td>92.492118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5202</th>\n",
       "      <td>86.879073</td>\n",
       "      <td>86.879073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       calculated_recovery  output_recovery\n",
       "5713             86.118713        86.118713\n",
       "14387            87.462479        87.462479\n",
       "15561            92.630778        92.630778\n",
       "1019             92.492118        92.492118\n",
       "5202             86.879073        86.879073"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae_calc.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary \n",
    "The calculated_recovery and rougher.output.recovery have similar values. The MAE score is 0.0. This indicates the calculated value from the simulated recovery process is similar to the rougher.output.recovery."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Recovery data was validated successfully!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the features not available in the test set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will first create a dataframe of the features not available in the test sets and inspect its information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rougher.calculation.floatbank10_sulfate_to_au_feed',\n",
       " 'secondary_cleaner.output.tail_sol',\n",
       " 'primary_cleaner.output.tail_ag',\n",
       " 'secondary_cleaner.output.tail_au',\n",
       " 'final.output.tail_ag',\n",
       " 'primary_cleaner.output.tail_au',\n",
       " 'rougher.output.tail_au',\n",
       " 'rougher.calculation.sulfate_to_au_concentrate',\n",
       " 'primary_cleaner.output.tail_pb',\n",
       " 'primary_cleaner.output.tail_sol',\n",
       " 'rougher.output.tail_sol',\n",
       " 'rougher.output.recovery',\n",
       " 'rougher.output.concentrate_pb',\n",
       " 'final.output.concentrate_au',\n",
       " 'final.output.recovery',\n",
       " 'final.output.concentrate_pb',\n",
       " 'rougher.calculation.floatbank11_sulfate_to_au_feed',\n",
       " 'secondary_cleaner.output.tail_pb',\n",
       " 'rougher.calculation.au_pb_ratio',\n",
       " 'final.output.tail_sol',\n",
       " 'primary_cleaner.output.concentrate_ag',\n",
       " 'final.output.concentrate_sol',\n",
       " 'secondary_cleaner.output.tail_ag',\n",
       " 'final.output.concentrate_ag',\n",
       " 'rougher.output.tail_pb',\n",
       " 'primary_cleaner.output.concentrate_sol',\n",
       " 'primary_cleaner.output.concentrate_au',\n",
       " 'rougher.output.concentrate_sol',\n",
       " 'final.output.tail_pb',\n",
       " 'rougher.output.tail_ag',\n",
       " 'final.output.tail_au',\n",
       " 'primary_cleaner.output.concentrate_pb',\n",
       " 'rougher.output.concentrate_ag',\n",
       " 'rougher.output.concentrate_au']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_diff = list(set(gold_recovery_train.columns).difference(gold_recovery_test.columns))\n",
    "features_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "\n",
    "There are 34 features in the train set that are not available in the test set. The missing features contain the concentration of metals (Au, Ag, Pb). The parameters type are output - product parameters and calculation characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Yep, you are right!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "Ill make a function to calculate the percentage of missing values and the total number, presented as a table. Because these are time series data points, the forward fill method should work nicely to resolve our missing data points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_values_table(df):\n",
    "    mis_val = df.isnull().sum()\n",
    "\n",
    "    mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "\n",
    "    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "\n",
    "    mis_val_table_ren_columns = mis_val_table.rename(\n",
    "    columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "\n",
    "    mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "        mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "    '% of Total Values', ascending=False).round(1)\n",
    "\n",
    "    print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "        \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "            \" columns that have missing values.\")\n",
    "\n",
    "    return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5856, 53)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_test=gold_recovery_test.merge(gold_recovery_full[['final.output.recovery', 'rougher.output.recovery']], how='left', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5856, 55)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_date_time(df, col):\n",
    "    df[col] = pd.to_datetime(df[col])\n",
    "    df.sort_values(col, inplace=True)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 87 columns.\n",
      "There are 85 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <td>2573</td>\n",
       "      <td>15.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_ag</th>\n",
       "      <td>2250</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_au</th>\n",
       "      <td>2249</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_sol</th>\n",
       "      <td>2249</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.output.tail_sol</th>\n",
       "      <td>1986</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <td>27</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <td>27</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <td>27</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.calculation.sulfate_to_au_concentrate</th>\n",
       "      <td>27</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.calculation.floatbank11_sulfate_to_au_feed</th>\n",
       "      <td>27</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Missing Values  \\\n",
       "rougher.output.recovery                                       2573   \n",
       "rougher.output.tail_ag                                        2250   \n",
       "rougher.output.tail_au                                        2249   \n",
       "rougher.output.tail_sol                                       2249   \n",
       "secondary_cleaner.output.tail_sol                             1986   \n",
       "...                                                            ...   \n",
       "primary_cleaner.state.floatbank8_b_level                        27   \n",
       "primary_cleaner.state.floatbank8_c_level                        27   \n",
       "primary_cleaner.state.floatbank8_d_level                        27   \n",
       "rougher.calculation.sulfate_to_au_concentrate                   27   \n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed              27   \n",
       "\n",
       "                                                    % of Total Values  \n",
       "rougher.output.recovery                                          15.3  \n",
       "rougher.output.tail_ag                                           13.3  \n",
       "rougher.output.tail_au                                           13.3  \n",
       "rougher.output.tail_sol                                          13.3  \n",
       "secondary_cleaner.output.tail_sol                                11.8  \n",
       "...                                                               ...  \n",
       "primary_cleaner.state.floatbank8_b_level                          0.2  \n",
       "primary_cleaner.state.floatbank8_c_level                          0.2  \n",
       "primary_cleaner.state.floatbank8_d_level                          0.2  \n",
       "rougher.calculation.sulfate_to_au_concentrate                     0.2  \n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed                0.2  \n",
       "\n",
       "[85 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 55 columns.\n",
      "There are 53 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <td>830</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>final.output.recovery</th>\n",
       "      <td>614</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <td>353</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <td>302</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <td>284</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <td>257</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <td>166</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <td>123</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <td>67</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <td>55</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <td>40</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <td>34</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <td>23</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <td>22</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <td>20</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Missing Values  % of Total Values\n",
       "rougher.output.recovery                                830               14.2\n",
       "final.output.recovery                                  614               10.5\n",
       "rougher.input.floatbank11_xanthate                     353                6.0\n",
       "primary_cleaner.input.sulfate                          302                5.2\n",
       "primary_cleaner.input.depressant                       284                4.8\n",
       "rougher.input.floatbank10_sulfate                      257                4.4\n",
       "primary_cleaner.input.xanthate                         166                2.8\n",
       "rougher.input.floatbank10_xanthate                     123                2.1\n",
       "rougher.input.feed_sol                                  67                1.1\n",
       "rougher.input.floatbank11_sulfate                       55                0.9\n",
       "rougher.input.feed_rate                                 40                0.7\n",
       "secondary_cleaner.state.floatbank3_a_air                34                0.6\n",
       "secondary_cleaner.state.floatbank2_b_air                23                0.4\n",
       "rougher.input.feed_size                                 22                0.4\n",
       "secondary_cleaner.state.floatbank2_a_air                20                0.3\n",
       "rougher.state.floatbank10_c_air                         17                0.3\n",
       "rougher.state.floatbank10_e_air                         17                0.3\n",
       "rougher.state.floatbank10_f_air                         17                0.3\n",
       "rougher.state.floatbank10_d_air                         17                0.3\n",
       "rougher.state.floatbank10_a_air                         17                0.3\n",
       "rougher.state.floatbank10_b_air                         17                0.3\n",
       "secondary_cleaner.state.floatbank3_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank3_b_air                16                0.3\n",
       "secondary_cleaner.state.floatbank3_a_level              16                0.3\n",
       "primary_cleaner.state.floatbank8_a_level                16                0.3\n",
       "secondary_cleaner.state.floatbank4_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank4_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank2_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank4_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank5_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank5_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank5_b_air                16                0.3\n",
       "secondary_cleaner.state.floatbank5_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank6_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank6_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank4_b_air                16                0.3\n",
       "rougher.state.floatbank10_d_level                       16                0.3\n",
       "secondary_cleaner.state.floatbank2_a_level              16                0.3\n",
       "rougher.state.floatbank10_f_level                       16                0.3\n",
       "rougher.state.floatbank10_e_level                       16                0.3\n",
       "primary_cleaner.state.floatbank8_b_air                  16                0.3\n",
       "rougher.state.floatbank10_b_level                       16                0.3\n",
       "rougher.state.floatbank10_a_level                       16                0.3\n",
       "primary_cleaner.state.floatbank8_a_air                  16                0.3\n",
       "rougher.input.feed_au                                   16                0.3\n",
       "rougher.input.feed_pb                                   16                0.3\n",
       "rougher.input.feed_ag                                   16                0.3\n",
       "primary_cleaner.state.floatbank8_d_level                16                0.3\n",
       "primary_cleaner.state.floatbank8_d_air                  16                0.3\n",
       "primary_cleaner.state.floatbank8_c_level                16                0.3\n",
       "primary_cleaner.state.floatbank8_c_air                  16                0.3\n",
       "primary_cleaner.state.floatbank8_b_level                16                0.3\n",
       "rougher.state.floatbank10_c_level                       16                0.3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 87 columns.\n",
      "There are 85 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <td>3119</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_ag</th>\n",
       "      <td>2737</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_au</th>\n",
       "      <td>2736</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.output.tail_sol</th>\n",
       "      <td>2736</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <td>2257</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.calculation.floatbank10_sulfate_to_au_feed</th>\n",
       "      <td>44</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.calculation.floatbank11_sulfate_to_au_feed</th>\n",
       "      <td>44</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <td>43</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <td>43</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <td>43</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Missing Values  \\\n",
       "rougher.output.recovery                                       3119   \n",
       "rougher.output.tail_ag                                        2737   \n",
       "rougher.output.tail_au                                        2736   \n",
       "rougher.output.tail_sol                                       2736   \n",
       "rougher.input.floatbank11_xanthate                            2257   \n",
       "...                                                            ...   \n",
       "rougher.calculation.floatbank10_sulfate_to_au_feed              44   \n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed              44   \n",
       "primary_cleaner.state.floatbank8_d_level                        43   \n",
       "primary_cleaner.state.floatbank8_c_level                        43   \n",
       "primary_cleaner.state.floatbank8_b_level                        43   \n",
       "\n",
       "                                                    % of Total Values  \n",
       "rougher.output.recovery                                          13.7  \n",
       "rougher.output.tail_ag                                           12.0  \n",
       "rougher.output.tail_au                                           12.0  \n",
       "rougher.output.tail_sol                                          12.0  \n",
       "rougher.input.floatbank11_xanthate                                9.9  \n",
       "...                                                               ...  \n",
       "rougher.calculation.floatbank10_sulfate_to_au_feed                0.2  \n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed                0.2  \n",
       "primary_cleaner.state.floatbank8_d_level                          0.2  \n",
       "primary_cleaner.state.floatbank8_c_level                          0.2  \n",
       "primary_cleaner.state.floatbank8_b_level                          0.2  \n",
       "\n",
       "[85 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resolving the missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since missing values in the target cannot be inferred, we will drop the null values from each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_train=gold_recovery_train.dropna()\n",
    "golf_recovery_test= gold_recovery_test.dropna()\n",
    "gold_recovery_full=gold_recovery_full.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will check each dataset for any values that are still missing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 87 columns.\n",
      "There are 0 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Values, % of Total Values]\n",
       "Index: []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 55 columns.\n",
      "There are 53 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <td>830</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>final.output.recovery</th>\n",
       "      <td>614</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank11_xanthate</th>\n",
