Capstone_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grocery Recommendation Project\n",
    "\n",
    "1. Data Clustering on users  \n",
    "2. Recommendation systems\n",
    "  * Content-based filtering  \n",
    "  * Collaborative filtering  \n",
    "3. Metadata NLP Search Engine\n",
    "3. Market Basket analysis  \n",
    "4. Website interface  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this analysis I will be working as a Data Scientist for a grocery store that is looking to discover insights from sales data that could be used for targeted direct mail marketing (specific coupons mailed to customers), targeted email marketing (\"An item you like has gone on sale!\"), and online shopper recommendations to 'add to cart' based on similar items and also based on items other people who bought that item have purchased.  \n",
    "\n",
    "If time permits, I may also perform a market basket analysis to forecast what products a customer is likely to purchase in their next order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data was retrieved from Kaggle and was provided by Instacart for a market basket analysis competition in 2018.  \n",
    "\n",
    "The data is divided into 6 files:\n",
    "\n",
    "- **_Aisles.csv_**: 134 Unique aisle numbers and descriptions\n",
    "- **_Departments.csv_**: 21 Unique department numbers and descriptions\n",
    "- **_Products.csv_**: 49,688 Unique product ids, with description, aisle id, and department id\n",
    "- **_Orders.csv_**: 3,421,083 Unique order id, with user id, order number, order_dow, order_hour_of_day, days_since_prior_order, and eval_set indicating if the order is in train, prior, or test\n",
    "- **_Order_products_train.csv_**: Order id, product id, add to cart order, and reorder indicator\n",
    "- **_Order_products_prior.csv_**: Order id, product id, add to cart order, and reorder indicator\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import and basic info\n",
    "\n",
    "**Output** - aisles, orders, products, departments, order_products_prior, order_products_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import time\n",
    "from user_functions import *\n",
    "from datetime import datetime\n",
    "import pickle\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set default visualization parameters\n",
    "\n",
    "CB91_Blue = '#2CBDFE'\n",
    "CB91_Green = '#47DBCD'\n",
    "CB91_Pink = '#F3A0F2'\n",
    "CB91_Purple = '#9D2EC5'\n",
    "CB91_Violet = '#661D98'\n",
    "CB91_Amber = '#F5B14C'\n",
    "color_list = [CB91_Blue, CB91_Pink, CB91_Green, CB91_Amber, CB91_Purple, CB91_Violet]\n",
    "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=color_list)\n",
    "sns.set_context(\"notebook\", rc={\"font.size\":16, \"axes.titlesize\":20, \"axes.labelsize\":18})\n",
    "sns.set(font='Franklin Gothic Book',\n",
    "rc={'axes.axisbelow': False,\n",
    "'axes.edgecolor': 'lightgrey',\n",
    "# 'axes.edgecolor': 'white',\n",
    "'axes.facecolor': 'None',\n",
    "'axes.grid': False,\n",
    "'axes.labelcolor': 'dimgrey',\n",
    "# 'axes.labelcolor': 'white',\n",
    "'axes.spines.right': False,\n",
    "'axes.spines.top': False,\n",
    "'axes.prop_cycle': plt.cycler(color=color_list),\n",
    "'figure.facecolor': 'white',\n",
    "'lines.solid_capstyle': 'round',\n",
    "'patch.edgecolor': 'w',\n",
    "'patch.force_edgecolor': True,\n",
    "'text.color': 'dimgrey',\n",
    "# 'text.color': 'white',    \n",
    "'xtick.bottom': False,\n",
    "'xtick.color': 'dimgrey',\n",
    "# 'xtick.color': 'white',    \n",
    "'xtick.direction': 'out',\n",
    "'xtick.top': False,\n",
    "'ytick.color': 'dimgrey',\n",
    "# 'ytick.color': 'white',\n",
    "'ytick.direction': 'out',\n",
    "'ytick.left': False,\n",
    "'ytick.right': False})\n",
    "%matplotlib inline\n",
    "\n",
    "# NOTE: if you visualizations are too cluttered to read, try calling 'plt.gcf().autofmt_xdate()'!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "aisles = pd.read_csv(\"Data/aisles.csv\")\n",
    "departments = pd.read_csv(\"Data/departments.csv\")\n",
    "order_products_prior = pd.read_csv(\"Data/order_products__prior.csv\")\n",
    "order_products_train = pd.read_csv(\"Data/order_products__train.csv\")\n",
    "orders = pd.read_csv(\"Data/orders.csv\")\n",
    "products = pd.read_csv(\"Data/products.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Nan values\n",
    "\n",
    "Data is very clean. Nan values in orders.days_since_prior_order, but they are valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aisle_id    0\n",
       "aisle       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department_id    0\n",
       "department       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_id       0\n",
       "product_name     0\n",
       "aisle_id         0\n",
       "department_id    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                       0\n",
       "user_id                        0\n",
       "eval_set                       0\n",