       "      <td>353</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <td>302</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.depressant</th>\n",
       "      <td>284</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank10_sulfate</th>\n",
       "      <td>257</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.input.xanthate</th>\n",
       "      <td>166</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank10_xanthate</th>\n",
       "      <td>123</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_sol</th>\n",
       "      <td>67</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.floatbank11_sulfate</th>\n",
       "      <td>55</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_rate</th>\n",
       "      <td>40</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_air</th>\n",
       "      <td>34</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_air</th>\n",
       "      <td>23</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_size</th>\n",
       "      <td>22</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_air</th>\n",
       "      <td>20</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_c_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_e_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_f_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_d_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_a_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_b_air</th>\n",
       "      <td>17</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank3_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_d_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary_cleaner.state.floatbank2_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_f_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_e_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_a_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_a_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_au</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_pb</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.input.feed_ag</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_d_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_c_air</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary_cleaner.state.floatbank8_b_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rougher.state.floatbank10_c_level</th>\n",
       "      <td>16</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Missing Values  % of Total Values\n",
       "rougher.output.recovery                                830               14.2\n",
       "final.output.recovery                                  614               10.5\n",
       "rougher.input.floatbank11_xanthate                     353                6.0\n",
       "primary_cleaner.input.sulfate                          302                5.2\n",
       "primary_cleaner.input.depressant                       284                4.8\n",
       "rougher.input.floatbank10_sulfate                      257                4.4\n",
       "primary_cleaner.input.xanthate                         166                2.8\n",
       "rougher.input.floatbank10_xanthate                     123                2.1\n",
       "rougher.input.feed_sol                                  67                1.1\n",
       "rougher.input.floatbank11_sulfate                       55                0.9\n",
       "rougher.input.feed_rate                                 40                0.7\n",
       "secondary_cleaner.state.floatbank3_a_air                34                0.6\n",
       "secondary_cleaner.state.floatbank2_b_air                23                0.4\n",
       "rougher.input.feed_size                                 22                0.4\n",
       "secondary_cleaner.state.floatbank2_a_air                20                0.3\n",
       "rougher.state.floatbank10_c_air                         17                0.3\n",
       "rougher.state.floatbank10_e_air                         17                0.3\n",
       "rougher.state.floatbank10_f_air                         17                0.3\n",
       "rougher.state.floatbank10_d_air                         17                0.3\n",
       "rougher.state.floatbank10_a_air                         17                0.3\n",
       "rougher.state.floatbank10_b_air                         17                0.3\n",
       "secondary_cleaner.state.floatbank3_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank3_b_air                16                0.3\n",
       "secondary_cleaner.state.floatbank3_a_level              16                0.3\n",
       "primary_cleaner.state.floatbank8_a_level                16                0.3\n",
       "secondary_cleaner.state.floatbank4_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank4_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank2_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank4_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank5_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank5_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank5_b_air                16                0.3\n",
       "secondary_cleaner.state.floatbank5_b_level              16                0.3\n",
       "secondary_cleaner.state.floatbank6_a_air                16                0.3\n",
       "secondary_cleaner.state.floatbank6_a_level              16                0.3\n",
       "secondary_cleaner.state.floatbank4_b_air                16                0.3\n",
       "rougher.state.floatbank10_d_level                       16                0.3\n",
       "secondary_cleaner.state.floatbank2_a_level              16                0.3\n",
       "rougher.state.floatbank10_f_level                       16                0.3\n",
       "rougher.state.floatbank10_e_level                       16                0.3\n",
       "primary_cleaner.state.floatbank8_b_air                  16                0.3\n",
       "rougher.state.floatbank10_b_level                       16                0.3\n",
       "rougher.state.floatbank10_a_level                       16                0.3\n",
       "primary_cleaner.state.floatbank8_a_air                  16                0.3\n",
       "rougher.input.feed_au                                   16                0.3\n",
       "rougher.input.feed_pb                                   16                0.3\n",
       "rougher.input.feed_ag                                   16                0.3\n",
       "primary_cleaner.state.floatbank8_d_level                16                0.3\n",
       "primary_cleaner.state.floatbank8_d_air                  16                0.3\n",
       "primary_cleaner.state.floatbank8_c_level                16                0.3\n",
       "primary_cleaner.state.floatbank8_c_air                  16                0.3\n",
       "primary_cleaner.state.floatbank8_b_level                16                0.3\n",
       "rougher.state.floatbank10_c_level                       16                0.3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 87 columns.\n",
      "There are 0 columns that have missing values.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Values, % of Total Values]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(gold_recovery_full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "While using forward fill for missing features is valid in this case, it is not appropriate to fill missing targets: we'd like to be reasonably sure that targets are correct to be able to evaluate the model and trust the metrics\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "I didn't mean that you need to drop all missing values (using forward fill is fine for the features in this case), but ok\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixing data types\n",
    "Using the time_time function I will convert the 'date' column to date_time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_date_time(gold_recovery_train, 'date')\n",
    "convert_date_time(gold_recovery_test, 'date')\n",
    "convert_date_time(gold_recovery_full, 'date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                          datetime64[ns]\n",
       "final.output.concentrate_ag                          float64\n",
       "final.output.concentrate_pb                          float64\n",
       "final.output.concentrate_sol                         float64\n",
       "final.output.concentrate_au                          float64\n",
       "                                                   ...      \n",
       "secondary_cleaner.state.floatbank5_a_level           float64\n",
       "secondary_cleaner.state.floatbank5_b_air             float64\n",
       "secondary_cleaner.state.floatbank5_b_level           float64\n",
       "secondary_cleaner.state.floatbank6_a_air             float64\n",
       "secondary_cleaner.state.floatbank6_a_level           float64\n",
       "Length: 87, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion \n",
    "The original dataset had a significant number of missing values, these have been dropped. There were no duplicate entries found. The date datatype was changed to datetime. The data is now ready for analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Plots\n",
    "First I will plot the distributions of the concentrations of the metals across each stage of the purification process for each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "metals = ['au', 'ag', 'pb']\n",
    "stage_parameter = ['rougher.output.concentrate', 'primary_cleaner.output.concentrate', 'final.output.concentrate']\n",
    "xcolors = ['green', 'orange', 'dodgerblue']\n",
    "\n",
    "def plot_distribution(df):\n",
    "    kwargs = dict(hist_kws={'alpha': 0.6}, kde_kws={'linewidth':2})\n",
    "    for element in metals:\n",
    "        plt.figure(figsize=(10,6), dpi=80)\n",
    "        for features, colour in zip(stage_parameter, xcolors):\n",
    "            sns.distplot(df[features+'_'+element], color=colour, label=features+'_'+element, **kwargs)\n",
    "            plt.axvline(0, c=\"r\")\n",
    "        plt.title('Distribution of ' +element+ ' concentrations across purification stage')\n",
    "        plt.xlabel('Concentration of '+element)\n",
    "        plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TSVxnSazf78fv93d6/1h//rFKklghhBACM7jkrrvuYvr06dx+++3s27ePuXPnMmnSJCZOnMgTT7S0JYwaNYr8/PzA10ceeSQffvghAOvXr2fmzJlMmDCBuXPncvrpp7Nw4cLAsevWreOUU05h3LhxzJ07l6amJgCam5u57bbbmDFjBnl5efzgBz+gvLwcMBXcK664guOPP56JEye2itvn83HLLbcwceJEJk6cyE9+8pPAOdtWfm+++Wbuvvtu8vPzefzxx3nhhRfIy8vjnnvuYfv27fTp04ebb76ZyZMnM2HCBN59912AwD5bTU1NYDDOtddeC8CsWbPIy8tj3759+722S5cu5bjjjmPKlClMnjyZV155BYCvvvqKY445hsmTJzNjxgw+++yzVo931113MW3aNHJzc3njjTcOeL5NmzZx9tlnM336dCZPnsxjjz3W6vv7u9/9jhkzZjB69GieeeYZAO655x52797NBRdcQF5eHvn5+dx9992cd955nHHGGUycOJE9e/Zw8803M336dPLy8jj++OPZsGFDh8+/urqaq6++mhkzZjB58mSuueaawPekPV6vlzPOOIMjjzySCRMmcPHFF1NbWwuYfzby8vICx65evZpRo0Z1eK7eRJJYIYQQwuJ2u1m+fDkPPvggP/nJTxg/fjyrVq3i/fff59577+WLL7444Dl++MMfcs0117BmzRruu+8+Pv7441b78/PzWbJkCevWrWPv3r289NJLADz44IOkpaWxbNky8vPzmTRpEnfccUfgfl9//TWvv/4669evb3W+J598kuXLl/P111+Tn5/Pli1bWLBgQacx5uXlce2113LJJZeQn5/PnXfeCUBlZSWHHXYYK1eu5K9//SsXX3wx1dXVnZ7r8ccfB+CTTz4hPz+fgQMHttpfVlbGnDlzuP/++/n222/Jz89n1qxZNDU1MXfuXO666y5WrlzJww8/zHnnnUdNTU0glsmTJ/P111/z2GOPccMNN3R6Pp/Px0UXXcQf//hHli9fzhdffBF4bWxJSUksW7aMN998k5/+9Kd4vV7uvPNOhg4dyr/+9S/y8/MDCePSpUt59tlnWbt2LcOGDePWW29l+fLl5Ofnc9111/Gzn/2sw+d/0003MWvWLJYtW8a3336L3+/nz3/+c4evodvt5sUXX+Srr75i9erVZGVl8eijj3b6ugsZ2CWEEEIEXHHFFYHtd999l6+//hqAgQMHMnfuXN59912OPvroDu9fVVVFfn4+l112GQCHHXYYxx13XKtjzj33XFJTUwGYMWMGW7ZsAWDx4sVUVlYGktqmpqZWFbfvf//7ZGRk7PeY7777LvPmzSMpKQmAq6++mr/85S/ceuut3X36eDwe5s2bB8DRRx/N0KFDWbFiBSNGjOj2uWxLly5l/PjxzJo1CwCXy0V2djarVq3C5XJxxhlnAHDccccxaNAg8vPzycnJITk5mblz5wIwc+bMwOvU0fnWrl3LmjVruPDCCwOPXV1dzdq1a5k+fToAl1xyCQCHHnooHo+HoqIicnJy2o37rLPOYtCgQYGv33nnHR599FGqq6vx+/2UlZV1+JwXL17M0qVLefjhhwGor6/H7XZ3eLzWmgULFvD666/j9XqprKzkmGOO6eRVFSBJrBBCCBGQnp7e4b7gOW09Hg8+ny/wdWf9kG3nwk1OTg5su93uQI+r1ppHH32U008/vduxdSfOrp4n+Hzdeb4HIzj2pKSkwNdut7vV47dHa012dnarNo+2Onrt2xP8Ou3cuZPrr7+e5cuXM3bsWFauXMnxxx/faSwvvfQS48aN6zRm24svvsj777/PRx99RGZmJo888gjvv/8+0L33Wm8jSawQolfqaDonmY7JOdH22p966qk89dRT3HfffRQXF7No0SL+/e9/A5Cbm8uXX37JtGnTWLZsWaA/MjMzkylTpvD888/zox/9iA0bNvDpp58GKoCdmTNnDgsWLOC4444jNTWVuro6tm3bxoQJEw4Y57PPPsvFF1+My+Xi6aefDiTCubm5LFu2DIDS0lLeeOONQJU4MzOTHTt2tDqX1+vlueeeY968eSxbtozdu3eTl5dHSkoKWmvWrl3L4YcfzrPPPtvqfhkZGVRWVrbqm7Udc8wxbNq0iU8++YRZs2bh9/upqKhg/Pjx+P1+3nnnHU477TQ+//xzioqKyMvLo6SkpMPn29n5MjMzeeaZZ7j88ssB2Lx5M9nZ2WRnZ3f6GmZmZlJZWdnh/srKShISEhgyZAha61a9tu09/zlz5vCHP/yBJ554Ao/HQ3l5OaWlpR0uq1peXk7//v3JzMykurqahQsXBqrfY8aMYceOHRQXFzNgwACee+65Tp9LbyI9sUIIIUQ7HnnkEdatW8ekSZM46aST+NWvfsVRRx0FwL333stf/vIXpkyZwt/+9rdWieazzz7L//t//4+JEydy6623Mn369HaTu7bsY4866igmT57M0Ucf3WFV8aqrruLVV18F4JprrmHq1KlMnTqVvLw8Ro0axc9//vPAvuLiYg477DAuu+yyVq0Q5557bqAH9J577gEgKyuL1atXM2XKFC6//HJefPFFMjIy8Hg8PProo3z3u99l+vTpNDc3t4rnpptu4rTTTgsMbHr11Ve56qqrAOjbty8vv/wyt912G5MnT2bq1Kl89tlnJCYmsmjRIu666y4mT57Mz3/+c/7zn/8csFLc0fk8Hg+vvfYaixYtCgxMu/LKK6mvrz/ga//Tn/6Uq6++OjCwq61JkyZx4YUXMmHCBKZPn75fe0Xb579gwQJSUlLIy8tj8uTJnHLKKWzfvr3Dx7/sssuoq6tj/PjxnHnmmYFWCYChQ4fyi1/8ghkzZnD00UcfMCHvTVR3l/iKFzk5ObqgoMDpMELHXmv4ANOACCEMqcQ6y+fzsXHjRsaNG9dpr2AsqqmpIS0tDaUU27ZtY+bMmSxfvpzhw4c7HVqntm/fTl5eHhUVFU6HInqZzn4fKKUKtdbtNi5LO4EQQggRQp9//jm33HILYP44L1iwIOoTWCFikSSxQgghRAidfvrpHQ7OimajRo2SKmyYzZ49m507d7a6rW/fvnzwwQcORRTbwp7EKqUOAf4O9AcqgXla6zVtjjkZ+D2QDmjgdeA2rbXf2v9d4CHADayyzlF1oH1CCCGEENHC7mMWoRGJgV1PAE9qrccBfwAWtnNMOXCh1vpwYBpwDHAZgFIqHfgrMEdrfQiwG/j1gfYJIYQQQoj4FdYkVik1EDgSeN666SVguFKq1RwTWusVWuut1nYDkA+MsnafCazQWttLlPwvcFEX9gkhhBBCiDgV7krscGCP1toLoM1UCDuBDpf+UEoNBs4HXrNuGgEET2S3HRiilPIcYJ8QQgghhIhTUZXsKaUygSXAA1rrr0J87huBG+2vs7KyQnl6IYQQB2tZ+9OeHbQZMm2aEPEo3JXYXQRVRpVZP24EphrbilIqA3gLeEVr/XDQrp3AyKCvR9FS3e1sXyta64e11jn2pbvL7gkhhBC2V199lRtuuMHpMLrs7rvvDiyAEA8+/PBD3nrrrZCc6+67747ppVz/9Kc/UVRU1OP7x/LzD2sSq7XeB3wDXGrddB5QoLXeHHycNUDrLeAtrfW9bU7zFjBVKXWo9fV1wD+7sE8IIYQIOa/Xy+zZs1mwYEHYHsPv9+OPw8VrvN79akw9Esok9je/+U3MJnHQeRLblfdRLD//SMxOMB+Yr5TaCNwGXA6glHpaKTXbOuZnwAxgrlIq37r8CkBrXQ1cBSxWSm0GcoDfHmifEEII0R1KKe644w6OOOIIxo0bxwsvvNBq31133cX06dO5/fbbWbhwIXPmzAFMQjVx4kR+/OMfM3nyZCZNmsTKlSuZN28ekyZN4qijjqKwsBCAVatWcdxxxzF16lQOP/xw7r23pW5z9913c95553HGGWcwceJEnnvuuVbzzfp8PkaOHMnatWs7fA6VlZVcddVVTJw4kSlTpnDFFVe0e9xDDz3EjBkzmDp1Kt/5znfYscMML3nvvfeYOXMmRxxxBBMmTOCvf/1r4D7z5s1j/vz5nHLKKYwbN465c+fS1NQEQHNzM7fddhszZswgLy+PH/zgB5SXlwfud8UVV3D88cczceLEVnH4fD5uueUWJk6cyMSJE/nJT34SOOe8efP405/+FDj25ptv5u677yY/P5/HH3+cF154IbBk7vbt2+nTpw8333xzYMnZd999FyCwz1ZTU4P5YBiuvfZaAGbNmhVYMratpUuXctxxxzFlyhQmT57MK6+8AsBXX33FMcccw+TJk5kxYwafffZZq8e76667mDZtGrm5ubzxxhsHPN+mTZs4++yzmT59OpMnT+axxx4L3Ecpxe9+9ztmzJjB6NGjeeaZZwC455572L17NxdccEFgydy276M9e/Zw8803M336dPLy8jj++OPZsGFDh8+/urqaq6++mhkzZjB58mSuueaawPekPV6vlzPOOIMjjzySCRMmcPHFF1NbWwuYn428vLzAsatXr2bUqFEdnqvbtNa98jJs2DAdV5QyFyFEl1zz6jXtXkRkeL1evXbtWu31eltu/PKa8Fy6CNB33HGH1lrrLVu26L59++pt27YF9v3mN78JHPvMM8/oc845R2ut9QcffKDdbrf+6quvtNZa33HHHbpfv3563bp1Wmutr7vuOn3zzTdrrbWuqqrSDQ0NWmut6+rqdF5enl66dKnWWuu77rpLDxkyRBcVFQVeo5EjR+r169drrbVetGiRPvnkkzt9DvPmzdM//vGPtc/n01prvW/fvsC5f/azn2mttX7hhRf0VVddFXjtn332WX3WWWdprbUuKysL3F5aWqpHjBihd+3apbXW+kc/+pGeMWOGrq2t1V6vVx9zzDH6xRdf1Fprfd999+l77rknEMc999yjr7vuusD9Jk+erKuqqvaL93//93/1CSecoBsaGnRzc7M+88wz9e9///vA/RYsWBA49qabbtJ33XXXfs9Ha623bdumAf30009rrbVeunSpHjBggK6qqtLbtm3TWVlZgWOrq6u1SX8MQJeXl7f7epaWluqBAwfqjz/+WGuttc/n06WlpbqxsVEPHz5cv/XWW1prrT/55BM9aNAgXV1dHYjlP//5j9Za6zfffFOPGzeu0/N5vV49bdq0wHumtrZWT5o0SS9btiwQ40MPPaS11nrdunU6PT1dNzc3a621HjlypF6xYkUg5rbvI61b3gdaa/2Pf/xDn3HGGR0+/6uvvlr//e9/11pr7ff79ZVXXqkfeOCBdl8f+5iSkpLA9rXXXqvvv/9+rbX52ZgyZUrg2FWrVumRI0fud452fx+0xFegO8jlompglxBCCOGkq666CoAxY8Zw/PHH8/HHHwcqRx1VNQFyc3OZNm0aAEceeSS5ubkceqjpdJsxYwYvv/wyAPX19Vx33XXk5+fjcrnYtWsX+fn5HH300QCcddZZDBo0CAC32811113HX/7yFx555BH+8pe/cP3113ca/2uvvcaXX36Jy2U+aB0wYMB+xyxevJjly5cH4vX5fIF9paWlXHnllWzcuBGPx0NpaSmrV68mJ8csXX/uueeSmpoaeF5btmwJnLOyspKXXnoJgKamplYVt+9///tkZGTsF8u7777LvHnzSEpKAuDqq6/mL3/5C7feemunz7M9Ho+HefPmAXD00UczdOhQVqxYwYgRHU6IdEBLly5l/PjxzJo1CwCXy0V2djarVq3C5XJxxhlnAHDccccxaNAg8vPzycnJITk5mblz5wIwc+bMwOvU0fnWrl3LmjVruPDCCwOPXV1dzdq1a5k+fToAl1xyCQCHHnooHo+HoqKiwPelreD3EcA777zDo48+SnV1NX6/n7Kysg6f8+LFi1m6dCkPP2yGJ9XX1+N2uzs8XmvNggULeP311/F6vVRWVnLMMcd08qqGjiSxQgghRAfsj50BOhsQnJycHNh2u937fW33gv7yl7+kf//+rFixAo/Hw9y5c1v1I7Z9jKuvvprDDz+cyy67jM2bNzN79mwOltaa22+/nWuuuWa/fddeey1nnXUWL730Ekoppk6d2iq+jp6X1ppHH320w+V2uzqYOvj19ng8rRLshoaGLp8n+HztnSccgmNPSkoKfO12u1s9fnu01mRnZ5Ofn9/hMR299u0Jfp127tzJ9ddfz/Llyxk7diwrV67k+OOP7zSWl156iXHjxnUas+3FF1/k/fff56OPPiIzM5NHHnmE999/H2j/exhKkeiJFUIIIWKC3Wu4fft2Pvnkk0DFLFTKy8vJycnB4/GwYcMG3nnnnU6P79u3L+eccw7nnnsu8+fP77QiBjB79mweeuihwGCe4uLi/Y6ZM2cOjz/+eKAa19zczIoVKwLxjRw5EqUUH3/8Md9++22XntecOXNYsGABdXV1ANTV1bFmzZoD3AtOPfVUnn32WZqamvB6vTz99NOBRDg3N5dly5YBpkIc3FeamZlJZWVlq3N5vV6ee+45AJYtW8bu3bvJy8tj8ODBaK0DvcTPPvtsq/tlZGTsdy7bMcccw6ZNm/jkk08AAlXM8ePH4/f7A9+/zz//nKKiolb9n909X2ZmZuD9B7B58+ZOK6advRbBKisrSUhIYMiQIWitW/Xatvf858yZwx/+8IdAklxeXs7mza3G47dSXl5O//79yczMpLq6moULFwb2jRkzhh07dgTeh/b3J1SkEiuEECI6RMF8rj6fjyOOOILa2loeeeSR0A5CAe644w5++MMf8ve//52xY8dy8sknH/A+V199NQsXLuTqq68+4LELFizghhtuYNKkSSQkJDB9+nSeeuqpVsdccskllJaWctJJJwEm+bviiis44ogj+P3vf891113Hb3/7W/Ly8jjqqKO69LxuvfVWGhsbOeqoowIVyFtvvZUJEybsd+xVV13F7NmzmT17Ntdccw1btmxh6tSpAJx44omBqcCuueYazj//fA477DDGjBkTaLkA09bw3HPPkZeXx9y5c7nsssvIyspi9erVTJkyBa/Xy4svvhhoYXj00Uf57ne/S79+/Tj//PNbxXPTTTdx2mmnkZqayttvv80XX3zBq6++ytNPP03fvn15+eWXuemmm6iursblcvHb3/6W733veyxatIif/vSn3HTTTSQnJ/Of//yH9PR0SkpKOnydOjvfa6+9xs9//nMWLFiAz+ejf//+vPjiiwd87X/6059y9dVXk5qa2iqBtE2aNIkLL7yQCRMm0K9fv8CAxI6e/4IFC7jtttvIy8vD5XLh8Xh44IEHyM3N3e/cAJdddhmvvPIK48ePZ8CAAcyaNSswUHDo0KH84he/YMaMGQwaNIgzzzzzgM+nO5Tpme19cnJydEFBgdNhhI7V/0QcTskiRDjMX9L+xPpPfM/5RKo38Pl8bNy4kXHjxh2wuhgpSinKy8tbjWSPBg899BDr1q1rNVOAaG379u3k5eVRUVHhdCiiBzr7faCUKtRat9v8K5VYIYQQIkpNmDABpVTI5kQVIp5IEiuEEEJgBrREm/b6St944w1++ctf7nf77bffzgUXXBCJsKLOqFGjpAobZrNnz2bnztYLrvbt25cPPvjAoYgkiRVCCCFiyllnncVZZ53ldBiil3n11VedDmE/MjuBEEIIIYSIOVKJFUIIIUTMKqjq2nE5meGNQ0SeVGKFEL2SX8tMHkIIEcskiRVC9Cp+7eePn/+Rhd8u5M3Nb9Lka3I6JCGEED0g7QRCiF7l4pcu5l9r/gXArqpdvLbxNc4+5GySPEkORyZufy88573/lK4d98orr3DbbbeRlJTEqlWrqKioCEyW3xOjRo1i8eLFB1zFqau2b9/OW2+9xbXXXnvQ51q4cCFHH300hx56aAgii7zFixczePDgVgsgdEesP39hSCVWCNFrfL7r80ACO6rPKFzKRUl9Cd/u7drSmiK+Pf7449x5553k5+fj8/kOKoENh+3bt/P444+H5FwLFy5k/fr1ITmXExYvXswXX3zR4X57ydSOxPrzF4YksUKIXuOPS/8IwLHDj+X0MaczdbBZ6nJT2Sbpke3lfvrTn/LJJ5/wy1/+kmOOOQalVGDe0VGjRnHnnXcyc+ZMRo8ezb333hu438MPP8z06dPJy8tj+vTpLF26tEuP99xzzzF58mQmT57M2WefTWFhIWCSq+BlQV977TVOPPFEAK699lo2bNhAXl4es2fPDsR2yy23MG3aNHJzc3nwwQcD9x01ahT5+fmBr4888kg+/PBDnn76ab766ituuOEG8vLyeOONN/aLr7CwkPPPP59JkyYxefJkfv3rXwOwb98+5s6dy6RJk5g4cSJPPNGywl1nr1NH56uurubqq69mxowZTJ48mWuuuYamJtPic+KJJ3LzzTcza9Ysxo4dG6hAv/HGG7z66qs8+OCD5OXl8Y+/P83STz7klKMmcPP/XMkZx+Xx1pKXefnfL/K9k4/iO8cdwenHTmHJkiUAHT7/hx56iBkzZjB16lS+853vBJZO7cjNN98c+N4ff/zxbNiwIbAv+P0D0L9/f7Zv397p+UT3STuBECKu2cvLVjZUsmjdIgDSEtIAGNdvHF/t+Yra5lp2V+8mJ7PdlQ1FL/DII4+wcuVKfv7znzNnzhyUUq32V1RUsHTpUkpKShg7diyXX345w4YN44c//CE33ngjAF988QXz5s07YIVv9erV3HLLLXz99dcMGzaM++67j6uuuoo333yz0/s9/vjj/PznP2+VmALs3buXr776itLSUqZOncqxxx7LMccc0+F5rrrqKp5//vnAc23PpZdeyumnn85//vMfAIqLiwH4yU9+wvjx41m0aBH79u1j2rRpTJkyJfCxfkevU0fnu+mmm5g1axZPPfUUWmuuvvpq/vznP3PLLbcAsGXLFj744AOam5s5/PDDWbp0KWeddRazZ88mLy+Pn//85xRUwdJPPmTThnXc+8f/5aG/mOV5y8tKmXP+RSil2LVjO+eedjQ7duxo9/m/+OKLbNiwgaVLl+J2u3nuuee47rrreP311zt8HW+99VYeeughAP75z3/ys5/9TFZWizBJYoUQvcK6knUAZCVlMTJrJADpiekMyxhGYXUhG0o3SBIrOnTxxRcDpqI2ZswYtm3bxrBhw1ixYgX33XcfpaWleDweNmzYQH19PSkpKR2e64MPPuA73/kOw4YNA+C6667jnnvuwefz9Si2K6+8EqUU/fv3Z+7cubz77rudJrEHUlNTw6effsp///vfwG0DBgwA4N133+Xrr78GYODAgYHHs5PY9l6nrKysDs+3ePFili5dysMPPwxAfX09brc7cNwFF1yAx+PB4/GQl5fHli1bmDlzZrtxjxg1hpnHnRD4euvWbTxw7yUU7ykgIcFDWVkZ27Zta7cPdvHixSxfvpxp06YBdOl78c477/Doo49SXV2N3++nrKzsgPcRoSVJrBCiV9hVtQuAQ7IPaVVlG99vPIXVhWyv2I7X33kfnei9kpOTA9tutxuv10tTUxNz587lgw8+YPr06VRVVZGVlUVjY2OnSWxbwe9Hj8fTKoFqaGjodqz2+UJxrq4+lq2916kzWmteeuklxo0b1+7+7pwvLT09sO3X8JMrL+Rnv/o9p33vfBLccOy47A5fA601t99+O9dcc02n8dp27tzJ9ddfz/Llyxk7diwrV67k+OOPbxVruF97IT2xQoheoKaphvKGcgCGZw1vtW9E1ggUCp/2sa92nxPhiRjV0NBAU1MTI0aMAODRRx/t0v1OOukk3nrrLXbv3g2YNoFTTjkFt9tNbm4uK1eupL6+Hq/Xy4svvhi4X2ZmJpWVlfudb+HChQCUlZXx8ssvc8opZjqG3NxcvvzySwCWLVvWqmezo3MBpKenc/zxx/PHP/4xcJv98f+pp57KU089Fbht0aJFnHbaaZ0+387ON2fOHP7whz8EktPy8nI2b97c6fkOFD9ATRNUVZaTM2I0AC//63nKy8vx+du//5w5c3j88ccD1dTm5mZWrFjR4fkrKytJSEhgyJAhaK157LHHWu0Pfu0XLVpEbW3tAZ+T6D6pxAoh4l5BVQEAyZ5k+qf0b7Uv0Z1Idko2pfWl7K3d60R4wtLVqbCiRWZmJvfeey8zZsygf//+XHjhhR0e+/jjj7N7927uueceJk6cyIMPPsh3vvMdAIYPHx5IDI8++mjOOussJk6cyJAhQzj22GMDydDkyZOZMGECEydOZMyYMYG17AcMGMC0adOorKzk+uuvD7QS3HvvvfzoRz/iiSeeYObMmUyYMCEQzzXXXMNNN93EggUL+N3vfkdeXh5nnXVWoN/2ueee4yc/+QkTJkwgISGBc845h9/85jc88sgj/PjHP2bSpElorfnVr37FUUcddcDXqqPzLViwgNtuu428vDxcLhcej4cHHniA3NzcTs/3wx/+kHnz5rF48WIuvuJ/GDWm5fh6L3j98Ivf/pmbrz6fjKw+HHnMyQwZNoLyhvaf/yWXXEJpaSknnXQSYGY3uOKKKzjiiCPaffxJkyZx4YUXMmHCBPr167dfb/GCBQv46U9/yh133MHZZ59Nv379Dvgaie5TWmunY3BETk6OLigocDqM0HFZRXW/jLAWItj8JfN5Z+s7bKvYRm7fXE4effJ+x3y661PWFq9leOZwdt6w04Eoex+fz8fGjRsZN25cqx5I0T2hnos2FgUvO+vTUGklqskeSE0w23XN0OA1t42TfDLqdPb7QClVqLVud8CCVGKFEHHNr/0UVpvpizoauDU4bTBri9eyt3Yvfu3HpaTTSohY1GS1zLpUSwILkOg2SWyDF3ZWmv0dyckMb4widCSJFULEtdL60sDSsh0msemDAWjyNbGueB0TBk5o9zghoo3MPdpas/VhZGKb4r7HBUqB1tDsg6QuZj/33HMPixYt2u/2l156ibFjxx5ktOJgSRIrhIhrxbVmAElGYgapCantHpOemE5aQhq1zbV8tuszSWKFiEFam15YgIR2OlQSXNDkM4luVxeZvvPOO7nzzjtDFqMILfnMTAgR14rrTBI7IHVAp8cNSh8EwNKCrq24JA5O26mZhDhYTVYCqzAJa1t2Ytvcs+l4RQR09/eCJLFCiLhWUlcCQP/U/p0eZ89asGrvqrDHJMwfK6UUzc3NToci4oSdnLZXhQVItDIeTUvFVkSH5ubmwO+E7pB2AiFE3Kpvrqes3sz7ODBtYKfHZqdkA7C2eC0+vw+3S0bMh5NSij59+rB3716GDRsmlVnRY9pKSJu8pqXA4265rS03JoFtagZ3QvvH9HDhNNFDWmv27t1Lnz59JIkVQvRe85fMb/X13tq9aMw0ggeqxNpJbL233kzHld35PJXi4A0cOJAdO3awadMmp0MRMay8wcwuWW/NTJCaYAZxtafZZ/pi3QqSO0hia5Lbv12ET3JyMgMHdl5oaI8ksUKIuGUP6spKyiLRndjpsWkJaSS4Emj2N7N632pJYiPA5XIxevRo/H4/vXXOcnHw7voQVu6F9aXQNwVOG93xsSV18P52sz17nJk3tq3fnBj6GEXHlFK4XD3rbpUkVggRt7o6qAvML9LslGz21u5l1d5VzDl0TpijE7ae/gETAsAH7KiCRj/0TzVfdyQrBfyYGQp218LIrP2PkbU3Yof85hBCxC27H/ZArQS2vil9AVhdvDpsMQkhQqveCxWNZntoRufHuhRkp5jtiobwxiXCT5JYIURc8ms/FQ0VAPRN7tul+2Qnm77Y1fskiRUiVpTVm2t3UILamUxrktiqxvDFJCJDklghRFyqaarBp80Hi32S+3TpPvbgrg0lG2j0yl84IWJBuZXE9knufDlZW5aVxFbKj3jMC3sSq5Q6RCn1uVJqo1JquVJqv6VwlFKjlFIfKqUqlVL5bfZdrpTKD7qUKKUWBd3P12a/rAMnhKC8oRwAj8tDemJ6l+5jV2x92seG0g1hi00IETplVltAV6qw0FKJrW4Ev4wnjGmRqMQ+ATyptR4H/AFY2M4xVcAdwMVtd2itn9Fa59kXoAh4IeiQ6uD9WustIX8GQoiYY7cS9Enu+tyDKQkpgf7ZDSWSxAoRC+xKbN8uTo1lV2I1UN0UlpBEhIQ1iVVKDQSOBJ63bnoJGK6UajV3jda6TGv9KVB7gPMdBQwEXg1DuEKIOFJebyqxXe2HtY3rNw6ATWUyd6kQ0a68HmqtRd+6WolN9rQsS1slg7tiWrgrscOBPVprL4A2EwHuBEb08HxXAs9prYPXKUyz2hS+UUrdqZSSyTGEEK0qsd1hJ7EbSzeGOCIhRKitMbPo4VItFdYDUUr6YuNFzAzsUkqlARcCfw26eQ8wTGs9HTgVmAXc1MH9b1RKFdiXmpqasMcshHCG1jrQE9vdSuwh2YcAksQKEQtW7zPXWUng7kZGIzMUxIdwJ7G7gCFKKQ+AMo1pIzDV2O76PrBGa73WvkFr3ai13mdtlwF/wySy+9FaP6y1zrEv6eldG+ghhIg9dc11NPvNBzY9rcRKO4EQ0W+VlcR2tZXAlmX1z0olNraFNYm1EsxvgEutm84DCrTWm3twuitpXYVFKTVQKZVgbScBc4EVPY9YCBEP7CqsS7nITMrs1n3tSmxJXUmgr1YIEZ3sSmx2Fwd12YIrsbLiceyKRDvBfGC+UmojcBtwOYBS6mml1GxrO1UpVQD8Gzjc+sj/fvsESqnxQB7wrzbnPg5YoZT6FpMsFwH3hfn5CCGiXFVjFQCZSZm4VPd+zeVmt4w7lWqsENGrqhG2V5rtvt2txFpJrE+3DAwTsccT7gfQWm8AZrZz+1VB23VAzgHOsd9iclrrRcCi0EQqhIgXgSQ2sXtVWIC0xDRyMnMoqCpgY+lGZgybEerwhBAhsNYa1KUwCx10R1qCWeHLp01LQXpiyMMTERAzA7uEEKKrgiux3TV/yXz8fj8AC5YuYP6S+SGNTQgRGsGDujzdzGaUksFd8UCSWCFE3Klq6nkSC5CVnAVAZWNlyGISQoTWaqsS291WApsksbFPklghRFzRWh9UJRYgK8lKYhskiRUiWq3u4cwEtsBcsbLgQcySJFYIEVcavA14/V6g50msfb+qpiq0DF0WIuo0+2Bbhdnu08VFDtoKXvBAfsxjkySxQoi4YldhAdITezYftJ3ENvmaaPTJZ41CRJudVeA1reuBtoDusu/X7IcGb2jiEpElSawQIq7YSWxaQhoeV88mYAmu4AYnxUKI6LClzFxnJEJyD+dZCp6RoKbp4GMSkSdJrBAirhzsoC4Aj8tDakKqOZ8ksUJEna3WOiRj+5qZBnrC7YIUKwGWuWJjkySxQoi4YiedGUn7TS3dLfbgLklihYg+m60kdkzfgztPaoK5rpMkNiZJEiuEiCvVjdVAzxY6CGYnwTLNlhDRJ7gSezDSrJYCqcTGJklihRBxJRTtBNCSBEslVojoojVstnpiD7YSm2ZVYiWJjU2SxAoh4obP76OuuQ44+HaCwDRbksQKEVXK6s20WAC52Qd3LmkniG2SxAoh4kZtc21gOz2hZ9Nr2eye2HpvPTVNNQd1LiFE6NitBC4FI7MO7lyBSqzMThCTJIkVQsQNO9l0KVdgdoGeCm5H2Fq+9aDOJYQIHXtQ1/BMSOrh9Fo2O4lt9psFFERskSRWCBE37CQ2LSEN1dN5dyxJniSS3GY29C1lWw46NiFEaGwJ0aAuaGknAOmLjUWSxAoh4oadxPZ0pa627GrslnJJYoWIFltDNL0WQKIbPFYmJEls7JEkVggRN8KVxG4u2xyS8wkhDp69WtfBDuoCs1CCDO6KXZLECiHihlRihYhvjV7YZU0YEop2ApDBXbFMklghRNwIWxIrPbFCRIUdleDTZjsU7QTQksRKJTb2SBIrhIgLWmtqmq0k9iCn17LZSeyOyh00+aRMI4TT7EFdWUnQLyU050yVBQ9iliSxQoi4UFZfhtfvBUJYibVW7fJrPzsqdoTknEKIngse1HWQE5AESCU2dkkSK4SICzsrdwa20xLTQnLO1IRU3MoNSF+sENHArsTmhqiVACA10VzXNYNfh+68IvwkiRVCxAU7iU1yJ5HoTgzJOZVS0hcrRBTZXmGuR/UJ3TntSqwG6qUaG1MkiRVCxAU7iQ1VK4FNptkSInrsrDTXI/uE7pwpQat+SUtBbJEkVggRF3ZV7QLMal2hJNNsCREd6pqhuM5sj8wK3XndrpZEVgZ3xRZJYoUQcaGgqgAIXT+sTZJYIaLDrsqW7REhTGJBBnfFKklihRBxobC6EAhfJXZr+Vb82h/Scwshum6HlcRmJkGf5NCe2x7cJZXY2CJJrBAiLhRWWUlsiCuxWUmm5NPgbWB39e6QnlsI0XV2P2yoq7AQtGqXJLExRZJYIUTM01qHrRKbnpjeMs2WzFAghGPsSuyIzNCfO1XaCWKSJLFCiJhXVl9Gg7cBCH0S61IuRvYZCUhfrBBOCsxMEM5KrCzMF1MkiRVCxDy7CguhbycAyM3OBaQSK4STwtlOYFdim/1SjY0lksQKIWKe3Q/rcXlIcCWE/Pxj+44FYHO5zBUrhBN8fiioNtvhSGKD54rdVxv684vwkCRWCBHzgvthVagWVA9iJ7FSiRXCGXtroclntsORxCZ5wP7NIUls7JAkVggR88I1R6xtbLZViS3bjNayuLoQkWYP6vK4YGhG6M/vUpBsVWP3ShIbM8KexCqlDlFKfa6U2qiUWq6UmtDOMaOUUh8qpSqVUvlt9p2olKpXSuUHXVKC9l+plNqklNqilHpKKRX6zxKFEFEtML1WiAd12eye2MrGSsrqy8LyGEKIjtn9sMMyTCIbDnZLgVRiY0ckKrFPAE9qrccBfwAWtnNMFXAHcHEH59igtc4LutQDKKVGA78FZgG5wCDgmhDHL4SIcuGaXss2pu+YwLbMUCBE5O0I46AuW7JVApMkNnaENYlVSg0EjgSet256CRiulMoNPk5rXaa1/hTo7lvnfOBVrXWRNp/xPQ5cdJBhCyFijJ3EpiakhuX8qQmpDEkfAkhfrBBO2BXG6bVsKdJOEHM8Bz7koAwH9mitvQBaa62U2gmMALozzHesUuobwAc8o7X+X+v2EcCOoOO2W7cJIXoRuyc2PTE9bI8xNnsse2r2SCVWiAi4/b3WX3+6y1yv3rf/vlCRdoLYEwsDu74BcrTWU4FzgWuVUj/o7kmUUjcqpQrsS01NTcgDFUJEXn1zfaBPNVyVWGjpi91UtilsjyGEaJ+9CEFGYvgeI8VqJ5BKbOwIdxK7CxiilPIAKDP3zQhgZ1dPoLWu0lpXWtsFwD8wPbBY5xkZdPiojs6ttX5Ya51jX9LTw1exEUJEzp6aPYHtcPXEAozvNx6ADSUbwvYYQoj9Nfmg0ZpeKz2cSaxUYmNOWJNYrfU+TCX1Uuum84ACrXWXWwmUUkOUUi5rOwP4LrDC2v0SMFspNdhKkK8F/hmq+IUQ0W939W4AFIqUhJQDHN1zdhK7vmS9TLMlRATVBC0FG9Yk1qrEVjVCgzd8jyNCJxLtBPOB+UqpjcBtwOUASqmnlVKzre1UpVQB8G/gcOsj//ut+58HrFJKfQt8AbwDPAOgtd4K3AV8humxLcbMhiCE6CX2VJtK7MC0gbhU+H6lHdr/UMBMs7Wvdl/YHkcI0ZqdxCa5IcEdvseRVbtiT7gHdqG13gDMbOf2q4K264CcDu7/GPBYJ+d/Cnjq4CMVQsQiu51gSMaQsD7O2OyxuJUbn/axoXQDg9IHhfXxhBCGncSGswoLLYsdgOmLDed0XiI0YmFglxBCdMiuxA7NGBrWx0l0Jwbmi11fsj6sjyWEaFHbbK7TwpzEBq/aJZXY2CBJrBAipgUqsenhrcQCjO8vg7uEiLRAEhuB9ThlcFdskSRWCBHTIpnEHtrP9MWuL5VKrBCRYk+vFckkdq/MwhkTJIkVQsQ0e3aCcPfEglRihYg0rSNcibWXnq0L/2OJgydJrBAiptk9sRGpxFozFGyr2EajtzHsjydEb9fsB6/fbIe7JxakEhtrJIkVQsSsJl8TpfWlQIQqsdZcsX7tZ3NZd1bOFkL0RG3QHLFSiRVtSRIrhIhZRTVFge1IVGL7p/YnOyUbkBkKhIgEu5UgwRXeOWJtMrArtkgSK4SIWXYrAcDg9MFhfzylVMvys6XSFytEuEVqei2bncRWNMiqXbFAklghRMyyZybol9KPJE9SRB7THtwllVghwq8ugoO6oKWdAKBYWgqiniSxQoiYFRjUFYF+WJs9zZZUYoUIv0hOrwVmsQNlbcvgrugnSawQImYFpteKQD+sLXiaLa11xB5XiN7IbidIjVAS61LQL8VsS19s9JMkVggRswILHUSyEmtNs1XZWMne2r0Re1wheqNI98QCDEwz13sliY16ksQKIWJWJFfrso3pOwa3MsOkZdEDIcLH528ZXBWpdgJoSWKLJYmNepLECiFiViQXOrAluhMZmz0WkMFdQoSTXYUFZ5JYaSeIfpLECiFill2JHZoxNKKPK9NsCRF+9swELmUGXEXKoHRzLe0E0S+CbwshhAgdn9/Hvtp9QPh7Yucvmd/q6x0VOwCpxAoRTvbMBKkJoFTnx4bSwFRzLVNsRT+pxAohYtK+2n34tVlUPZLtBAB9kvsAUokVIpxqIzxHrG2A9MTGDElihRAxyW4lgMjOTgAtSey28m00eBsi+thC9BaOJbFWJba0Hrz+yD626B5JYoUQMcmeIzYzKZPUhNSIPradxGo0m0o3RfSxhegtnJheC1oGdmlMIiuilySxQoiY5MTMBLZkTzLJnmRA+mKFCJe6CK/WZesf9D+xtBREN0lihRAxyYmFDoLZ1dh1JesceXwh4pnWUOfAHLFgBpKlW9VfmWYrusnsBEKImDN/yXw+2fkJALsqd+03e0Ak9EnqQ1FNkVRihQiDei/4rVWdI7XkbLABqVDTJDMURDupxAohYlJds/nrkpaQ5sjj25VYSWKFCL3ghQ6cSmJB2gminSSxQoiYZCexkR7UZQueZsue6ksIERr2HLEpHnA7kKkEptmSSmxUkyRWCBGToiWJrWuuo6CqwJEYhIhXdQ5Nr2UbKElsTJAkVggRc7TWjiex6YnpJLmTAGkpECLU7HaC1AhPr2WTdoLYIAO7hBAxp8HbgMaM+nAqiXUpF2kJaTT6GvnNh7/hpbUvBfY98b0nHIlJiHhR69D0WrYBsvRsTJBKrBAi5thVWHAuiYWWloKKhgrHYhAiHjm1WpdNemJjgySxQoiYU+c1f1ncyk2Cy6G/ckgSK0Q4aO3cal02uxJb09TSnyuijySxQoiYE5heKzENpZRjcQSS2MYKx2IQIt5UNYLXmvDD6UosQIlUY6OWJLFCiJgTGNTlca6VAFrPUNDka3I0FiHiRUF1y7ZTSWy/FLD/PZZVu6KXJLFCiJjj9MwEtqykrMC2tBQIERq7rSQ2wQUJbmdi8LhMIgvSFxvNJIkVQsScaEliE9wJgRXDJIkVIjQKqsy1U/2wtsDgLqnERq2wJ7FKqUOUUp8rpTYqpZYrpSa0c8wopdSHSqlKpVR+m30nK6WWKaXWKqXWKKUeUEq5gu7nU0rlB13Ghvs5CSGcFS1JLMjgLiFCrdCqxDrVSmCTabaiXyQqsU8AT2qtxwF/ABa2c0wVcAdwcTv7yoELtdaHA9OAY4DLgvZXa63zgi5bQhq9ECLqRFMS2ze5LyBJrBChUmhXYp1OYmWaragX1iRWKTUQOBJ43rrpJWC4Uio3+DitdZnW+lNgv6K91nqF1nqrtd0A5AOjwhi2ECKKRcNqXcGkEitEaNk9sY63E8iqXVEv3JXY4cAerbUXQGutgZ3AiJ6cTCk1GDgfeC3o5jSrTeEbpdSdSimH2sCFEJFQ2ViJT/uA6Ehis5LN4K7Kxkr82u9wNELEPrudINXpSqy0E0S9mBnYpZTKBJYAD2itv7Ju3gMM01pPB04FZgE3dXD/G5VSBfalpqYmInELIUJrT/WewHZUJLHWDAUaTU2T/F4R4mA0eFuSxqhpJ5BKbNQKdxK7CxiilPIAKDMr+QhMNbbLlFIZwFvAK1rrh+3btdaNWut91nYZ8DdMIrsfrfXDWusc+5Kent6jJySEcNaeGpPEupSLJHeSw9FAWkIabusDoMqGSoejESK27Y6COWJtwZVYrZ2NRbQvrEmslWB+A1xq3XQeUKC13tzVcyil0jEJ7Fta63vb7BuolEqwtpOAucCKUMQuhIhOdiU2NSHV0dW6bEopMpMyAdNSIIToOTuJdSlI9jgbi12JbfZDZaOzsYj2RaKdYD4wXym1EbgNuBxAKfW0Umq2tZ2qlCoA/g0cbn3kf791/58BM4C5QdNo/cradxywQin1LSZZLgLui8BzEkI4ZHf1bsD51bqC2S0FVY1VDkciRGyz54hNTQCn/0cdGPQrRlbtik5h/z9Ha70BmNnO7VcFbdcBOR3c/z46SEy11ouARaGJVAgRC+x2gmjoh7VJJVaI0IiWOWIBMpMg0Q1NPpPEjuvndESiLYeL9UII0T3RmMQGz1AgYsiy+Qc+ZsYT4Y9DBOyOoiRWKdMXW1gtMxREq5iZnUAIIaB1T2y0sNsJqhurZZotIQ5CQZTMEWuTabaimySxQoiYEpWV2KBptqobqw9wtBCiI9GyWpdtoEyzFdUkiRVCxJTAwK4oSmJTE1JbptmSlgIhesTnhz3WVMvRksRKJTa6SRIrhIgZVY1VgQUF0hLSHI6mhVJKZigQ4iAV14HX6saJmnYCqcRGNRnYJYSIGYVVhYHttMToSWIBMpMzKWsok0psrNAamiqhYQ80lkFSNqSNBHeK05H1Wvb0WgpIiZLsRCqx0a3LbxOl1BfAI8C/tdbN4QtJCCHaV1htkthoWa0rWGaimWZLemJjQPm38PmlULm6zQ4FqcNg4Angia5/knoDe3qtgWngjpLPiQOVWElio1J33iZ3Aj8AtiulfquUGhammIQQol12P2xaQlpUrNYVzJ4rtrpJktiopTVsehz+e1RLAutOgZQccCUCGuoKoGAxNJU7GWmvZA/qGpbpbBzB7EpsWT00+5yNReyvy5VYrfXbwNtKqRHAtcBypdRnwJ+01p+FK0AhhLDZ7QTRNKjLlpGUAZieWK111CXZvU57c8AWf96SvCb0gUEnQVJ/MyGo9kPtTtj3IXhroeBVGHJGJCPu9exKbE6Gs3EEGxBUkC+th8HpzsUi9teTgn1fYBDgB/YAjymlHgtpVEII0Q67nSCaBnXZ7HYCn/ZRVFPkcDRiPxWrWxLYjENg+LmQPKBlbVPlgvRRMGy2aSXwN8Ket6F+r2Mh9zZ2Ejs0mpLYoP+XZXBX9OlyEquUutCqvD4PfAEcorX+KXAkcHaY4hNCiIBAEhtlg7oA0hNbSjTbKrY5GInYT+1OKFlqttNzYeCJ4OpgDqekbBh2DriSwd8Ay681bQgi7OwkdlgUJbHJHsi0ZkrYJ32xUac7ldhLgLu01pO01k9presBtNY+4KdhiU4IIYLY7QTRWIl1u9yBuLaWb3U4GhHQXAVF7wEakgfBwONbqq8dSUiHgceZ7YLFsP35cEfZ62kdnT2xINNsRbPuJLEva63fDb5BKXUFgNZ6SUijEkKIdkTjQgfB7MFdksRGCa1h38egm8GTDoNPB1cXh4KkjzFVW4CvfmIGfImwqWqEWmveo2jqiQWZZiuadSeJvb6d2/4nVIEIIURnfP6WXtNorMQCZCSav76SxEaJqg1Qb/7xMdNmdXMO2AHHQMoQaK6E1b8NfXwioCBoUo9o6okFmWYrmh3wX1Kl1AxgJjBAKRXcNpAFRNdEjUKIuLW3di8+bea4icaeWGipxEpPbBTw1kLpF2Y7c7yZ/7W73Mkw+bfw5VWw9RmY8CtIGxHaOAXQ0kqQmQQZUZZZBCqx0k4QdbpSiR0C5AGpwBFBl/7AvHAFJoQQwYJX64rWdgJ7mi2pxEaB4s/B32Tmge13dM/PM/oySBsF/mZYc3/IwhOtReP0WjZpJ4heB6zEaq1fAV5RSp2ptX4zAjEJIcR+7JkJslOy8XS1rzHC7Gm2CqsKafA2kOxJdjiiXqp4KdRa1fABx8LBrO721fWQOhxqt8PmJ81AsYR2Jgud8UTPH0O0zEwQZYO6QAZ2RbMDVmKVUidYmwlKqdltL2GOTwghgJZK7NCMoQ5H0jG7EqvR7KjY4XA0vZTWkH+r2U4eBGmjD/6cmePMwDD8UJ5/8OcT+7HbCaKtHxakEhvNulLOuBT4CLihnX0aeDWkEQkhRDvsmQmGZUTvitcpnhTcyo1P+9hZuZPx/cc7HVLvs/t1KP7EbPc76sDTaXWFckPfI8x5q9ZDvyNNv6wImWicI9ZmV2Jrm6G2CdISnY1HtOhKO8HV1vVJ4Q9HCCHaZ7cTRHMSq5QiPTGdysZKdlbudDqc3sfvg/zbzHbaSEgZHLpzZ46D0mVmJa+qjdB3cujOLQKV2JwoaCe4/b3WX9c3t2zf+m7rgWf3nxKZmET7urNi1/eUUpnW9s1Kqf8opSaELzQhhGgRSGIzozeJhZaVuySJdcCOF6FyjVlCtt+M0J5buc0sBwBV62QVrxBq8EJJvdmOxnaCJA/Y9fx6r6OhiDa6M0/sfVrrKqXUFEyLwTvA4+EJSwghWrN7YqO5Egst03/trJIkNqK0H9b+3myPuhQS+4b+MTIPNdfNldBQFPrz91K7g+aIjcZ2ApcyiSyYhFtEj+4ksfa37nTgSa31E0B0TtYohIg7diU2mgd2AaQnSCXWEYVLoHItoODw28LzGIl9IHmI2a5cF57H6IXsVoIkN/SPztnzSLaSWKnERpfuJLFupdRRwHnAB9ZtCaEPSQghWqtpqqGq0fylk3YCsR+tW+ZwzZkDWYeF77Hsc9dsBV9D+B6nF7FX6xqaYaqe0ShFKrFRqTuTLd4BPAG8p7Vep5QaD2wMT1hCCNHCnpkAor+dwE5id1Xuwq/9uFR3agWiS5bNb/113W4o/dL6Qu+/P5TSRoEryQzwqt4IfWSA18HaHZTERitJYqNTl3+7aq2XaK3ztNY3WV9v0FqfF77QhBDCsPthE1wJDEgb4HA0nbOT2EZfI8W1xQ5H00vYc7emDIPkgeF9LJcHMg4x29VbwvtYvUQ0T69lk3aC6NTlSqxSyoNpJRgbfD+t9T1hiEsIIQLsftghGUOivrJpJ7FgWgoGpQ9yMJpeoKkc6gvMdt8pkXnMjFyoXA2NxdBUCYlZkXncOBVN02t1xE5iG5o7P05EVnf+GvwT+AnQH8gIugghRFjFwmpdNo/Lw4BUUy2WvtgIqFhjrhP7mkpsJCQNAI/1569GqrEHKxYqsSnWCCCpxEaX7vTETgIO1VomxxNCRFYsrNYVbETWCIrriiWJDTdfk+lLBciaEJrVubpCKcgYa9oYarZA9tTIPG4c8vlhT43Zjuae2OSgnlitI/dWE53rThK7C0gEGsMUixBCtCsWVusKVlpXCsDf8v/G+pL1ADzxvSecDCk+VW8A7QVXYkufaqSkW0lsUzk0lkX2sePIvlrw+s12NLcT2AO7NNDoa0lqhbO6823YDHyolHoZCMwrorV+JORRCSFEkFhZrctm98XWNNU4HEkc09qaFxbIGAeuCM/4mJgNCX2guUJaCg6CPb2WAgand3qoo4KT1gavJLHRojvfhiRgPRA8AZ+0Fgghwi6WemKhJYmtbap1OJI4VldgVs4C00oQaXZLQdnXJomVz5h7xJ5ea1A6JLqdjaUzCS5wK/BpmWYrmnQ5idVaXx7OQIQQoj1+7WdPzR4gdtoJpBIbAVXWilmpw52bHSDdSmKbq6B8hfTG9oA9M0E098OC+f8k2QO1zVAvMxREjS7PTqCUylJKPaaUWmJ9fbhS6qIu3O8QpdTnSqmNSqnlSqn9/mVWSo1SSn2olKpUSuW3s/9KpdQmpdQWpdRTSqmEruwTQsS+fbX78PpN6SPW2gnqvfWB2EUIeeugdofZzgzj6lwHktjHzIoAUPCKc3HEMHtmgpwoT2KhpS9WZiiIHt2ZYusJoAgYbX29Dbi1i/d7Ums9DvgDsLCdY6owK4Jd3HaHUmo08FtgFpALDAKuOdA+IUR82FW5K7A9PHO4g5F0XfBcsdJSEAbVmwAN7hRIG+FsLGmjzLUksT1SYFVio3l6LZtMsxV9upPEjtNa3ws0A2it6zG92B1SSg0EjgSet256CRiulMoNPk5rXaa1/hRo77f9+cCrWusia3qvx4GLurBPCBEH7GmqBqQOICUhxeFouibFkxJYlEFaCkJMa6gyMz6QcQg4vfiFncRWfAs12xwNJRbtjoHptWyBSqy0E0SN7gzsagr+QimVwgGSWGA4sEdr7QXQWmul1E5gBGa2g64YAewI+nq7dduB9gkhYtz8JfNZuXclABrN/CXzHY6oa5RSpCemU9VYRU2zJLEhVfxZy4CuzEOdjQUgqT+408BXCwWvwqE/czqimKF1S0/ssCieXssmldjo051/YT9QSt0BJCulTgX+AywKT1ihp5S6USlVYF9qauQPixCxwE4C0xOieP6ddtjxSiU2xLb+1VwnDzI9qU5TCtJHmu2CxY6GEmsqG81AKYitntg6qcRGje5UYn8N3ILpX70PeAXT49qZXcAQpZRHa+1VSilMpbQ7y9jsBMYGfT0q6P6d7WtFa/0w8LD9dU5OjkwPJkQMsJPA4D7TWCAzFIRBczXs+D+zHQ1VWFvaKDNnbfEn0FgKSf2cjihq3f5ey3ZZfcv2U99AQhRPsQWQGlSJlRnVokOXKrFKqenAC8ClQDpmUNc7WmtfZ/fTWu8DvrHuB3AeUKC17morAZg+2tlKqcFWEnwt8M8u7BNCxAF7YJQksYJdi8BXB8oD6WOcjqZFyhBIyATtg8LXnY4mZtgVzQRX9Cew0FKJ9Wto9jsbizAOmMQqpWYCbwNbgV9hZhHYCvxXKXVUFx5jPjBfKbURuA243Drv00qp2dZ2qlKqAPg3cLj1kf/9AFrrrcBdwGeYPtpizIwHne4TQsQHqcSKgO0vmOv00ZFfoaszyg1DzzbbhTJLQVfZrQRpic7G0VUpQW85aSmIDl1pJ/gFcIXW+uWg215WSn0J3A7M6ezOWusNwMx2br8qaLsOyOnkHE8BT3V3nxAitnn9Xuq95jPHWE5izeQp4qDU7Ya91mfRGYc4G0t7hn0XdvwD9rwDviZwx0hm5qBaa7h4WhT9P9KZ4FW76r3Qx+mARJfaCSa0SWAB0Fq/Ahwe+pCEEMIInmM1VpNYn/bR4G1wOJo4sOOfoP2QPBhSonD54cGnAwq81VDyudPRxIRAJTZGklilZJqtaNOVJLauk30yi7cQImzsj+Ldyk2KJzbmiLUFJ90yzVYIbLemGx95kfNzw7YnuT/0m2G2d7/pbCwxItbaCUCm2Yo2XflNkKSUmqSUmtz2AiSHO0AhRO9lJ39piWmoGBsK7HF5SPaYX5E1jZLEHpSKNVC+wmyPvrTzY5009CxzvUeS2K6oi7F2ApBKbLTpSk9sCvBqB/uk0UsIETaxOqjLlpaQRoO3gdpm+dDqoNgDujIPg75HOBtLZ4aeCavugopVUFcAqR0O9ej1vH5osOY3So2lJFYqsVHlgEms1npUBOIQQoj9BJLYGFvowJaWkEZpfakksQdDa9MPCzDq4uienDN7GiQNgMZi2P0W5F514Pv0UsGj+2OqnUAWPIgqUdhYJIQQRnVTNRDDldjENKD1ADXRTWVfQ+02sz3iAmdjORDlgiFnmG1pKeiU3Q/rUpAcA3PE2qQSG10kiRVCRK3qRpPEZibFwMLq7UhLsJJYqcT23E5rha6+R0BmFE6t1dbQM8110bvgl3JdR4Kn14rm4npbqVYltsFatUs4qzvLzgohRMR4/d5AO0FGYgwsrN4OWfDgIGndksSO+IGzsRzIsvnm2mdNp9ZcBZ/MbT0d2AxZi8cWa9Nr2exKrF9DY6drlopIkEqsECIq7archbbGjmYkxWYSG1yJlQUPeqB0OdTuMNsjvu9sLF3lToakgWa7dpezsUQxu6c0lgZ1QUtPLMgMBdFAklghRFTaVmH6IF3KRWpCqsPR9IzdE+vXfkrqShyOJgbZVdjsaZAx1tlYuiNtuLmukyS2I4F2ghga1AWQ4AaPlTlJX6zzpJ1ACBGVtpWbJDY9MR1XNE5u3wV2JRagoKqAAWkDHIwmBtgfyYM1K8GLZlu5W++LdqnDzYC0pjLw1oIn7cD36WVqrCpmeoxVYsFUY6ubpBIbDWLzL4MQIu7ZldhY7YcFSHAnkOg2pabC6kKHo4kxjcUmAQRIH+NsLN2VNABc1lpAUo3dj1+3JICxVomFlr7YOqnEOk6SWCFEVIqHJBZaqrEFVQUORxJjaqxptZL6Q0KMzU6hVMtCB9IXu5+65paVkmJtYBcErdolSazjJIkVQkQlu50gVgd12ey+WEliu0FrqN1uttNGORlJz9l9sfUFoP3OxhJl7H5YRUtVM5bYg9GkncB5ksQKIaKSVGJ7saZyaK4027GaxKZaSay/GRqKnI0lytQETa/liqE5Ym3JUomNGpLECiGiTn1zPUU15g9/rC50YJMktgfsKmxCFiT2dTSUHnMnm95YkJaCNmJ1ZgJboJ1AKrGOkyRWCBF1tldsD2zHeiXWXvBAkthuCG4liKXlnNpKlam22mMvdJAeo0ms3U7Q4AWfdIo4SpJYIUTU2Vq+FQCPy0OyJ9nhaA6OPcdtQVWBLHjQFc010GjNqZs+ytFQDprdF9tUBt46Z2OJIjVBS87GIrsSq4HSekdD6fUkiRVCRJ3NZZsByErKQsVyJY6WSmxtcy1VjVUORxMD7CqsO7Vl5atYlTQAXFa5sV6mWLPVxvD0WtB6MFqRrCjtKElihRBRZ1PZJiD2+2Fh/wUPxAHUWlNrxXorAYByQcows10n33swH7/XBw3sikUeFyS6zfYeSWIdJUmsECLqBFdiY12iOxGPy3z+KEnsAfgaod4ayR/rrQQ2e77YugIzdVgvV+9tmSM2VntioaUvVpJYZ0kSK4SIOnYSGw+VWKWUzFDQVXW7AA0qAVKGOB1NaKRalVhfPVSscjaWKGD3w7pUS29pLAoksdXOxtHbSRIrhIgqzb7mwOwE8VCJBVnwoMtqd5rr1GGg3M7GEioJmWaqMICit52NJQrY/bCpCbHdLZJqJeC7JYl1lCSxQoiosr1iOz7tA+KjEgsyV2yX+L0tU1GljXQ2llCzq7F7JIm154hNj9F+WJu0E0QHSWKFEFHFHtSVmpAamJ4q1gWS2GpJYjtU8jn4G8126ghnYwm1FKsvtvgT8PbuOZlqYnyhA5sksdFBklghRFSx+2Fzs3Njfnotm91OUFgl0yx1qHCJuU4aCJ4UZ2MJtdShgAJfAxR/6nQ0jqqN8ZkJbHb8e2vAL+P1HCNJrBAiqmwqNZXY3OxchyMJHWkn6ILC18x1WpxVYcHMFZs8yGz38r7YWF9y1mbPFdvsh2JZx8IxksQKIaLK5nJTiT0k+xCHIwkde8GD8oZyaptqHY4mClVvhqr1Zjve+mFt9lRbvbgvtskHdV6zHcvTa0FLOwHIDAVOkiRWCBFV4rkSC1BYLS0F+yl83Vx70iAx29lYwsVOYitWQv0eZ2NxSHCyF+vtBB4XJMmCB46TJFYIETUavA1sqzArNo3vN97haEIn2ZNMotuUnqSloB17/muuU4fH9rxLnUnqDwl9zHbRu46G4pRd1qrLsT5HrE3minWeJLFCiKixuWwzfu0H4LABhzkcTegopRiWYaZZkiS2DV8D7PvQbKcOdzSUsFIuGHyq2e6lLQV2EpsW43PE2uwkdrdUYh0jSawQImqsLzF9kf1S+tE/tb/D0YTWsExJYttV/KlZzUq5IWWY09GE15DTzXXRO71yCdqCoCQ2HthJbJEksY6RJFYIETXsJPbQ/oc6HEno5WSankiZZqsNu5Wg/9HgjvHRPgcy+DRz3bC3Vy5Bu7PSXMf6oC5boBIr7QSOCXsSq5Q6RCn1uVJqo1JquVJqQgfHXamU2qSU2qKUekoplWDdfrlSKj/oUqKUWmTtG6WU8rXZPzbcz0kIER5xncRmmCRWFjxow05iB5/hbByRkD4KMqxZN3rhVFs74jSJlZ5Y50SiEvsE8KTWehzwB2Bh2wOUUqOB3wKzgFxgEHANgNb6Ga11nn0BioAXgu5eHbxfa70lrM9GCBE2cZ3EWpVYaScIUre7pSI5pBcksQCDrZaCXtgXu73CXGckORpGyNhJ7N5a8PmdjaW3CmsSq5QaCBwJPG/d9BIwXCnVdu6c84FXtdZFWmsNPA5c1M75jgIGAq+GL2ohhBO01pLE9jZ2NTIxG7KnORtLpNh9sfs+7lVL0FY2QKW1qnC8VGLt3l6flgUPnBLuSuxwYI/W2gtgJag7gbZLsowAdgR9vb2dYwCuBJ7TWjcH3ZZmtSl8o5S6UynlDln0QoiIKawupLbZLAQQz0nsvtp9NHobHY4mSgRaCU4DVy/51T3oRFAe8DdC8SdORxMxdisBQHqcDOwKniZM+mKdETMDu5RSacCFwF+Dbt4DDNNaTwdOxbQj3NTB/W9UShXYl5oaGU4oRDSxq7CJ7kRG9RnlbDBhYCexALurdzsYSZTw+8wofeg9rQQACZnQf6bZ7kUtBXYSm+yBhDj5f8Xtgv4pZlsWPHBGuKcb3gUMUUp5tNZepZTCVFh3tjluJxA8IGtUO8d8H1ijtV5r36C1bgT2WdtlSqm/ARcDD7QNRGv9MPCw/XVOTk7vm99EiChmJ7G52bl4XHEwE3obg9IH4VIu/NpPQVUBo/uOdjokZ5V/A42lZtv+iD3eLZtvrn1WG8HWZ8DbpoQ344nIxhQhOyrMdby0EtiGZEBJvVRinRLWSqzWeh/wDXCpddN5QIHWenObQ18CZiulBluJ7rXAP9sccyWtq7AopQYGzWKQBMwFVoT2WQghImHNvjUATBjQ7gQmMc/j8jAkfQggS88CLa0EWRMhNc7nh23LXoK2qQy8vaOZcnuczUxgG5phrqUS64xItBPMB+YrpTYCtwGXAyilnlZKzQbQWm8F7gI+AzYDxZhZDbCOHQ/kAf9qc+7jgBVKqW8xyXIRcF84n4wQIjzWFMd3EgsyuKsVO4ntLVXYYEn9wWVlc3W9471gzxGbEWdJ7JB0cy3TbDkj7J/Zaa03ADPbuf2qNl8/BTzVyTky2rl9EbAoNJEKIZyitWZZ4TIAPt35KfOXzHc4ovDIyczhy8Ive2cSuyzoe+prguLPzHbV+tb7egPlMquT1W6D+gLIHOd0RGEXb3PE2gJJrFRiHREzA7uEEPGrqKaIRp8ZsZ+dku1wNOEjlVhL/W5Am6Vmkwc7HY0z7JaCusK4X4K2wduyNGvcVWKt8pr0xDpDklghhONW71sNgEu5yEzKdDia8JEk1lK3y1ynDIE4HMTXJXYS66s3vbFxbGfw9FpxlsQOtSqx+2qhyedsLL2RJLFCCMfZ/bB9kvvgUvH7a0mSWEzV0e4DTc3p/Nh4lpABCVlmO877YncGtRIkxcn0WrYc639uTUu1WURO/P61EELEDLsS2ze5r8ORhNewDDMKf0/NHrx+r8PROKS5qmVaqdThzsbitEBLQXwnsfZysyOyQClHQwm5gWmQYGVSu6qcjaU3kiRWCOE4uxIbz/2w0FKJ9Ws/e2v2OhyNQ+xWAk8aJPRxNBTH2UlsQxHE8T819qCuUVnOxhEObhcMs/pid1V2fqwIPUlihRCO0loH5oiN90rs0Iyhge1e21JQb82Rm5ITf2W57koZAijQPqjf43Q0YWMnsSPjMImFlpaCAhncFXGSxAohHLWjcgfVTea3f7xXYpM8SQxMGwj00iRW+1uStd62wEF7XImQPMhs18fv+8HuiR3Zx9Ewwma4lZxLJTbyJIkVQjjq26JvAUhwJZCRuN900HGnVw/uaiwBf5PZThna+bG9RfBUW3HI64cCq1d0RLxWYq1fWwXSExtxksQKIRz17V6TxGanZKN6wcfLvTqJrd9trhP7gifV2ViiRZwvQbu7Gpr9Zjte2wnsSqy0E0SeJLFCCEfZSWy/lH4ORxIZORlWElvdC5NYu9qYIq0EAUn9wZVktuNwlgK7lSDR3bK6Vbyxe2L31kBj/I7Pi0qSxAohHGW3E8R7P6xtWKZJ4HpdJdbvNaPwQVoJgilXS39wHCaxW8vN9fBMM5I/HgXPFSsrd0VWnL6lhBCxoLqxmi3lWwDol9pLKrFWO0FhVXz2QHaoYZ8ZhY+yRuWLgBSrpaC+0Ax+iyNbrCQ2N47/Rx2Y2rKIg8wVG1mSxAohHLNq3yoAFIrs5Dj+KxckkMRWF+KPs4SlU/bUWkn9wZ3kbCzRxq7E+uqhYqWzsYTYZmtF3dw4nj1PqaBptiSJjShJYoUQjrFbCXKzc0lwJzgcTWTYSWyTr4mSuhKHo4kge1CX9MPuL3gJ2j1vOxtLiPWGSixIEusUSWKFEI6xB3VNGTzF4Ugix156FnpRX2xztWknAEiVfth22bMUxFESW9MEe2rM9tg4rsSC6fkFaSeINElihRCOyS/KB2DKoN6TxKYlpgVWJus1Sey+jzHDXlyQPNjpaKKTncQWfxo3U23Zg7oAxkgSK8LA43QAQojeyef3BXpipwyawq7KXQ5HFF7zl8wPbCvMfLgPfPYAs8fPdiqkyCl6z1ynDAKX/Nlpl70Erb8R9n0CQ89wOqKDttlKYgenQ0act0FLO4EzpBIrhHDElvIt1DWbilNvaicAU40FqG2qdTiSCNn7vrmWftiOuRJbqtRF8dFSYA/qivdWAmhJYovroEHmio0YSWKFEI6wB3X1Se7D8MzhDkcTWWkJVhLb3AuS2IZiqDDfa0liD8CepSBO+mK39KIk1m4nAKnGRpIksUIIRwQGdQ2a0iuWmw1mV2JrmmocjiQC9n5grlUCJA9wNpZoZ/fFVq6Gut3OxhICm3vJzAQA2SmQYnXKSF9s5EgSK4RwRHAS29ukJ5r1N3tHEmu3Egwxq1OJjiX1h0SrbFn0jrOxHKRmH+yoMNu9oRKrFAy3ZkmTSmzkyG8UIYQj7HaCvMF5zgbiADuJrW2ujf8FD+xBXTK11oEpFww+1Wzv+a+zsRykXVXQbL21D+kFlViAnAxzLUls5EgSK4SIuLL6MnZVmdkIetugLoCMRPPXzq/9FNUUORxNGNXuhJrNZlv6YbtmiDUrwZ7/gt/nbCwHwR7UlZ4IA9OcjSVS7EqstBNEjiSxQoiIW7nXLK3pVm4OH3C4w9FEnj2wC2BHxQ4HIwkzu5UgqT8k9pJy3MEaepa5biqD0i+cjeUg2Ct1je1rPmrvDewZCnZWOhtHbyIT9gkhImr+kvmBJDYrKYufvfkzhyOKPLfLTWpCKnXNdeyo3MHM4TOdDik87FaCQSf3nkzmYKUMgexpUPY1FL4OA451OqIesSuxvWFQl22UVYndUQFay1s+EqQSK4SIuJK6EgD6p/Z3OBLn2H2xOyt3OhxJmGgNRe+a7UEnOxtLrBl6trne/bqzcRyE4EpsbzGqj7muaoKyekdD6TUkiRVCRJwksZCeYJLYuG0nqFwLDVa/75DTnI0l1thJbMVKqI29ley07p2V2BFZYBdft1U4GUnvIUmsECKimn3NVDRWAL08ibUqsTsq4zSJtaeISh9jLqLr+h0JyQPN9u43nI2lB/bVQnWT2e5NldhkDwyzZijYXuFoKL2GJLFCiIgqrS8NbPdL6edgJM6K+3YCu5XAnjJKdJ1ytQzwKnzN2Vh6YK35oIUkN4zMcjaWSLNbCqQSGxmSxAohIspuJeiT1IcEd4LD0TjHnmYrLiux/mbY96HZliS2Z+yWgr3vgTe2GixX7zPXh/aHBLezsUSancRKJTYyJIkVQkSU9MMadiW2qrGKioYKZ4MJtZIvwFsLKBnU1VNDTgflAV99y9K9MWJNsbme0AtXGR7dx1xLJTYyJIkVQkSUJLGGncRCHLYU2K0E2VMhqfe2jByUhEwYdJLZLljsaCjdZSexEwc6G4cT7CR2e4UZ4CbCS+aJFUJETH1zPeUNZu6d3p7EJnmSSHAl0OxvZkfFDiYPmux0SKEj/bChkTPHDJArfAX8/w9c0f/ZfGVjy2T/vaESe/t7rb+uajTXtc1ww38hxeqYuv+UyMbVW4S9EquUOkQp9blSaqNSarlSakIHx12plNqklNqilHpKKZVg3X6iUqpeKZUfdEk50P2EENFn1b5VaEx5ojcP6rJlJJm+2G0V2xyOJISaKqH0S7M9WKbWOig555jrhn0xs3rXWqsK61amJ7a3SUtomWbLnqFBhE8k2gmeAJ7UWo8D/gAsbHuAUmo08FtgFpALDAKuCTpkg9Y6L+hS38X7CSGiyDd7vgEgMymTJE+Sw9E4LzPRrFO5rTyOkth9H4H2gTs5Zlebihqpw6DfDLMdIy0Fa6xBXbnZZsqp3sbtMoksQHWjs7H0BmFNYpVSA4Ejgeetm14Chiulctscej7wqta6SGutgceBi7rwED29nxDCAXYS2z+lF5Zo2mFXYrdWbHU4khCyWwkGHGcSWXFwcuaY610vx0STZW8e1GXLsP4/l0ps+IW7Ejsc2KO19gJYieZOYESb40YAwfPMbG9zzFil1DdWO8J13bifECKKfL3na0D6YW32NFtxVYm1FzmQVoLQyDnXXNdsgco1zsbSBat78aAuW0aiuZYkNvxiodj/DZCjta5USuUAbyilSrTW/9edkyilbgRutL/OyuplMzAL4bAmXxOr9q4CJIm1ZSaZdoKt5VvRWqOUOsA9otSy+ebaWwNV6812yZctt4ueyzoUMsdD1QZTje0z0emI9mMPbvL6YZO1lslnu1qWnu1tApVYaScIu3BXYncBQ5RSHgBlfkOPwFRjg+0ERgZ9Pco+RmtdpbWutLYLgH9gemA7vV9bWuuHtdY59iU9Pb29w4QQYbJm3xqa/c2AJLE2uxJb21wbmHosptXtNteuJJlaK5TsloKCRY6GcSAVDWA3PPTtxZ0kwZXYGOgAiWlhrcRqrfcppb4BLsUM6DoPKNBab25z6EvAp0qpu4G9wLXAPwGUUkOAvVprv1IqA/gu8NcD3U8IEV3sftj0xHSSPb34L1wQuycWzAwFA9JivJGwrsBcpw6DWK0qO6296nWDNVqqPB8+vRCOi84/c+UN5jo9ERKjfzawsLGTWJ+Gei+kypxJYROJ2QnmA/OVUhuB24DLAZRSTyulZgNorbcCdwGfAZuBYsysBmAS31VKqW+BL4B3gGe6cD8hRBRZvns5IIO6gnlcHganDwZMS0FM0xrqC812yjBnY4k3SQPAY/3DU7PF2Vg6UW6tjtubq7AAaYlB02xJS0FYhb0nVmu9AZjZzu1Xtfn6KeCpdo57DHisk/O3ez8hRHSxk9iYrzaG2Ji+YyiqKYr9wV1N5WaJVDCVWBE6SkH6GKj4Fmqi95+dMqsSm53S+XHxzqVMNbq6yVwGOR1QHJNlZ4UQYdfgbWDl3pUADEiVJDbY6D6jgThY8KDeaiVIyDQXEVrpY811UxlUrnc2lnb4/KYnFqQSCy2Du6qkEhtWksQKIcLu26Jv8fq9gCSxbY3pOwaIg3aCOmklCKukfi3/HOz8l7OxtKOkDvzafIw+INXpaJyXafXFVsk0W2ElSawQIuzsVoJDsg+RlbraiItKrN8L9XvMtrQShIdSLdXYHf+KumHve2vNdXYKJPTiQV22LKsaXdngbBzxTpJYIUTY2Uns9GHTHY4k+ozNNonJjoodNPuaHY6mh+r3gPYCClJynI4mftlJbNU6qFztbCxt7LOS2IFpzsYRLbKs/9Vrm6HZ52ws8UySWCFE2C0vtJLYoZLEtnVI9iEA+LQvdquxddb03CmDwZ3obCzxLLEvJPQx29v/4Wgowbx+KLHG9A2SJBZoSWJB+mLDSZJYIURYVTdWs77EDESRJHZ/g9MHk55oFl/ZVLrJ4Wh6QGuotZLYVFn1O6yUgoxcs739edB+Z+OxlEo/7H4S3C3zw1ZKEhs2ksQKIcLq6z1fo9G4lZsjhhzhdDhRRylFbrZJTDaWbnQ4mh6o2gDearOdJkls2GWYyj11u2DfR87GYpF+2PbZ1VhJYsNHklghRFjZrQQTBk4gNUHKNO2xWwo2lcVgJXb36+bak9HyUbcIn4QMGHiC2d72nLOxWOwkVloJWutjDe6qkMFdYSNJrBAirAKDuqSVoEOxncS+Ya7ThstSs5Ey+jJzvfPf4K1zNJT6Zii1+mFlUFdrUokNP0lihRBhJUnsgR3Sz0piY60ntrkK9n1stqUfNnJGnA/uZPDWQMFiR0P5pkj6YTtiJ7F1zVAj88WGhSSxQoiwKa4tZnvFdkCm1+qMXYndWbmTBm8Mffa45x0ztZZyQ8pQp6PpPRIyIedcs+1wS8EX1kJt0g+7v8ygGQo2lTkXRzyTJFYIETZf7f4KgCR3EpMGTnI4muhlV2I1OrZW7rKrgCnDwOVxNJReZ/QPzXXR2y2rpTng4x3mWvph95fghjRrhoKNpc7GEq8kiRVChI3dSpA3OI8Ed4LD0USvAakDyEwyS4rGTEuBrwkKl5jt9NHOxtIbDT7N/POg/bD1GUdC2FUF+XvN9rBMR0KIenZLgSSx4SFJrBAiLOYvmc/T3zwNQE1TDfOXzGf+kvkORxWdlFKBloKYmWZr7wfQXGlaCdJGOh1N7+PywJjLzfaWvzoyZ+zr1ls1NQH6p0T84WOCvfystBOEhySxQoiw0Fqzr3YfAIPSBjkcTfQ7tP+hAIGFIaJewSJzPfBEM8hIRN7YK8x17XYoei/iD7/E+tBgRKZMTNERqcSGlySxQoiwqGiooNFn5pYZnD7Y4Wii32H9DwNgbclahyPpAr+vpR92+FxHQ+nV0kebtgKALU9H9KG3V8Bq8z8qI7Mi+tAxxa7E7qmBaplqK+SkE18IERZ7a02zXFpCGmkJMurjQA4fcDgA64rXobVGRXNpq+RzaLAymJw5UPGto+H0OsuC2nLsNoKd/4Gll4Hb+lx/xhNhDWGJ1UowPNPMTCDal9VmhoKpQ5yLJR5JJVYIERZ2EjsobVB0J2RRwk5iKxsr2VOzx+FoDmCX1UrQfyakytRajkofBa5kwA9VkRsU+JqVxH73EGkl6IzH1TJDwQZpKQg5SWKFEGGxt8ZKYtOlH7YrxmaPJcFl/tqtLY7ilgKtW5JYaSVwnnJD5jizXbXWfH/CbFMZrLcSsu+OC/vDxby+VqXabr8QoSPtBEKIkCutK6WisQKQQV0HEjxjQ3piOuUN5dz1wV2cOuZUB6PqRMlSqNtptiWJjQ6Zh0HFSrOCWt0uSAvv6mmLrbGHo/vAhAFhfai4kJ0MBVWSxIaDVGKFECH3RcEXALiVm34p/RyOJnb0Te4LQHlDucORdGL78+a639GQPsbZWISRmAWpw8125ZqwPlR9M7ywymyfd5i0EnSF3TO8rgSafc7GEm8kiRVChNznuz4HzCT+bpesRdlVfZL7AGZmh6jkb4ad/2e2R13ibCyitayJ5rpuFzRVhu1hXloH5Q2Q7IFLZRG+LrGT2EafzBcbatJOEEe01lzbzmTyT3wvvKNUhWjr8wKTxEo/bPf0TYnySuye/0JjqenDHHmB09GIYKk5kJBpWgoqw9NT7dfw9Aqz/f3DW3o9ReeSPTAk3UyztWofHC4tGCEjlVghREg1+5pZVrgMkH7Y7rLbCRq8DYGFIqLKNquVYMgZkCx/iaOKUpA1wWxXbwBvbcgf4t2tsK0CFHDVESE/fVybNNBcS19saEkSK4QIqZV7V1LXXAdIEttdWUlZuJT5tbxy70qHo2mjuQoKXzHb0koQnTLGgfKAv6nlH44QevIbc336WBjVJ+Snj2sTrSR2lSSxISVJrBAipOx+2MykTFIS5PPG7nC73IFqbH5RvrPBtLXrZfA1gCcNcs5xOhrRHncSZBxitjc+FtLptr7eA8t3m+2rp4bstL2GXYldVwJev7OxxBNJYoUQIRXoh5UqbI/Yszl8uzfKVsHa+jdznXOuSWRFdLJbCipXw76PQ3JKreEPn5ntaUPgSFl1qtvsSmyDFzbL4K6QkSRWCBEyWms+3mH+cEoS2zPZKdlAlFViK9e1JES5Vzsbi+hcUjakWKuobXwsJKd8bxt8WWi2bztWptXqiYFpMMj6309aCkJHZicQQoTM5rLN7K42nzkOzZDlSHuif2p/ANaXrKfB20CyJ9nhiIDNT5rrzMNgwCxnYxEHljUB6ndDwctQuwvShvfoNLe/Z2YkeGOz+TonA15eby6i+yYNhL3bTBL7/cOdjiY+SBIrhAiZD7d/CMCQ9CFkJWU5G0yMsiuxXr+XtcVrmTrE4QbEL66E7S+Y7cQ+sPxaR8MRXZA20ix+ULcLNj8OU+7r8am2lkNVo5mRYMrg0IXYG00aCO9uk0psKEk7gRAiZD7Y/gEAJ446ESWfOfZIsieZtATzueO3RVHQF1uzFfyNZm5Ye9CQiG7KBYf82GxvfgK8dT06TbMPVloJ19hsyEoKUXy9lD24a20x+GRwV0hIEiuECAmtdaASe9Kok5wNJsZF1eCuqnXmOn0MuKOgtUF0Te414E4xi1Ns+3uPTrG62AxE8rhgkkwLfNBkcFfoSRIrhAiJjaUb2VOzBzCVWNFz/VJNEvvV7q+cDaQ8Hxr2mu3MwxwNRXRTUj8Yc4XZXr8A/L5u3X1TGawvMdsTB0BKQojj64UGpcPQdLNtT1cmDk7Yk1il1CFKqc+VUhuVUsuVUhM6OO5KpdQmpdQWpdRTSqkE6/aTlVLLlFJrlVJrlFIPKGVmA1dKjVJK+ZRS+UGXseF+TkKI/b2/7X0AhmUMIzc71+FoYtvAVFOy+WbPNzT7mp0LZN1D5jqpHyTLbBMx59AbAAXVm6BwSZfvpjXc9SFoIDMJxvcLV4C9z4xh5nqZJLEhEYlK7BPAk1rrccAfgIVtD1BKjQZ+C8wCcoFBwDXW7nLgQq314cA04BjgsqC7V2ut84IuW8L2TIQQHXp769sAnDrmVOmHPUgD00wSW++tZ03xGmeCqN0JO/5ptvtMkXmVYlHGWBh+rtle/8cu3+31TfDZLrN95BBwy2e2IWMnsV8WhnQtil4rrG9NpdRA4EjAXv/uJWC4UqptmeZ84FWtdZHWWgOPAxcBaK1XaK23WtsNQD4wKpxxCyG6p9nXzHtb3wPgjLFnOBxN7EtJSGFUn1EAfFnwpTNBrP8TaB940k0/rIhNh95sros/hZIvDnh4TRP89hOzPSITBqeHMbZeyE5ii2pgV5WzscSDcP9/NRzYo7X2AlgJ6k5gRJvjRgA7gr7e3s4xKKUGYxLe14JuTrPaFL5RSt2plHKHMH4hRBd8UfAF1U3VKBSnjT3N6XDiwoxhMwD4stCBJLapArY8Zbb7TDKj3UVsGjAT+h9jttf+/oCH/3GpSbDSEmCqrMwVcrl9IdtajVv6Yg9ezPxmUkplAkuAB7TW9miHPcAwrfV04FRMO8JNHdz/RqVUgX2pqamJSNxC9Ab/3fJfAKYNnRaYrF8cnKOGHQXAssJlkX/wTY+DtwYS+kDmoZF/fBFaE2431wWvQMXqDg9buRcWWhNi3DwTUmUwV8gpBdOtdWDsVdBEz4U7id0FDFFKeQCUaZQbganGBtsJjAz6elTwMUqpDOAt4BWt9cP27VrrRq31Pmu7DPgbJpHdj9b6Ya11jn1JT5fPSIQIlSe/Nis6+fw+5i+Zz/wl8x2OKPbZSeza4rVUNUbwc8fm6pb+yUN+DC7JZGLe0LNNRR1g7R/aPcTrh9usFbomDYQfTYlgfL3MUVZLwXJJYg9aWJNYK8H8BrjUuuk8oEBrvbnNoS8Bs5VSg61E91rgnwBKqXRMAvuW1vre4DsppQYGzWKQBMwFVoTr+Qgh9ldUU0RxXTEAOZk5DkcTP6YOmYrH5UGjWV64PHIPvOHP0FgCngw49MbIPa4IH6Xg8NvM9o5/QM22/Q55Jh/WFINLwf2nyGCucLL7YrdWwL5aR0OJeZFYdnY+sFAp9UugCrgcQCn1NGYw16ta661KqbuAz6z7fIiZ1QDgZ8AMTO/rXOu2f2ut7wOOA+5RSvms5/I+0PP19YQQ3bZkg5m6J8mdxKA0mYYpVFISUpg6ZCrLCpfx8Y6POWXMKeF/0Kbylmm1Dr0BkqU1JGYta/NpiPabf0y81fDh2TDQ+tByxhNsK4eHlpovL5/SsrKUCI/D+0N6ohlEt3w3nC0L4fVY2JNYrfUGYGY7t1/V5uungKfaOe4+OkhMtdaLgEWhiVQI0ROvbHgFgBFZI3DJAKCQOmHkCSwrXMZHOz6KzAOufRCaKyGxr1Rh441yQd8pZpaCqg2QfQR40vH54eZ3zCpSI7Lg5mOcDjT+uV0wbQh8tAOWFUoSezDkL44Qosdqmmp4d+u7AIzKGuVsMHHohJEnAGb2hwZvQ3gfrH6vaSUAOPxWSMwK7+OJyMscD540wA9l+YBpI/jKLLTHQ6fJYK5Isftil0lf7EGJRDuBECJOvb3lbRp9jbiVW/phw+C4EcfhUi4afY18WfAlJ4w6IXwP9u3t4KszK3ONuz58jyOco9zQdyoUf4K3aiN3lD3E/1WYXeP6weL15iLCz+6LXVcCJXXQP9XZeGKVVGKFED320rqXALPUbIJbSjihlpWcxRGDjwDgw+0fhu+BSr6Arc+Y7bzfW9U6EZcyx1HGYHzaxRfVh+DTpj8zT9rZIypvkFnSVwMfbHc6mtglSawQokdqm2p5Zb3phx3TV1Z0Che7peDDHR+G5wH8PvjKqrz2OxpGX9b58SK2KTfvcxm/q32Qbd5cXGiOyQGPZAMRleCGk0aZ7Xe2OhpKTJN2AiFEjyzZuITa5lqSPcmBJVJF6J00+iQe/uJhPt/1OTVNNaQnhniO6y1PQ9nXgILpf5HVuXqBVxov4eP6yQD8MOsfNKde7HBE8e/29/a/raTOXL+3DX7xjhnwdX8EJiGJJ/LbSgjRI/9Y/Q8AvjfueyS6Ex2OJn6dNOokktxJNPmaeH/b+6E9eV0h5Fvzh+bOh+ypoT2/iDo7G/qztPowAE5MfIO7Ey4lpzmC8xCLgCHpoDALTeyV+WJ7RJJYIUS3ldWX8eamNwG4aOJFDkcTn+yVz278740MSBsAwBub3gjdA2gNX14FzRWQPBim3HvAu4jYVuVN5or1/0OzTiDF1ciNGb/DpTRnVd9k3g8iohLdMNBqPy+odjaWWCXtBEKIbnv222dp9jfTJ7kPZx5yJm9tfsvpkOLaiMwRFFQV8MamN9BaYxY2PEjvngDFn5jtvlPg218e/DlF1PJqF9dvuppN9UNx4WdW1lqWui5kCp8xuvkTDm98hbXJc5wOs9cZlmGqsIVVoIc4HU3skUqsEKJbtNY8+fWTAPxoyo9I9iQ7HFH8G545HIBdVbtYU7zm4E9Ys9XMSACQMR7SRh78OUVUu3f7+XxUMRGAozM30D+hmh1qMquTzEKYZ9b8ArdudDLEXikn01zXe6E8zFNBxyNJYoUQ3fLpzk9ZV7IOgPnT5h/gaBEKWclZZCWZxQde3fDqwZ3MWw+fnA+6GTzpMGC/BRVFnPnfwjN4psiMGPpZzmuMSi4O7Hsr/Q94SaC/bxMn1D7gVIi9VnoiZCWZ7UJpKeg2SWKFEN3y+NePAzBrxCwOG3CYw9H0HvYMEP+35v96fhKt4avroHwFoGDQyeCSQXnx7NmiE/jDTlNtndP/S36e81qr/aWeXD5KM4P7Tqy9j37eTRGPsbcblmGud1VKa3J3SRIrhOiyi/9zMf9YZWYlSHAlBAYfifAb23csAN/u/Zb1JT1cVmnzk7B1odnuPxNSBocmOBGVXio+ml9vM9Nnnd43n4fGLsSl9s+SPkz7JSXuXBJoZE71jyWTirCR1grPFY2wcq+zscQaSWKFEF22ct9KNJqMxAxZ4CDC+qX0Izc7F4B/rf5X90+w5+2WRQ1GXQpZE0IYnYg2/9h7LDdt/hEAx2Wt5dFxT5Hg8rd7rFclszjDfMKS2/QeRzQ8F7E4BfRNgewUs/2PELS89yaSxMY4rTW/fv/X+LVGA69vep1GrzTni9Arri0OVAAnD5qMSybFjyilFBdMuACAf635F7o71bLS5fDJXNBeyD4SZjwBoZjhQESlv+45hdu2XobGxVEZG3lq/P8j2eXt9D5bkk7hm+QfAjC7+if09W2LRKjCktvXXL+6AWqbnI0llsgUWzHuoc8f4t5P7uU31teF1YW8vvl1zs49myRPkqOxifjyu09+h0/7SPYkM77feKfD6ZUunHgh931yH+tK1rF893JmDJtx4DtVbYQPzwJvLWQcAie+Dp7U8AcrIur2LZegNayqHcnqOjPbxJDEMkYm7+O327/fpXMsyfgzY5o+oI+/gB9UXspTfT/CryRNiISRWfBNEdQ2w6sb4aKJTkcUG6SUEsPe2/oet757K2BW/QBwKRcldSV8Wfilc4GJuLO5bDN/Wf4XAKYOnorHJX/YnDBx4MRA4vrEV08c+A4Va8x8sI0lZkGDk/4LyQPDHKVwgk8rPq86NJDADk8q5visNXhU+y0E7Wlw9eX/sp7Hj2JU8+ecVCsLYERKgrulN/afq52NJZZIEhujtNbc8s4taDTHjTgOlEIBxw4/FoANpRsory93NkgRN25991aa/c1kJmVyWH+ZkcBJ9rRm/1zzTyobKjs+sOwbeO8EaCiCpP4mgU0fHaEoRSQVN2XwXvlkdjSaf1DGpRRybOY63O0M4trPvo9bXbZVKD7iEgBOrv0th+x9KJyhiyBjrZaC/L2wrrjzY4UhSWyMem3ja6woWgHAI995JFCJHd9vPH2T+6LRLN8t62GLg7do3SIWrVsEwFFDj8LtcjscUe92wYQLyErKoq65judWdjAA54Oz4O2jobEU3Kkw6CTY9BdYNr/lIuLCpxWHcubKX1PizUKhmZa+mSMztuA6iJbnd7mc7UzEhZ+L+A0DvOtCF7DoUL8UOLSf2f77SmdjiRWSxMYgrTX3fHwPAOeMP4cjhhwR2PfDxk+5ZYDpd9teuZ2jK96WP1yix4pri7n2tWsBmD1+dmCuUuGctMQ0LptyGQB/+uJPeP1BA3a0hnUPw563wG8tZjDse5DY16FoRbg0+d08sHMOl677GcXNWSSqZk7IWs341N0HfW6/8vACv6WCgaRQw48qvkeqvzQEUYvOKAXz8sz2v9fCjgono4kNksTGoI93fMxXu78C4M4T7txv/zHp6eQmmUFdL5VJS4HomWZfMxcvupjiumKyU7J54rtPoGREu6PseXmLa4tRKLaUb+GM584wO5sq4PNLYMVNgIbkQZAzBxKzHIxYhMPyqrGcvfJX/KXwzMAMBGdlf83QpND9vq9R2fyd+2kkhX6+LVxW8T0S/bKkVLidfxiMygKvH/74hdPRRD9JYmOQPcDmxFEnMnXI1P32K6U4r5+pvLxTWUVxY31E4xOxT2vNDf+9gXe3vgvA0997msHpMjF+tMhMygzMGbuiaAX+ovfgjcmwwyxEQcZ4GPZdmYUgzuxryuT2LZdw/ppfsLF+GB7l44acJfxjwsOkukM/L1ORyuVf/Bo/LkY2L2VexVkk+mtC/jiiRYIbbrJWgn51g/TGHogksTFmd/VuXl7/MgD/M/1/OjzulMxM+rjdNGnNk7ukn0l0ndaam9++OfDP0j0n3sO5h53rcFSirbxBeQx2w8NZ5bjePxXqdoEnA47+Oww8HpT0LseLSm8KD+w8h+NX3MuL+44HYFrGZt6YfC8/H/5a1wZw9dA6dSz/l/kcflyMbv6UH1V8VxLZMPvuODh8AGjgwaVORxPdZJ6cGPPk10/i9XsZmjGUc8af0+FxSS4Xs/v24dmSUv5351puGTOFRBmQIw6gvrmeH7/+Y/7+7d8BuOqIq7jj+Dscjkq0laybuNi1jb+NUqS7TALj638s7mOeh/RRUPKZswGKkKj3JXDhmhtZWzecJp0AQLKrkclpOxibXMTCPSdFJI5vUy7GhY/zq37EmOaPuKb8eP7e5zWq3UMj8vi9jUvBL46Bea/Ae9vgiwI4OsfpqKKTJLExpNnXzJNfPwmYaXYS3AmdHn9O3z68WFLK7sY6/lO0jYuH5kYiTBFj5i8xA/4qGip4f9v7lNSXAHD99Ov585l/lj7YKJLpr+OE5rWc1LSaNJrABbu9cGsJjB50Ivekj3I6RBECDb4E/lV8LI8VnMm+5j4AJKhmJqTuYlzq7m7N/RoqK1J+iMbFeVWXM8y7guvKjmZh3zfY65FZ+cPhxJFw9DD4ohBuegfeuhgyZP2i/UgSG0MWr1/Mnpo9eFwerp569QGP75+QwElZmbxTWcWftq/ioiFjkXREtOXz+1i5byXf7PkGn/ahUPz5O3/m+hnXSwIbBZT2M863h1nN65jq3YYbU3mtJ4H3EifxYI3m4+oVeD7/A3MOn9tun7yIDTW+JJ4vOoGn95xKcbMZkOfGx/jUQg5PLSDxAEvHhlt+yiVUuYdwacVc+vh38eOyo1mc8QT5KZc4Glc8Ugp+fwqc9Q8oqII7P4QFZzgdVfRR3Vp/O47k5OTogoICp8PolhMXnshHOz7iggkX8M/z/9l6p8uF1ppP/u/QVjevq6/n2m07APj86HOYecbiCEUrop3Wmtc2vsalL19KVWMVAFlJWZw48kQGpQ9yOLpeTmtG+/cxvXkL07xb6aPrAruacLPb1ZdCV198yk2T389NhSWsrC7j0LQ+fH3sXFLdUp+IJRXNqSwsOpm/7TmZSl8aAEmqiYsGfUplcyopYRi0dTAG6u1cxu30w0zntYzv8trAf9GsZCDhwbr/lNZf/2sN/MKMr+WR78A5vXDFb6VUoda63YYK+U0XI1btXcVHOz4C4Lrp13X5foelpHBMn0F8XrGXP+9YxcxwBShiyjd7vuGX7/2S/275L2CWK548cDJTh8iSsk5R2s+19W/TT9cwwF9NMs2BfRqoVKnscfWhRGWggyrkiS4Xz085memfv8z62gouX/kh/8g7BZdU0aPW7VtM5bLSm8LG+qFsaxiEV5ufO4/yMi5lN+NTC2nye6IugQXYp0bxmH6SuTzIJD5iBq8xtnQyL2X+lW2JJzgdXlz5weHwwXZ4czPc8T4c1h/G9XM6qughf61ixO8+/R0AUwZNYdaIWS07Goqh+DNQGhQM95VQp5KoVUk0qEQAfjZqIp/n7+U/RdsoqCogJ1M6xHur9SXr+fUHv+Y/a/8TuG1k1kiOHnY0Wckyn2ikJesmJnh3Mdm7g4m+XaTrxlb7q1QyxSqTYlcGTarjHvhJGdn8+bCZXLvmU/6vaCvDN6Tz4PijpB0kClV6U9lcP5jtDQMD/a4AiaqZ8amFjE/Z7XjbQFc0qAxe1L9hJi9zJv+Pfr4tXFN+Il+k/Ji30++l3pXtdIhxwW4ryC+CPTVw6cvwn+/DCPl1DUg7gdNhdMmGkg0c9pfD0Gj+df6/+MFh50HhK7B1Iex+E7QXfmQd/PeW+1WTxB53X0YPP50xn7xEQUMttx17G/efer8TT0M4xOf38ebmN/nf5f/LW5vfQls9ldOGTOP+U+5vldCK8ErRjeT6ihjn3cMhvj2M9JfgovXv4CqVTKlKp9iVGfhH9ECOH2mmXbpl/Rc8tM2sV/k/Iw7nkcOPlYpsFNheP4APKybwYcVEPq08jGbdUj/KctdySMpuRifvI8HlczDKnuuvdzE34XFGN38KQK3qxzvpv2V5ytX4ldTKQqGiwcxU0OiDtAQ4bQz8+TtORxUZnbUTSBIbA360+Ec8++2zHNp/PKvPeQD3yjugYlXLAZ4MuLgaDVQ/l0yqbsJD0OhVVyIf+gdy1uYCPAkZbLh+A0MyhkT8eYjIqWyo5MvCL/nv5v/y0rqX2FG5I7DvsP6Hce/J93LuoeeilArMTiBCK1k3McxXxnB/CTn+Mkb6isnxl+43OXcjHtZ6cljpGcE4726ae/BH305i/Vozf/UnPF2wHoAz+ufwzKQTGZIsvYqR0uD3sKpmJCtqRrOiegzf1IyhqKn1sr8JysvwpBJGJ+9lYEIl8fB/hhpwHDPqn+D0mjtI1WUAFLvH8V7aXaxMvgAt8xYftLJ6k8g2+yE9EV7+Qe9oLZAkth2xksR+uvNTZj0zi4mJ8M5hhzG4zl64QJklJcfMgyHfgYTkloFdWpOuGxiiKxngrwoktHt98JtSqBtxEQvnvujUUxIhVN1YzaayTWwq3cSmsk1sLN3IV7u/Yn3J+kDFFUChGJ41nAn9J5CTmSMfM4eIS/vpr6sZ4K9koL+KgX7zMzfYX8EA3f4SnX4UVSqZSpVKhUqlSqWg1cGtO2MnsWAG7P1601fct2UFANkJSdydO435Iw6TuaJDrKQ5g/V1w9hQN4z1tUN5t3wKFd40/O2sI5TqamRIYhlDk8oYmlgW1gUKnJSqKzmVZ5jBq7gxleV9jOAzvs+KgffI4K+DtK/W9Mj6tKnI/vF0ODPOZ8+UJLYdsZDENnobOfHJSVzMJq7LAreddwz7Hkz5HfQJmp+vg9kJ3NrHsRnppnKrTZ/VliaoP+xWJk6/F2QQT9Rp9DZSVFPE7urdgcu+2n3sq91HcV1xq+uKhooOz+NSLgakDmBk1kjG9h1LRlJG5J5ELNOaRLwk6WaSaSZFN5Gl6+jrr6WPrg1cD/BXka1rAlNetXsqoJ5EalQStSqZKpVClUo+6KS1Kz6vruGPRSWUNDcAMCwpjR+POIyLhuYyJjUz7I8f63xaUe1NocSbwe7GbAob+1HYmG1d+rGlYTAlzR29jposdx39E6ron1BN/4QqMt11cVFx7ap+uoBTWMgU3sNlFVLqVF9WpFzGiuRLKfRMo1e9ICFUXg8f74Raa+zn5Xlw49GQGafzyEoS245oT2J1cw2vvHISJ9V/RZZdPOkzCaY9CoP2H/2pXebXxJVP/GO/fUPSh5CuSzmJ5zhSLybRqgA0pwwj4fBfwJjLIUESnJ7yaz97qvdw3evXUd1YTYOvAZ/fh9fvRaOZPX42HpeHBFcCHpcHj8uDS7mobPz/7d19lB11fcfx92fu8959SHaTkCDEaBLTEgsJD2ILQj2V1qdaKjZq6xN6BNpTjy1HRT2t1Oo5hf7hQW0RqFYeWgtVWqCnqEhVfEBQhPAokGgSnhJJwu5mH+7zfPvH/O7du8tuHiF3N/t9nTNnZn4zc+9v5nfn3u/9zW/mN8xzped4rvRcK2h9fPfjVBqVfb/pFIV0gd5cL325PvoL/SwpLmFR16KOPWkgbQ16bZxeK9EXj9NnydBMy1uNtDXIEAarExNRUZqKMpTJJGNlGCe5UbF5w2JJWeqkqCuiQYoaKQyRIiZNg7Q1kjExeauFYLRK3mrJPJPHk6er5Kg/r53qvjQQJbKUlKGsLCVlGQv5jg9DwDqTtctexWc238vlTzxCzSaaGJ3QM8AfH7WC3+1fxikLlhzRj+Qyg9FGnsF6kaF6N4P1IoO1IoP1bobqxdYwWC8yXC8yVO9isNbNnkYB24+e2dNqsDK/gzVdT7OtvJiBzAj96RGyc7R96wttkT3BaXydE7mNLOVW+s7UK3g092Yey76RbdnTqCvfwVzOPZU67CrBHaGl2OIuuOg0OOc3kx6/jiQdDWIlrSa53WgRMAy8z8wenma9DwAfByLgu8BfmFntUJbtzawNYse2ET9+OeOPfpFuKwEwHnXRdeI/wqrzaZDmsd1wzzNwz3b4xS7YNQ43fGotCF7/8Y1kVSavMXqj3fSldrG6J0Mu3O2a7c6wZvvvsaFYIt38oKfycPSbYPkGWHImFPwZoe1Gq6NsH9neqhXdNryNrUNb2TK0hS2DW9g2vI1q48V5DE4hXaAr00UhU6CQLpBP5ymkCxQyyXQxU6Q310s2Nf0NQDILAV2DtMWkiDHAQrcXzWlDoCnzYXmEkbMaOauTp0rO6hSsSq+V6LFSKzDtsTK9Nk5PXEp6kzqClMgwGHUzpC4GVWQoKrIr6mGn+ji99guqpGZ1rdLOWo2bBoe4bWiYZ+uT73xPSZzQM8D63gGO617I2u6FHNe9kGPyxRes2YkZjMc5Rhs5qnGGqqWpxSlqlqZqaRoWEZuIw2fPwnRsyd+JGGFt07FFxIhqnGa43sVwo4vhepE99S6G611JoBqC0sF6N3U7tGYUKRp0pSoUowrFVJmuVIWeVIkF6TF6UqUjtmnACylvI5zU/Tjry9fxkvp9k5bVybA9vZ4nM69iV3oNu1Mr2Z1axVDqpTT28+bG+eizr4WvboTL7oaR8JX70j74s99KHs21sNDR7L1gOh3Efhe41syulvQ24CIzO2XKOi8DfgycCPwauBn4tpn988Eu21e+Zk0QWy/B8EOw43bsmVth550oXHoZjeF/s3/A4vU38sDuIj/fDvftmPiwtvvOJWsBOOvjz/t/AEBvapyBzB4W9S9F/IKHtryK84qjfLAPuqdWNhRfBgvXQc9KKK6AbD9keiHTF8Y9EOUgykwMSodx52qdZmJmjNXGGCoPtYbB0uCk+VZ6eXL6rvFdlKojdEXQJVrjQtt0e1pvOktPOkM+EnlBXpDFyMqQQYM8NXLU4zyNqIDRRVqQjYxsBFlBLjJyqZgu1SioREY1ohBaRmEQRjQlQG1Od1mlbb3Oq5GiSoqq0lRJU1WaBkkQEhMRKwlYRNLGNE0SbEfErf3KtGpYk3ntpY6sFeggGkTJoGhieob5+l6Wx4owgwZp6pbUH9ctQ705tkxrWZ0MDWsuz1K3TNibVAjKwt5ZalJ6a68tHdpUGjmVyalETuNkVSYXlVrz7csyqlKzLBUrULUcVStQtTwl62Y07mMs7mMs7mU07uOZWpFnahG7ajAaC0iD0ogIsypGBaNChjq9aWNBBvozsCgTsTib4qhsxOJsUiqlOEslzlJqZCnHGcbiHCP1AqONAiONPCONAnvC/HTtRDshrQZZ1chFNXKqk41q5KI6WdXJRTWyk9KScU612fwfZe5YkrTdXlJ/mOPKN7Gm+k2W137Sam4wVUzEULSc0dRSxrSIsWgxY1EyHm+ONUA56qOsXsrqo6ruWf2H8oXU7Bhh5xhceid84xFa15CyKThpGbxmObz6GHhF/9zttrZjQaykJcBmoN/M6kr+1m8HTjezzW3rfRRYaWYXhPk3Ap80s9MPdtm+8nbYglgz+OWXoT4KtVGoj0D5WR5+rsBjwz2MlUcpxQXGrZtxKzJuRZ5qLOKB2grGUsdRbkx/mf+YXjh5GaxbCsu64cy1RTBjwxe+RdXyjMV9DMcDjMQLMZ5fC5FLxdTiX1KuPsYxqac5LjvKb2TG6YvGKGiMAuOk1GjVikzUzjVr6DRpaO0uglZQImJFSEmwkrQDTNaPlYRZCqdc8jmMMYsBA4uTIRxDQxDH4f3bQ7oQCBGFAEEhQArLLGotb193xmXWDP0iIkVERMl+0L7uxPpJ4JCnQo6K5ak2x5ajQhiH9DozP+dzb1LUyVEmr1JryFEmpXorV5NyqCnzzeWaJq0t/XnbTFk/Obap5FI+aSpkkE1Oa5BqDfUwbh6zyYFaMz0J4JLwNAqfsIm8SNY2H6an5Le5bxOfxuY6zU9D8hlrkKJhIdS3ZoODFHVLArLYUiEQTbeC1EZ7oIrXCL1YmmXXnAZC2bcvh9Y1BNH6BsiGADTbCkAnAtKcamSjieA0F9W81nSWydsIx/Iox/IIR/M4/TzDAE+T5cCbVQGhWVIPZfVRiZLAtqw+KuqlpjwNZWmQo64sDbI0lEzHZCaqCTRRZUD4pml94jS5SmHik6jWMnh+EL07tZJnMicdyqHap5EKbB6EXw5CdZrWLEcVk+fLLupKhu4M5NKQS0E+nUxnU+FXNuxGc2+kiXEhDa97+Yu6K5N0sseuY4HtZskdRWZmkp4AlpMEt03LgW1t81tD2qEsmx0kuOdDEE8+IW8evZQrSx/b+7bhQ5hLwXGLYf1SOPnoJHg9qnvKurUSZsZAavuk5NgicvlV7Kr1sKvWy+5aL2NxnkojAlaTzazmWeDZOnx/9j9fe15qkGacbsate+JvtpvVmn+lWhfIJwXZ7Y022uY1OS35W5emEYLr2NLhT8H+f21HNEirQoZaMlaVNDUiNSYC/jAGMCIaFlG1CpW4QtXq1K1B3WLqZsQIyCIySY2tjWOME1sJYxyzcWIbxmyYuDXsmZhnD2ZloIpZlTMG3sur+98+XyrO3DTK6mETp7CJtgu0ZvSwmwGepp/tFBmiyBBdDFNkOMwP08UwXUx+CkhETMGGKdgwM1TwdsRdhQu4+UUOYntySZxw/BLYMQY7RpNhOIQfvx5LhkO1uAvuOYxB7N4cua35p5B0IXBhW1JD0o5O5QcuCsP+2UTSVmIfujnvHaMHnSX3YugGvExmDy+PWeQOLucOLvcymX28TF5wV4ThoMyq8ngC0HmH9S0Xz7TgxQ5inwSWSUq3NSdYTnIM2j0BrGybX9G2zsEum8TMPgd87oD3YA6R9NRMVe6uM7xMZhcvj9nHy2T28TKZXbw8ZvaitrQ3s2eBe4F3haRzgKfa28MGNwJvkbQ0BLoXANcf4jLnnHPOOXeEOhy3i54PnC/pcZJHYZ0LIOnLkt4CYGa/Ai4medLAZmAncOWhLHPOOeecc0euedvZwZFI0oWh2YSbJbxMZhcvj9nHy2T28TKZXbw8ZuZBrHPOOeecm3Nmx9OnnXPOOeecOwAexDrnnHPOuTnHg9gjgKTVku6U9Likn0la2+k8zTeSviBpqySTtK4t3cumAyTlJd0Ujvv9kr4jaVVYtkTStyRtkvSQpDM6nd/5QtJtkh6QtFHSDyWtD+l+nnSQpHPDd9fZYd7PkQ4JvyOPhXNko6S3h3Q/R6bhQeyR4UrgKjN7BXApcHVnszMvfQM4nck9yIGXTSddBawxsxNI+gr5cki/BLjLzFaTPC3la5IOrk9gd6A2mNnxZraO5LndV4d0P086RNIK4IPAXW3Jfo501tvNbF0Ybghpfo5Mw4PYOU7SEuBk4N9C0o3Asc1aJ3d4mNkPzOyp9jQvm84xs7KZ3WoTd67eRdIZCsAGQtc5ZvYz4BngzMOeyXnIzIbaZvsA8/OkcyRFJH/uPgS0943u58gs4ufIzDyInfuOBbabWR0g/Gg/QdIzmussL5vZ48PAzZIGgIyZtXc5vRUvk8NG0rWSngQ+A7wbP0866ULgx2b282aCnyOzwrWSHpT0FUmL8XNkRh7EOueOaJI+CawCPtHpvDgws/eY2bHA35BcFnUdIOmVJL1ofrbTeXGTnGFmxwMnAruAazqcn1nNg9i570lgmaQ0QOh+dznJvzTXWV42HSbpI8BbgTeY2biZ7Qbqkpa2rbYCL5PDzsyuAV4LPIWfJ53wGpLP/iZJW4FXk7Qj34CfIx1jZk+EcQ24jKSc/LdkBh7EznFm9ixwL/CukHQO8JSZbe5crhx42XSapAuBdwJnTWmL+XXggrDOKcBLgDsOewbnGUkLJB3dNn82sBvw86QDzOxLZrbMzFaY2QqSduPnmdmX8HOkIyQVJS1oS3oncJ//lszMe+w6AkhaQ3Kn4gCwBzjXzB7saKbmGUlXAm8ClpL8MI+Y2Sovm86QdAxJ7cWvgJGQXDGzUyUdBVwHvAyoAn9pZt/rTE7nD0kvJQmOCkAM7AQ+YmYb/TzpPEnfBy4zs5v8HOkMSS8nuWkrBYjk++vDZrbVz5HpeRDrnHPOOefmHG9O4Jxzzjnn5hwPYp1zzjnn3JzjQaxzzjnnnJtzPIh1zjnnnHNzjgexzjnnnHNuzvEg1jk370hKS7pY0qOSHpK0UdJVU57R2FGSVki64BC2XyfpHVPSNkrqOfTc7df7r5R0r6T7JJ17ON7TOTe/eBDrnJuPvgKcDPy2mb0SWA98B+jvaK4mW0F44Px0mr337MU6YFIQa2brzGxk+tVfcG8DfmZm683sq4fpPZ1z84gHsc65eUXSKuBPSB4WPghgia+b2a/COh+V9LCkByX9u6S+kP53km6Q9D+SHpH0XUn9ba99Udjmfkl3SeoK6e+WdHeomfyBpBNC+vsk3S7pP8J294QHngNcAawJtae3hPW3SrpU0k+BayQtlfQ9ST8P+f0nSZGkJcDfA68N218RtrdmbbOkkyXdKekBST+VdFpIXyFpSNKnw+tulvTGGY5lt6R/DbXZD0m6OKS/B/hr4K3h/Y+bst20+W47Jje1rfvm8CB+55ybZF//5J1z7khzIrDJzHZNt1DSG4D3k9TSDkm6CrgE+POwyqnASWa2W9L1wPnAP0h6L0l3kKeb2bCkhUAlBIfvBM4ws4qk1wBfA9aG1zsFWGdmWyRdAlwUXvMCkh6U1k3J4gBwqpmZpDzwh2Y2KikF3AxsMLPrJX0KONvMzp5mH7PAfwEfNLNvSzoduDEE+AB9wANmdrGk1wOfB26d5nD9LZADjifpietHkh41s2tDML7AzP5qmu2Gpss3cP006zrn3LS8JtY55yZ7HXCDmQ2F+S8BZ7Ut/5aZ7Q7TPwFWhuk3A1eY2TCAmQ2aWQP4I+AE4G5JG4EvAv2SCs3XMLMt07zeTK62ia4WI+BSSfcD95E0kVi3H/u4BojN7Nshrz8Cft22bZkkyN1Xnl4H/IuZxWY2BlzL5GM1k4PNt3POtXhNrHNuvrkXWC1poC0Y3ZupfXOX26Yb7Pt7VMA1ZvbJ5y2QDub1RtumLwSWkNTMliV9DsjvY/uZtO9npS1QbpD05X6gr7E3e8t3fcr7Hez+OOeOcF4T65ybV8xsM3Aj8JW29qGSdE64BH47sEFSb9jkfOC2/XjpW4AL2trPLgiXym8B3iVpeUiPJJ28H6+3h+Sy/t4sBHaEQHApSVvf/dn+MSCSdFbI0+8AS4GN+5GvdrcDHwjHrwi8m/07VnvL92bgeEkFJTev/ekB5sk5N094EOucm4/eD9xPcon/YeAR4PeB58zsm8BXgZ9IehDoBT6xrxc0s+tIguM7w2XyW4Gcmf0Q+Bjw3yH9YaY8NWAGDwAPhxumbplhnc8Dp4Z9uI4kqGz6PyAXbty6Ykpeq8BbgU9LegC4DHibmbXX8u6PzwA14EHgbuAWM/vP/dhuxnyb2V0kx+4h4PvApgPMk3NuntDEFSPnnHPOOefmBq+Jdc4555xzc44Hsc4555xzbs7xINY555xzzs05HsQ655xzzrk5x4NY55xzzjk353gQ65xzzjnn5hwPYp1zzjnn3JzjQaxzzjnnnJtzPIh1zjnnnHNzzv8DPfM2XKrxVU8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(gold_recovery_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary \n",
    "These plots show an increase in concentration across the purification process for gold, a decrease in concentration for silver and a near constant concentration for lead. This indicates that the process is working as expected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Alright, so the process is working as we would expect\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing the datasets\n",
    "Now I will look for differences in distribution across the datasets. I will look at feed particle size and look at differences in the averages for the rougher and the cleaner across the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_particle_size_distribution(df):\n",
    "    kwargs = dict(hist_kws={'alpha': 0.5}, kde_kws={'linewidth':2})\n",
    "    plt.figure(figsize=(10,6), dpi=80)\n",
    "    for features, colour, labels in zip(input_feed, xcolors, xlabel_):\n",
    "        sns.distplot(features, color=colour, label=labels, **kwargs)\n",
    "        plt.axvline(0, c=\"r\")\n",
    "    plt.title('Feed particle size distribution for '+ [x.split('__', 1)[1] for x in xlabel_][0])\n",
    "    plt.xlabel('Distribution of feed particle size')\n",
    "    plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average particle size for rougher input train set is 57.22\n",
      "Average particle size for primary cleaner input train set is 7.40\n",
      "\n",
      "Average particle size for rougher input test set is 55.94\n",
      "Average particle size for primary cleaner input test set is 7.26\n"
     ]
    }
   ],
   "source": [
    "rougher_input_train = gold_recovery_train['rougher.input.feed_size'].mean()\n",
    "rougher_input_test = gold_recovery_test['rougher.input.feed_size'].mean()\n",
    "\n",
    "primary_cleaner_input_train, primary_cleaner_input_test = gold_recovery_train['primary_cleaner.input.feed_size'].mean(), gold_recovery_test['primary_cleaner.input.feed_size'].mean()\n",
    "\n",
    "print('Average particle size for rougher input train set is {:.2f}'.format(rougher_input_train))\n",
    "print('Average particle size for primary cleaner input train set is {:.2f}'.format(primary_cleaner_input_train))\n",
    "print()\n",
    "print('Average particle size for rougher input test set is {:.2f}'.format(rougher_input_test))\n",
    "print('Average particle size for primary cleaner input test set is {:.2f}'.format(primary_cleaner_input_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will plot the feed particle size destribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_feed = [gold_recovery_train['rougher.input.feed_size'], gold_recovery_test['rougher.input.feed_size']]\n",
    "xlabel_ = ['gold_recovery_train__rougher.input.feed_size', 'gold_recovery_test__rougher.input.feed_size']\n",
    "xcolors = ['green', 'orange']\n",
    "\n",
    "plot_particle_size_distribution(gold_recovery_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_feed = [gold_recovery_train['primary_cleaner.input.feed_size'], gold_recovery_test['primary_cleaner.input.feed_size']]\n",
    "xlabel_ = ['gold_recovery_train__primary_cleaner.input.feed_size', 'gold_recovery_test__primary_cleaner.input.feed_size']\n",
    "xcolors = ['dodgerblue', 'orange']\n",
    "\n",
    "plot_particle_size_distribution(gold_recovery_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "These plots indicate that there is very little variation in the datasets between this feature. this means that I will be able to train the data on the training dataset and be able to test the model with the dataset and it will be a good indicator of the models quality. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Particle size distributions were compared successfully\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomolies \n",
    "Now I will look at the distribution of the total concentration of all substances at different stages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gold_recovery_full['rougher.input.feed.total_concentration'] = gold_recovery_full[['rougher.input.feed_ag', 'rougher.input.feed_pb', 'rougher.input.feed_sol', 'rougher.input.feed_au']].sum(axis=1)\n",
    "\n",
    "total_conc_stage_1 = gold_recovery_full['rougher.input.feed.total_concentration']\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(total_conc_stage_1, density=True, bins=100)\n",
    "\n",
    "plt.xlabel('rougher.input.feed_total concentration')\n",
    "plt.ylabel('frequency')\n",
    "plt.title(\"Distribution of total concentration in raw feed\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the rougher.input.feed.total_concentration is left-skewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gold_recovery_full['rougher.output.concentrate.total_concentration'] = gold_recovery_full[['rougher.output.concentrate_ag', 'rougher.output.concentrate_pb', 'rougher.output.concentrate_sol', 'rougher.output.concentrate_au']].sum(axis=1)\n",
    "\n",
    "total_conc_stage_2 = gold_recovery_full['rougher.output.concentrate.total_concentration']\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(total_conc_stage_2, density=True, bins=100)\n",
    "\n",
    "plt.xlabel('rougher.output.concentrate_total concentration')\n",
    "plt.ylabel('frequency')\n",
    "plt.title(\"Distribution of total concentration in rougher concentrate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the rougher.input.feed.total_concentration is left-skewed. At the zero mark, we noticed a large spike in concentration. This represents an anomaly or outlier in the data and should be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gold_recovery_full['final.output.concentrate.total_concentration'] = gold_recovery_full[['final.output.concentrate_ag', 'final.output.concentrate_pb', 'final.output.concentrate_sol', 'final.output.concentrate_au']].sum(axis=1)\n",
    "\n",
    "total_conc_stage_3 = gold_recovery_full['final.output.concentrate.total_concentration'] \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(total_conc_stage_3, density=True, bins=100)\n",
    "\n",
    "plt.xlabel('final.output.concentrate_total concentration')\n",
    "plt.ylabel('frequency')\n",
    "plt.title(\"Distribution of total concentration in final concentrate\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of the rougher.input.feed.total_concentration is left-skewed. At the zero mark, we noticed a small spike in concentration. This represents an anomaly or outlier in the data and should be removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary\n",
    "\n",
    "All three charts have a left-skew and the last chart showed a singular peak at zero. Possible reasons for the anomaly at zero could be data entry errors or they represent observations that occured under unusual conditions. Or, they are legitimate observations that accurately describe the variability in the study area. We never know but we must remove this anomaly in order to prevent a bias or variance in the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing the anomalies \n",
    "I will now resolve the anomalies in the data so that it will be ready for training and testing our models.\n",
    "\n",
    "First I will filter the full dataset without the anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the new dataset is:  (15748, 90)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>primary_cleaner.input.sulfate</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <th>rougher.input.feed.total_concentration</th>\n",
       "      <th>rougher.output.concentrate.total_concentration</th>\n",
       "      <th>final.output.concentrate.total_concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "      <td>15748.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.214304</td>\n",
       "      <td>10.026196</td>\n",
       "      <td>9.913246</td>\n",
       "      <td>44.400564</td>\n",
       "      <td>67.752523</td>\n",
       "      <td>9.798429</td>\n",
       "      <td>2.764170</td>\n",
       "      <td>10.945094</td>\n",
       "      <td>3.118651</td>\n",
       "      <td>152.759136</td>\n",
       "      <td>...</td>\n",
       "      <td>-459.278568</td>\n",
       "      <td>15.352882</td>\n",
       "      <td>-487.765038</td>\n",
       "      <td>11.899371</td>\n",
       "      <td>-486.228794</td>\n",
       "      <td>19.337494</td>\n",
       "      <td>-506.277787</td>\n",
       "      <td>58.117670</td>\n",
       "      <td>70.137178</td>\n",
       "      <td>69.554310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.310202</td>\n",
       "      <td>1.486243</td>\n",
       "      <td>2.726059</td>\n",
       "      <td>3.545001</td>\n",
       "      <td>7.804956</td>\n",
       "      <td>2.316004</td>\n",
       "      <td>0.923925</td>\n",
       "      <td>2.598995</td>\n",
       "      <td>0.841430</td>\n",
       "      <td>41.007674</td>\n",
       "      <td>...</td>\n",
       "      <td>68.123256</td>\n",
       "      <td>5.388184</td>\n",
       "      <td>35.931051</td>\n",
       "      <td>5.276311</td>\n",
       "      <td>39.346925</td>\n",
       "      <td>5.402735</td>\n",
       "      <td>39.407940</td>\n",
       "      <td>6.695667</td>\n",
       "      <td>7.445869</td>\n",
       "      <td>4.892338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.594630</td>\n",
       "      <td>1.388714</td>\n",
       "      <td>0.360562</td>\n",
       "      <td>5.901388</td>\n",
       "      <td>2.030917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.873963</td>\n",
       "      <td>...</td>\n",
       "      <td>-799.868171</td>\n",
       "      <td>-0.372054</td>\n",
       "      <td>-797.142475</td>\n",
       "      <td>2.781642</td>\n",
       "      <td>-800.006180</td>\n",
       "      <td>0.127304</td>\n",
       "      <td>-809.741464</td>\n",
       "      <td>8.217984</td>\n",
       "      <td>2.016647</td>\n",
       "      <td>11.022639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.295335</td>\n",
       "      <td>9.110201</td>\n",
       "      <td>8.183922</td>\n",
       "      <td>43.426501</td>\n",
       "      <td>63.957616</td>\n",
       "      <td>8.035304</td>\n",
       "      <td>2.119044</td>\n",
       "      <td>9.373385</td>\n",
       "      <td>2.554949</td>\n",
       "      <td>122.607905</td>\n",
       "      <td>...</td>\n",
       "      <td>-500.212520</td>\n",
       "      <td>10.965728</td>\n",
       "      <td>-500.469663</td>\n",
       "      <td>8.045268</td>\n",
       "      <td>-500.138455</td>\n",
       "      <td>15.001278</td>\n",
       "      <td>-500.809756</td>\n",
       "      <td>54.330388</td>\n",
       "      <td>66.935623</td>\n",
       "      <td>68.378342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.103525</td>\n",
       "      <td>10.094175</td>\n",
       "      <td>9.641001</td>\n",
       "      <td>44.972065</td>\n",
       "      <td>68.362500</td>\n",
       "      <td>9.911354</td>\n",
       "      <td>2.783907</td>\n",
       "      <td>10.858379</td>\n",
       "      <td>3.053608</td>\n",
       "      <td>150.047538</td>\n",
       "      <td>...</td>\n",
       "      <td>-499.346287</td>\n",
       "      <td>14.959353</td>\n",
       "      <td>-499.780937</td>\n",
       "      <td>11.004297</td>\n",
       "      <td>-499.931077</td>\n",
       "      <td>19.985565</td>\n",
       "      <td>-500.067964</td>\n",
       "      <td>58.257695</td>\n",
       "      <td>70.811068</td>\n",
       "      <td>69.957987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.902746</td>\n",
       "      <td>11.040210</td>\n",
       "      <td>11.370904</td>\n",
       "      <td>46.238841</td>\n",
       "      <td>72.661421</td>\n",
       "      <td>11.276489</td>\n",
       "      <td>3.352763</td>\n",
       "      <td>12.230140</td>\n",
       "      <td>3.601351</td>\n",
       "      <td>181.637569</td>\n",
       "      <td>...</td>\n",
       "      <td>-400.077723</td>\n",
       "      <td>18.008575</td>\n",
       "      <td>-498.228842</td>\n",
       "      <td>13.999019</td>\n",
       "      <td>-499.283556</td>\n",
       "      <td>23.041616</td>\n",
       "      <td>-499.495877</td>\n",
       "      <td>62.600756</td>\n",
       "      <td>74.833940</td>\n",
       "      <td>71.687447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.001945</td>\n",
       "      <td>17.031899</td>\n",
       "      <td>19.615720</td>\n",
       "      <td>51.571885</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>19.552149</td>\n",
       "      <td>5.804178</td>\n",
       "      <td>22.272019</td>\n",
       "      <td>8.245022</td>\n",
       "      <td>265.983123</td>\n",
       "      <td>...</td>\n",
       "      <td>-110.550392</td>\n",
       "      <td>43.709931</td>\n",
       "      <td>-244.483566</td>\n",
       "      <td>27.926001</td>\n",
       "      <td>-137.740004</td>\n",
       "      <td>32.188906</td>\n",
       "      <td>-104.427459</td>\n",
       "      <td>76.978947</td>\n",
       "      <td>87.273435</td>\n",
       "      <td>80.210644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 89 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       final.output.concentrate_ag  final.output.concentrate_pb  \\\n",
       "count                 15748.000000                 15748.000000   \n",
       "mean                      5.214304                    10.026196   \n",
       "std                       1.310202                     1.486243   \n",
       "min                       0.594630                     1.388714   \n",
       "25%                       4.295335                     9.110201   \n",
       "50%                       5.103525                    10.094175   \n",
       "75%                       5.902746                    11.040210   \n",
       "max                      16.001945                    17.031899   \n",
       "\n",
       "       final.output.concentrate_sol  final.output.concentrate_au  \\\n",
       "count                  15748.000000                 15748.000000   \n",
       "mean                       9.913246                    44.400564   \n",
       "std                        2.726059                     3.545001   \n",
       "min                        0.360562                     5.901388   \n",
       "25%                        8.183922                    43.426501   \n",
       "50%                        9.641001                    44.972065   \n",
       "75%                       11.370904                    46.238841   \n",
       "max                       19.615720                    51.571885   \n",
       "\n",
       "       final.output.recovery  final.output.tail_ag  final.output.tail_pb  \\\n",
       "count           15748.000000          15748.000000          15748.000000   \n",
       "mean               67.752523              9.798429              2.764170   \n",
       "std                 7.804956              2.316004              0.923925   \n",
       "min                 2.030917              0.000000              0.000000   \n",
       "25%                63.957616              8.035304              2.119044   \n",
       "50%                68.362500              9.911354              2.783907   \n",
       "75%                72.661421             11.276489              3.352763   \n",
       "max               100.000000             19.552149              5.804178   \n",
       "\n",
       "       final.output.tail_sol  final.output.tail_au  \\\n",
       "count           15748.000000          15748.000000   \n",
       "mean               10.945094              3.118651   \n",
       "std                 2.598995              0.841430   \n",
       "min                 0.000000              0.000000   \n",
       "25%                 9.373385              2.554949   \n",
       "50%                10.858379              3.053608   \n",
       "75%                12.230140              3.601351   \n",
       "max                22.272019              8.245022   \n",
       "\n",
       "       primary_cleaner.input.sulfate  ...  \\\n",
       "count                   15748.000000  ...   \n",
       "mean                      152.759136  ...   \n",
       "std                        41.007674  ...   \n",
       "min                         1.873963  ...   \n",
       "25%                       122.607905  ...   \n",
       "50%                       150.047538  ...   \n",
       "75%                       181.637569  ...   \n",
       "max                       265.983123  ...   \n",
       "\n",
       "       secondary_cleaner.state.floatbank4_b_level  \\\n",
       "count                                15748.000000   \n",
       "mean                                  -459.278568   \n",
       "std                                     68.123256   \n",
       "min                                   -799.868171   \n",
       "25%                                   -500.212520   \n",
       "50%                                   -499.346287   \n",
       "75%                                   -400.077723   \n",
       "max                                   -110.550392   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_air  \\\n",
       "count                              15748.000000   \n",
       "mean                                  15.352882   \n",
       "std                                    5.388184   \n",
       "min                                   -0.372054   \n",
       "25%                                   10.965728   \n",
       "50%                                   14.959353   \n",
       "75%                                   18.008575   \n",
       "max                                   43.709931   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_a_level  \\\n",
       "count                                15748.000000   \n",
       "mean                                  -487.765038   \n",
       "std                                     35.931051   \n",
       "min                                   -797.142475   \n",
       "25%                                   -500.469663   \n",
       "50%                                   -499.780937   \n",
       "75%                                   -498.228842   \n",
       "max                                   -244.483566   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_air  \\\n",
       "count                              15748.000000   \n",
       "mean                                  11.899371   \n",
       "std                                    5.276311   \n",
       "min                                    2.781642   \n",
       "25%                                    8.045268   \n",
       "50%                                   11.004297   \n",
       "75%                                   13.999019   \n",
       "max                                   27.926001   \n",
       "\n",
       "       secondary_cleaner.state.floatbank5_b_level  \\\n",
       "count                                15748.000000   \n",
       "mean                                  -486.228794   \n",
       "std                                     39.346925   \n",
       "min                                   -800.006180   \n",
       "25%                                   -500.138455   \n",
       "50%                                   -499.931077   \n",
       "75%                                   -499.283556   \n",
       "max                                   -137.740004   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_air  \\\n",
       "count                              15748.000000   \n",
       "mean                                  19.337494   \n",
       "std                                    5.402735   \n",
       "min                                    0.127304   \n",
       "25%                                   15.001278   \n",
       "50%                                   19.985565   \n",
       "75%                                   23.041616   \n",
       "max                                   32.188906   \n",
       "\n",
       "       secondary_cleaner.state.floatbank6_a_level  \\\n",
       "count                                15748.000000   \n",
       "mean                                  -506.277787   \n",
       "std                                     39.407940   \n",
       "min                                   -809.741464   \n",
       "25%                                   -500.809756   \n",
       "50%                                   -500.067964   \n",
       "75%                                   -499.495877   \n",
       "max                                   -104.427459   \n",
       "\n",
       "       rougher.input.feed.total_concentration  \\\n",
       "count                            15748.000000   \n",
       "mean                                58.117670   \n",
       "std                                  6.695667   \n",
       "min                                  8.217984   \n",
       "25%                                 54.330388   \n",
       "50%                                 58.257695   \n",
       "75%                                 62.600756   \n",
       "max                                 76.978947   \n",
       "\n",
       "       rougher.output.concentrate.total_concentration  \\\n",
       "count                                    15748.000000   \n",
       "mean                                        70.137178   \n",
       "std                                          7.445869   \n",
       "min                                          2.016647   \n",
       "25%                                         66.935623   \n",
       "50%                                         70.811068   \n",
       "75%                                         74.833940   \n",
       "max                                         87.273435   \n",
       "\n",
       "       final.output.concentrate.total_concentration  \n",
       "count                                  15748.000000  \n",
       "mean                                      69.554310  \n",
       "std                                        4.892338  \n",
       "min                                       11.022639  \n",
       "25%                                       68.378342  \n",
       "50%                                       69.957987  \n",
       "75%                                       71.687447  \n",
       "max                                       80.210644  \n",
       "\n",
       "[8 rows x 89 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_full_data = gold_recovery_full[(gold_recovery_full['rougher.input.feed.total_concentration'] > 0.8) & \n",
    "                                        (gold_recovery_full['rougher.output.concentrate.total_concentration'] > 0.95) & \n",
    "                                        (gold_recovery_full['final.output.concentrate.total_concentration'] > 0.8)]\n",
    "print('The shape of the new dataset is: ', format(gold_recovery_full_data.shape))\n",
    "print()\n",
    "gold_recovery_full_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will find the index of the train set that can be found in the new full dataset and use this new index to filter the gold_recovery_full_data for the new train dataset free of the anomaly.  I will do a similar operation for the test set too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_test.set_index('date', inplace=True)\n",
    "gold_recovery_train.set_index('date', inplace=True)\n",
    "gold_recovery_full_data.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10779,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_train_data_index = gold_recovery_full_data.index.intersection(gold_recovery_train.index)\n",
    "gold_recovery_train_data_index.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <th>rougher.input.feed.total_concentration</th>\n",
       "      <th>rougher.output.concentrate.total_concentration</th>\n",
       "      <th>final.output.concentrate.total_concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-15 00:00:00</td>\n",
       "      <td>6.055403</td>\n",
       "      <td>9.889648</td>\n",
       "      <td>5.507324</td>\n",
       "      <td>42.192020</td>\n",
       "      <td>70.541216</td>\n",
       "      <td>10.411962</td>\n",
       "      <td>0.895447</td>\n",
       "      <td>16.904297</td>\n",
       "      <td>2.143149</td>\n",
       "      <td>...</td>\n",
       "      <td>-504.715942</td>\n",
       "      <td>9.925633</td>\n",
       "      <td>-498.310211</td>\n",
       "      <td>8.079666</td>\n",
       "      <td>-500.470978</td>\n",
       "      <td>14.151341</td>\n",
       "      <td>-605.841980</td>\n",
       "      <td>51.680034</td>\n",
       "      <td>66.424950</td>\n",
       "      <td>63.644396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-15 01:00:00</td>\n",
       "      <td>6.029369</td>\n",
       "      <td>9.968944</td>\n",
       "      <td>5.257781</td>\n",
       "      <td>42.701629</td>\n",
       "      <td>69.266198</td>\n",
       "      <td>10.462676</td>\n",
       "      <td>0.927452</td>\n",
       "      <td>16.634514</td>\n",
       "      <td>2.224930</td>\n",
       "      <td>...</td>\n",
       "      <td>-501.331529</td>\n",
       "      <td>10.039245</td>\n",
       "      <td>-500.169983</td>\n",
       "      <td>7.984757</td>\n",
       "      <td>-500.582168</td>\n",
       "      <td>13.998353</td>\n",
       "      <td>-599.787184</td>\n",
       "      <td>50.659114</td>\n",
       "      <td>67.012710</td>\n",
       "      <td>63.957723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-15 02:00:00</td>\n",
       "      <td>6.055926</td>\n",
       "      <td>10.213995</td>\n",
       "      <td>5.383759</td>\n",
       "      <td>42.657501</td>\n",
       "      <td>68.116445</td>\n",
       "      <td>10.507046</td>\n",
       "      <td>0.953716</td>\n",
       "      <td>16.208849</td>\n",
       "      <td>2.257889</td>\n",
       "      <td>...</td>\n",
       "      <td>-501.133383</td>\n",
       "      <td>10.070913</td>\n",
       "      <td>-500.129135</td>\n",
       "      <td>8.013877</td>\n",
       "      <td>-500.517572</td>\n",
       "      <td>14.028663</td>\n",
       "      <td>-601.427363</td>\n",
       "      <td>50.609929</td>\n",
       "      <td>66.103793</td>\n",
       "      <td>64.311180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-15 03:00:00</td>\n",
       "      <td>6.047977</td>\n",
       "      <td>9.977019</td>\n",
       "      <td>4.858634</td>\n",
       "      <td>42.689819</td>\n",
       "      <td>68.347543</td>\n",
       "      <td>10.422762</td>\n",
       "      <td>0.883763</td>\n",
       "      <td>16.532835</td>\n",
       "      <td>2.146849</td>\n",
       "      <td>...</td>\n",
       "      <td>-501.193686</td>\n",
       "      <td>9.970366</td>\n",
       "      <td>-499.201640</td>\n",
       "      <td>7.977324</td>\n",
       "      <td>-500.255908</td>\n",
       "      <td>14.005551</td>\n",
       "      <td>-599.996129</td>\n",
       "      <td>51.061546</td>\n",
       "      <td>65.752751</td>\n",
       "      <td>63.573449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-15 04:00:00</td>\n",
       "      <td>6.148599</td>\n",
       "      <td>10.142511</td>\n",
       "      <td>4.939416</td>\n",
       "      <td>42.774141</td>\n",
       "      <td>66.927016</td>\n",
       "      <td>10.360302</td>\n",
       "      <td>0.792826</td>\n",
       "      <td>16.525686</td>\n",
       "      <td>2.055292</td>\n",
       "      <td>...</td>\n",
       "      <td>-501.053894</td>\n",
       "      <td>9.925709</td>\n",
       "      <td>-501.686727</td>\n",
       "      <td>7.894242</td>\n",
       "      <td>-500.356035</td>\n",
       "      <td>13.996647</td>\n",
       "      <td>-601.496691</td>\n",
       "      <td>47.859163</td>\n",
       "      <td>65.908382</td>\n",
       "      <td>64.004667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  final.output.concentrate_ag  \\\n",
       "0 2016-01-15 00:00:00                     6.055403   \n",
       "1 2016-01-15 01:00:00                     6.029369   \n",
       "2 2016-01-15 02:00:00                     6.055926   \n",
       "3 2016-01-15 03:00:00                     6.047977   \n",
       "4 2016-01-15 04:00:00                     6.148599   \n",
       "\n",
       "   final.output.concentrate_pb  final.output.concentrate_sol  \\\n",
       "0                     9.889648                      5.507324   \n",
       "1                     9.968944                      5.257781   \n",
       "2                    10.213995                      5.383759   \n",
       "3                     9.977019                      4.858634   \n",
       "4                    10.142511                      4.939416   \n",
       "\n",
       "   final.output.concentrate_au  final.output.recovery  final.output.tail_ag  \\\n",
       "0                    42.192020              70.541216             10.411962   \n",
       "1                    42.701629              69.266198             10.462676   \n",
       "2                    42.657501              68.116445             10.507046   \n",
       "3                    42.689819              68.347543             10.422762   \n",
       "4                    42.774141              66.927016             10.360302   \n",
       "\n",
       "   final.output.tail_pb  final.output.tail_sol  final.output.tail_au  ...  \\\n",
       "0              0.895447              16.904297              2.143149  ...   \n",
       "1              0.927452              16.634514              2.224930  ...   \n",
       "2              0.953716              16.208849              2.257889  ...   \n",
       "3              0.883763              16.532835              2.146849  ...   \n",
       "4              0.792826              16.525686              2.055292  ...   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_level  \\\n",
       "0                                 -504.715942   \n",
       "1                                 -501.331529   \n",
       "2                                 -501.133383   \n",
       "3                                 -501.193686   \n",
       "4                                 -501.053894   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_air  \\\n",
       "0                                  9.925633   \n",
       "1                                 10.039245   \n",
       "2                                 10.070913   \n",
       "3                                  9.970366   \n",
       "4                                  9.925709   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_level  \\\n",
       "0                                 -498.310211   \n",
       "1                                 -500.169983   \n",
       "2                                 -500.129135   \n",
       "3                                 -499.201640   \n",
       "4                                 -501.686727   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_air  \\\n",
       "0                                  8.079666   \n",
       "1                                  7.984757   \n",
       "2                                  8.013877   \n",
       "3                                  7.977324   \n",
       "4                                  7.894242   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_level  \\\n",
       "0                                 -500.470978   \n",
       "1                                 -500.582168   \n",
       "2                                 -500.517572   \n",
       "3                                 -500.255908   \n",
       "4                                 -500.356035   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_air  \\\n",
       "0                                 14.151341   \n",
       "1                                 13.998353   \n",
       "2                                 14.028663   \n",
       "3                                 14.005551   \n",
       "4                                 13.996647   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_level  \\\n",
       "0                                 -605.841980   \n",
       "1                                 -599.787184   \n",
       "2                                 -601.427363   \n",
       "3                                 -599.996129   \n",
       "4                                 -601.496691   \n",
       "\n",
       "   rougher.input.feed.total_concentration  \\\n",
       "0                               51.680034   \n",
       "1                               50.659114   \n",
       "2                               50.609929   \n",
       "3                               51.061546   \n",
       "4                               47.859163   \n",
       "\n",
       "   rougher.output.concentrate.total_concentration  \\\n",
       "0                                       66.424950   \n",
       "1                                       67.012710   \n",
       "2                                       66.103793   \n",
       "3                                       65.752751   \n",
       "4                                       65.908382   \n",
       "\n",
       "   final.output.concentrate.total_concentration  \n",
       "0                                     63.644396  \n",
       "1                                     63.957723  \n",
       "2                                     64.311180  \n",
       "3                                     63.573449  \n",
       "4                                     64.004667  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_train_data = gold_recovery_full_data.loc[gold_recovery_train_data_index]\n",
    "gold_recovery_train_data.reset_index(inplace=True)\n",
    "gold_recovery_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4969,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_test_data_index = gold_recovery_full_data.index.intersection(gold_recovery_test.index)\n",
    "gold_recovery_test_data_index.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>final.output.concentrate_ag</th>\n",
       "      <th>final.output.concentrate_pb</th>\n",
       "      <th>final.output.concentrate_sol</th>\n",
       "      <th>final.output.concentrate_au</th>\n",
       "      <th>final.output.recovery</th>\n",
       "      <th>final.output.tail_ag</th>\n",
       "      <th>final.output.tail_pb</th>\n",
       "      <th>final.output.tail_sol</th>\n",
       "      <th>final.output.tail_au</th>\n",
       "      <th>...</th>\n",
       "      <th>secondary_cleaner.state.floatbank4_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_a_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank5_b_level</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_air</th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <th>rougher.input.feed.total_concentration</th>\n",
       "      <th>rougher.output.concentrate.total_concentration</th>\n",
       "      <th>final.output.concentrate.total_concentration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-01 00:59:59</td>\n",
       "      <td>7.578381</td>\n",
       "      <td>10.466295</td>\n",
       "      <td>11.990938</td>\n",
       "      <td>40.743891</td>\n",
       "      <td>70.273583</td>\n",
       "      <td>12.688885</td>\n",
       "      <td>3.844413</td>\n",
       "      <td>11.075686</td>\n",
       "      <td>4.537988</td>\n",
       "      <td>...</td>\n",
       "      <td>-501.289139</td>\n",
       "      <td>7.946562</td>\n",
       "      <td>-432.317850</td>\n",
       "      <td>4.872511</td>\n",
       "      <td>-500.037437</td>\n",
       "      <td>26.705889</td>\n",
       "      <td>-499.709414</td>\n",
       "      <td>72.871822</td>\n",
       "      <td>79.939838</td>\n",
       "      <td>70.779504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-01 01:59:59</td>\n",
       "      <td>7.813838</td>\n",
       "      <td>10.581152</td>\n",
       "      <td>12.216172</td>\n",
       "      <td>39.604292</td>\n",
       "      <td>68.910432</td>\n",
       "      <td>12.829171</td>\n",
       "      <td>3.918901</td>\n",
       "      <td>11.132824</td>\n",
       "      <td>4.675117</td>\n",
       "      <td>...</td>\n",
       "      <td>-499.634209</td>\n",
       "      <td>7.958270</td>\n",
       "      <td>-525.839648</td>\n",
       "      <td>4.878850</td>\n",
       "      <td>-500.162375</td>\n",
       "      <td>25.019940</td>\n",
       "      <td>-499.819438</td>\n",
       "      <td>71.669225</td>\n",
       "      <td>81.118880</td>\n",
       "      <td>70.215454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-01 02:59:59</td>\n",
       "      <td>7.623392</td>\n",
       "      <td>10.424024</td>\n",
       "      <td>12.313710</td>\n",
       "      <td>40.724190</td>\n",
       "      <td>68.143213</td>\n",
       "      <td>12.977846</td>\n",
       "      <td>4.026561</td>\n",
       "      <td>10.990134</td>\n",
       "      <td>4.828907</td>\n",
       "      <td>...</td>\n",
       "      <td>-500.827423</td>\n",
       "      <td>8.071056</td>\n",
       "      <td>-500.801673</td>\n",
       "      <td>4.905125</td>\n",
       "      <td>-499.828510</td>\n",
       "      <td>24.994862</td>\n",
       "      <td>-500.622559</td>\n",
       "      <td>73.202598</td>\n",
       "      <td>79.267044</td>\n",
       "      <td>71.085315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-01 03:59:59</td>\n",
       "      <td>8.552457</td>\n",
       "      <td>10.503229</td>\n",
       "      <td>13.074570</td>\n",
       "      <td>39.290997</td>\n",
       "      <td>67.776393</td>\n",
       "      <td>12.451947</td>\n",
       "      <td>3.780702</td>\n",
       "      <td>11.155935</td>\n",
       "      <td>4.969620</td>\n",
       "      <td>...</td>\n",
       "      <td>-499.474407</td>\n",
       "      <td>7.897085</td>\n",
       "      <td>-500.868509</td>\n",
       "      <td>4.931400</td>\n",
       "      <td>-499.963623</td>\n",
       "      <td>24.948919</td>\n",
       "      <td>-498.709987</td>\n",
       "      <td>70.757057</td>\n",
       "      <td>81.335254</td>\n",
       "      <td>71.421253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-01 04:59:59</td>\n",
       "      <td>8.078781</td>\n",
       "      <td>10.222788</td>\n",
       "      <td>12.475427</td>\n",
       "      <td>40.254524</td>\n",
       "      <td>61.467078</td>\n",
       "      <td>11.827846</td>\n",
       "      <td>3.632272</td>\n",
       "      <td>11.403663</td>\n",
       "      <td>5.256806</td>\n",
       "      <td>...</td>\n",
       "      <td>-500.397500</td>\n",
       "      <td>8.107890</td>\n",
       "      <td>-509.526725</td>\n",
       "      <td>4.957674</td>\n",
       "      <td>-500.360026</td>\n",
       "      <td>25.003331</td>\n",
       "      <td>-500.856333</td>\n",
       "      <td>68.654396</td>\n",
       "      <td>80.902631</td>\n",
       "      <td>71.031520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  final.output.concentrate_ag  \\\n",
       "0 2016-09-01 00:59:59                     7.578381   \n",
       "1 2016-09-01 01:59:59                     7.813838   \n",
       "2 2016-09-01 02:59:59                     7.623392   \n",
       "3 2016-09-01 03:59:59                     8.552457   \n",
       "4 2016-09-01 04:59:59                     8.078781   \n",
       "\n",
       "   final.output.concentrate_pb  final.output.concentrate_sol  \\\n",
       "0                    10.466295                     11.990938   \n",
       "1                    10.581152                     12.216172   \n",
       "2                    10.424024                     12.313710   \n",
       "3                    10.503229                     13.074570   \n",
       "4                    10.222788                     12.475427   \n",
       "\n",
       "   final.output.concentrate_au  final.output.recovery  final.output.tail_ag  \\\n",
       "0                    40.743891              70.273583             12.688885   \n",
       "1                    39.604292              68.910432             12.829171   \n",
       "2                    40.724190              68.143213             12.977846   \n",
       "3                    39.290997              67.776393             12.451947   \n",
       "4                    40.254524              61.467078             11.827846   \n",
       "\n",
       "   final.output.tail_pb  final.output.tail_sol  final.output.tail_au  ...  \\\n",
       "0              3.844413              11.075686              4.537988  ...   \n",
       "1              3.918901              11.132824              4.675117  ...   \n",
       "2              4.026561              10.990134              4.828907  ...   \n",
       "3              3.780702              11.155935              4.969620  ...   \n",
       "4              3.632272              11.403663              5.256806  ...   \n",
       "\n",
       "   secondary_cleaner.state.floatbank4_b_level  \\\n",
       "0                                 -501.289139   \n",
       "1                                 -499.634209   \n",
       "2                                 -500.827423   \n",
       "3                                 -499.474407   \n",
       "4                                 -500.397500   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_air  \\\n",
       "0                                  7.946562   \n",
       "1                                  7.958270   \n",
       "2                                  8.071056   \n",
       "3                                  7.897085   \n",
       "4                                  8.107890   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_a_level  \\\n",
       "0                                 -432.317850   \n",
       "1                                 -525.839648   \n",
       "2                                 -500.801673   \n",
       "3                                 -500.868509   \n",
       "4                                 -509.526725   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_air  \\\n",
       "0                                  4.872511   \n",
       "1                                  4.878850   \n",
       "2                                  4.905125   \n",
       "3                                  4.931400   \n",
       "4                                  4.957674   \n",
       "\n",
       "   secondary_cleaner.state.floatbank5_b_level  \\\n",
       "0                                 -500.037437   \n",
       "1                                 -500.162375   \n",
       "2                                 -499.828510   \n",
       "3                                 -499.963623   \n",
       "4                                 -500.360026   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_air  \\\n",
       "0                                 26.705889   \n",
       "1                                 25.019940   \n",
       "2                                 24.994862   \n",
       "3                                 24.948919   \n",
       "4                                 25.003331   \n",
       "\n",
       "   secondary_cleaner.state.floatbank6_a_level  \\\n",
       "0                                 -499.709414   \n",
       "1                                 -499.819438   \n",
       "2                                 -500.622559   \n",
       "3                                 -498.709987   \n",
       "4                                 -500.856333   \n",
       "\n",
       "   rougher.input.feed.total_concentration  \\\n",
       "0                               72.871822   \n",
       "1                               71.669225   \n",
       "2                               73.202598   \n",
       "3                               70.757057   \n",
       "4                               68.654396   \n",
       "\n",
       "   rougher.output.concentrate.total_concentration  \\\n",
       "0                                       79.939838   \n",
       "1                                       81.118880   \n",
       "2                                       79.267044   \n",
       "3                                       81.335254   \n",
       "4                                       80.902631   \n",
       "\n",
       "   final.output.concentrate.total_concentration  \n",
       "0                                     70.779504  \n",
       "1                                     70.215454  \n",
       "2                                     71.085315  \n",
       "3                                     71.421253  \n",
       "4                                     71.031520  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold_recovery_test_data = gold_recovery_full_data.loc[gold_recovery_test_data_index]\n",
    "gold_recovery_test_data.reset_index(inplace=True)\n",
    "gold_recovery_test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Rows with abnormal total concentration were detected and removed\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion \n",
    "Through the initial analysis of the distribution of concentration of metals across purification stage, we observed that the concentration of gold (au) increased from the rougher.output to the final.output. This indicated that the process is performing as designed. \n",
    "Then I compared the feed particle size distribution for the training and test dataset, and observed similar particle size distributions with little or no variation in the distribution.\n",
    "When I checked for anomalies in total concentrations of all substances at different stages it was observed anomalies were present in the data with an abnormal peak in total concentration at 0. \n",
    "We used a threshold between 0.8 and 0.95 to remove the anomaly. We used the index of train and test set to generate new train and test data without anomaly from the full dataset. Now the data is prepared for model building."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Nice summary!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Model \n",
    "I am tasked with training a model which will predict two values:\n",
    "\n",
    "- rougher concentrate recovery rougher.output.recovery\n",
    "- final concentrate recovery final.output.recovery\n",
    "\n",
    "To do that, I will build and train different models, and evaluate them using cross-validation. I will use the sMAPE and Final sMAPE formulas as evaluation metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def smape(y_true, y_pred):\n",
    "    smape = (np.abs(y_true - y_pred)/((np.abs(y_true) + np.abs(y_pred))/2)).mean()\n",
    "    return smape\n",
    "\n",
    "def smape_final(y_true, y_pred):\n",
    "    smape_out_rougher = smape(y_true[:,0], y_pred[:,0])\n",
    "    smape_out_final = smape(y_true[:,1], y_pred[:,1])\n",
    "    return ((0.25 * smape_out_rougher) + (0.75 * smape_out_final))\n",
    "\n",
    "smape_score = make_scorer(smape_final, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "SMAPE calculation functions are correct, but there is a subtle problem with how the scorer object was initialized: in scikit-learn there is a convention that the higher the 'score' returned by the scorer object the better (so that GridSearhCV and similar tools would always just maximize the score). This works without any changes for metrics like accuracy, F1 score, precision, recall, etc., but for error functions like MAE, MSE, SMAPE it leads to maximizing the error. We'd like to minimize the error instead, how can we do that? Well, one way is to just maximize the flipped function, i.e. multiplied by -1: that's exactly what setting `greater_is_better=False` parameter of `make_scorer` does. To see why it makes sense, think of going from minimizing y = x^2 to maximizing y = -x^2, the minimum point of the original function is the same as the maximum point of the function multiplied by -1.\n",
    "    \n",
    "So, if you use the scorer for an error function without `greater_is_better=False`, tools like `GridSearchCV` will maximize the error leading to selecting the worst model.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Fixed!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "Going back to task 1.3, the point was that `output` and `calculation` columns are not present in the test set because these columns are not available before the process is finished, and thus we can't use them as features in our models\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment V2</b>\n",
    "\n",
    "Still not quite correct: `gold_recovery_test_data` has the same set of columns as `gold_recovery_full`, but we need to use the columns from the *original* test set as features (or just `input` and `state` features, in other words)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment V3</b>\n",
    "\n",
    "Unfortunately still not correct. If you look at the set of columns in `gold_recovery_test_data`, it contains all columns from the full dataset (the same set of columns as `gold_recovery_train_data` and `gold_recovery_full`). You need to save the set of columns from the original test set (`gold_recovery_test`).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment V4</b>\n",
    "\n",
    "Ok, fixed, but now the filtering for abnormal values from above is lost. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_train=gold_recovery_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_recovery_test=gold_recovery_test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features_train = gold_recovery_train.drop(['rougher.output.recovery', 'final.output.recovery'], axis=1)\n",
    "#features_test = gold_recovery_test.drop(['rougher.output.recovery', 'final.output.recovery'], axis=1)\n",
    "#target_train = gold_recovery_train[['rougher.output.recovery', 'final.output.recovery']]\n",
    "#target_test = gold_recovery_test[['rougher.output.recovery', 'final.output.recovery']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#target_train=target_train.values\n",
    "#target_test=target_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['rougher.output.recovery', 'final.output.recovery']\n",
    "features = list(set(gold_recovery_train.columns).intersection(set(gold_recovery_test.columns)).difference(set(target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_train, features_test = gold_recovery_train[features].values, gold_recovery_test[features].values\n",
    "target_train, target_test = gold_recovery_train[target].values, gold_recovery_test[target].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11017, 52)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4554, 52)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4554, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11017, 2)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Applying scaling to the whole dataset used for cross-validation is inappropriate: to avoid data leakage (and overly optimistic cross-validation scores as a result) scaling should be applied in each fold separately. One easy way to achieve this is using [pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline model \n",
    "using a dummy regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy_regr = DummyRegressor(strategy=\"mean\")\n",
    "dummy_regr.fit(features_train, target_train)\n",
    "dummy_regr_test_predictions = dummy_regr.predict(features_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11420314934872655"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smape_final(target_test, dummy_regr_test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our baseline sMAPE is 11.42. Lets see if we can improve."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "Good, there is a simple baseline\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean smape:  -10.29%\n",
      "sMAPE range of values are:  [-0.11327987 -0.08886104 -0.09270128 -0.10787246 -0.11183018]\n",
      "\n",
      "Execution time: 0.5574010070413351 mins\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "lr_regr = LinearRegression().fit(features_train, target_train) \n",
    "\n",
    "cv_score_lr = cross_val_score(lr_regr, features_train, target_train, cv=5, scoring=smape_score)\n",
    "print('Mean smape:  {:.2%}'.format(cv_score_lr.mean()))\n",
    "print('sMAPE range of values are: ', cv_score_lr)\n",
    "print()\n",
    "print('Execution time: ' + str((timeit.default_timer() - start_time)) + ' mins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regression Model\n",
    "I will check without tuning hyperparameters first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean smape:  -11.08%\n",
      "sMAPE range of values are:  [-0.09413354 -0.11737779 -0.09124396 -0.12524529 -0.12621457]\n",
      "\n",
      "Execution time: 6.943736650561914 mins\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "rf_regr = RandomForestRegressor().fit(features_train, target_train) # train the model \n",
    "\n",
    "cv_score_rf = cross_val_score(rf_regr, features_train, target_train, cv=5, scoring=smape_score)\n",
    "print('Mean smape:  {:.2%}'.format(cv_score_rf.mean()))\n",
    "print('sMAPE range of values are: ', cv_score_rf)\n",
    "print()\n",
    "print(\"Execution time: \" + str((timeit.default_timer() - start_time)/60) + ' mins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With hyperparameter tuning\n",
    "Using grid search to find the hyperparameters for the random forest algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_estimators: 10, Mean smape: -13.5919%\n",
      "n_estimators: 25, Mean smape: -11.8576%\n",
      "n_estimators: 50, Mean smape: -11.4970%\n"
     ]
    }
   ],
   "source": [
    "for i in [10, 25, 50]:\n",
    "    rf = RandomForestRegressor(n_estimators = i, random_state = 12345)\n",
    "    rf.fit(features_train, target_train)\n",
    "    cross_val_scores_rf = cross_val_score(rf, features_train, target_train, cv=5, scoring=smape_score)\n",
    "    print('n_estimators: ' + str(i) + ', Mean smape: {:.4%}'.format(cross_val_scores_rf.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are: {'n_estimators': 50}\n",
      "\n",
      "Execution time: 5.389975279119486 mins\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "\n",
    "grid = {\n",
    "    \"n_estimators\" : [10, 25, 50]\n",
    "} \n",
    "regressor = RandomForestRegressor(random_state = 12345)\n",
    "\n",
    "grid_search_rf = GridSearchCV(estimator = regressor, param_grid = grid, scoring=smape_score)\n",
    "\n",
    "grid_search_rf.fit(features_train, target_train)\n",
    "\n",
    "print('The best hyperparameters are: {}'.format(grid_search_rf.best_params_))\n",
    "print()\n",
    "print(\"Execution time: \" + str((timeit.default_timer() - start_time)/60) + ' mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean smape:  -11.53%\n",
      "sMAPE range of values are:  [-0.09829462 -0.12758366 -0.09432937 -0.12783726 -0.12825269]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_rf = RandomForestRegressor(**grid_search_rf.best_params_)\n",
    "model_rf.fit(features_train, target_train) \n",
    "cv_score_rf= cross_val_score(model_rf, features_train, target_train, cv=5, scoring=smape_score)\n",
    "print('Mean smape:  {:.2%}'.format(cv_score_rf.mean()))\n",
    "print('sMAPE range of values are: ', cv_score_rf)\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though we can see a decrease in our mean sMAPE score. The execution time has increased as well"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean smape:  -17.3835%\n",
      "sMAPE range of values are:  [-0.13588113 -0.21783936 -0.12846931 -0.20490243 -0.18208049]\n",
      "\n",
      "Execution time: 0.12293302174657583 mins\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "dt_regr = DecisionTreeRegressor().fit(features_train, target_train)  \n",
    "\n",
    "cv_score_dt = cross_val_score(dt_regr, features_train, target_train, cv=5, scoring=smape_score)\n",
    "print('Mean smape:  {:.4%}'.format(cv_score_dt.mean()))\n",
    "print('sMAPE range of values are: ', cv_score_dt)\n",
    "print()\n",
    "print(\"Execution time: \" + str((timeit.default_timer() - start_time)/60) + ' mins')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree with hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max depth: 2, Mean smape: -9.2568%\n",
      "Max depth: 4, Mean smape: -9.8451%\n",
      "Max depth: 8, Mean smape: -11.0130%\n"
     ]
    }
   ],
   "source": [
    "for i in [2, 4, 8]:\n",
    "    dt = DecisionTreeRegressor(max_depth = i, random_state = 12345)\n",
    "    dt.fit(features_train, target_train)\n",
    "    cross_val_scores_dt = cross_val_score(dt, features_train, target_train, cv=5, scoring=smape_score)\n",
    "    print('Max depth: ' + str(i) + ', Mean smape: {:.4%}'.format(cross_val_scores_dt.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters are: {'max_depth': 2, 'min_samples_split': 8}\n",
      "\n",
      "Execution time: 0.24930684745001297 mins\n"
     ]
    }
   ],
   "source": [
    "start_time = timeit.default_timer()\n",
    "\n",
    "params_ = {\n",
    "    \"max_depth\" : [2, 4, 8],\n",
    "    \"min_samples_split\" : [2, 4, 8]\n",
    "    }\n",
    "\n",
    "dt_regressor = DecisionTreeRegressor()\n",
    "\n",
    "grid_search_dt = GridSearchCV(estimator = dt_regressor, param_grid = params_, scoring=smape_score)\n",
    "\n",
    "grid_search_dt.fit(features_train, target_train)\n",
    "\n",
    "print('The best hyperparameters are: {}'.format(grid_search_dt.best_params_))\n",
    "print()\n",
    "print(\"Execution time: \" + str((timeit.default_timer() - start_time)/60) + ' mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean smape:  -9.26%\n",
      "sMAPE range of values are:  [-0.08691361 -0.08341002 -0.06984121 -0.11754129 -0.10513367]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt_model = DecisionTreeRegressor(**grid_search_dt.best_params_)\n",
    "dt_model.fit(features_train, target_train) \n",
    "cv_score_dt= cross_val_score(dt_model, features_train, target_train, cv=5, scoring=smape_score)\n",
    "print('Mean smape:  {:.2%}'.format(cv_score_dt.mean()))\n",
    "print('sMAPE range of values are: ', cv_score_dt)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "With the decision tree regressor, when we tuned various hyperparameters, the optimized sMAPE score was 9.26"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "The actual SMAPE value is positive (the formula doesn't allow for negative results), the value is negative only due to a technicality described above (setting `greater_is_better=False` negates the score in line with the convention that in scikit-learn the score is always maximized)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_run(X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    model_dtr = DecisionTreeRegressor(**grid_search_dt.best_params_)\n",
    "    model_dtr.fit(features_train, target_train)  \n",
    "    dtr_test_predictions = model_dtr.predict(X_test)\n",
    "    \n",
    "    print('The final sMAPE is: {:.2%}'.format(smape_final(target_test, dtr_test_predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final sMAPE is: 12.17%\n"
     ]
    }
   ],
   "source": [
    "test_run(features_train, target_train, features_test, target_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "Great, you tried a few different models and did some hyperparameter tuning and used our target metric to compare the models. It seems that you slightly misunderstood how the test set should be used though: its sole purpose in life is to be used to evaluate the single final model. To compare different models just use their cross-validation results. If we use the test set to make any decisions about the models, it won't be possible to get an unbiased estimate of how well the final model generalizes to new data.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V2</b>\n",
    "\n",
    "Great!\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Conclusion\n",
    "\n",
    "After the data was imported and the general information was inspected for the dataset, a significant share of NA where observed. I then proceeded to calculate the recovery for the rougher.output.recovery feature using the training set. I computed the mean absolute error between the calculated value and feature values to be 0.0. This signifies that the recovery value was calculated correctly since there was no error. I chose to resolve the NA's by dropping them and changed the datatype in the date feature to datetime.\n",
    "\n",
    "On analyzing the distribution of concentration of metals across purification stage, it was  observed that the concentration of gold (au) increased from the rougher.output to the final.output, and noted that our process is performing as designed. The data was then checked for anomalies in the data and compared the feed particle size distribution for the training and test dataset. An abnormal peak in total concentration was found at 0. The anomaly was removed using a threshold between 0.8 and 0.95. The index of train and test set was used to generate new train and test data without anomaly from the full dataset.\n",
    "\n",
    "In building the model, I wrote a function to calculate the final sMAPE value, and trained different models, evaluated them using cross-validation, with and without hyperparameter tuning. The final model was tested using the test sample. It was observed that the best performing model is the Random forest regressor followed by the linear regression model. In summary, the model with the best performance is the Random forest regressor based on the final sMAPE metric. This model was able to predict the amount of gold recovered and optimize gold production with a symetrical Mean Absolute Percentage Error of only 12.17%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "<s><b>Reviewer's comment</b>\n",
    "\n",
    "Please check the conclusions after fixing the problems above\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment V4</b>\n",
    "\n",
    "It's importnant to note that we weren't able to improve on the baseline, which means our model is not very useful. There can be several reasons for this: for example, maybe data quality is poor or maybe there is some hidden variable which we didn't account for\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thank you for your time reviewing my project! :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "<b>Reviewer's comment</b>\n",
    "\n",
    "You're welcome :)\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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