       "order_number                   0\n",
       "order_dow                      0\n",
       "order_hour_of_day              0\n",
       "days_since_prior_order    206209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.0    369323\n",
       "7.0     320608\n",
       "6.0     240013\n",
       "4.0     221696\n",
       "3.0     217005\n",
       "5.0     214503\n",
       "2.0     193206\n",
       "8.0     181717\n",
       "1.0     145247\n",
       "9.0     118188\n",
       "14.0    100230\n",
       "10.0     95186\n",
       "13.0     83214\n",
       "11.0     80970\n",
       "12.0     76146\n",
       "0.0      67755\n",
       "15.0     66579\n",
       "16.0     46941\n",
       "21.0     45470\n",
       "17.0     39245\n",
       "20.0     38527\n",
       "18.0     35881\n",
       "19.0     34384\n",
       "22.0     32012\n",
       "28.0     26777\n",
       "23.0     23885\n",
       "27.0     22013\n",
       "24.0     20712\n",
       "25.0     19234\n",
       "29.0     19191\n",
       "26.0     19016\n",
       "Name: days_since_prior_order, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.days_since_prior_order.value_counts()\n",
    "# Nothing greater than 30 here so it must actually be '30+'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "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",
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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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2398795        1    prior             2          3                  7   \n",
       "2    473747        1    prior             3          3                 12   \n",
       "3   2254736        1    prior             4          4                  7   \n",
       "4    431534        1    prior             5          4                 15   \n",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()\n",
    "# order_id is a unique key for the order\n",
    "# order_number is specific to a user\n",
    "# seems like for any user, order 1 will have a Nan in days_since_prior_order since it is their first order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id             0\n",
       "product_id           0\n",
       "add_to_cart_order    0\n",
       "reordered            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id             0\n",
       "product_id           0\n",
       "add_to_cart_order    0\n",
       "reordered            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze 'Products'\n",
    "  \n",
    "49688 unique products "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>All-Seasons Salt</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Robust Golden Unsweetened Oolong Tea</td>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Green Chile Anytime Sauce</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49688\n",
      "134\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "print(len(products))\n",
    "print(products.aisle_id.nunique())\n",
    "print(products.department_id.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>cookies cakes</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>All-Seasons Salt</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "      <td>spices seasonings</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Robust Golden Unsweetened Oolong Tea</td>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "      <td>tea</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>frozen meals</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Green Chile Anytime Sauce</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>marinades meat preparation</td>\n",
       "      <td>pantry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "\n",
       "   department_id                       aisle department  \n",
       "0             19               cookies cakes     snacks  \n",
       "1             13           spices seasonings     pantry  \n",
       "2              7                         tea  beverages  \n",
       "3              1                frozen meals     frozen  \n",
       "4             13  marinades meat preparation     pantry  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many products in each department and aisle?\n",
    "# I guess these would be better/more informative with aisle and dept names\n",
    "products_aisles = pd.merge(products, aisles, on = 'aisle_id', how = 'left')\n",
    "products_desc = pd.merge(products_aisles, departments, on = 'department_id', how = 'left')\n",
    "products_desc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='department', data= products_desc, order = products_desc.department.value_counts().index)\n",
    "plt.title('Number of Products per Department')\n",
    "plt.xticks(rotation=70)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Department')\n",
    "plt.savefig('Images/products_by_department.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='aisle', data= products_desc, order = products_desc.aisle.value_counts().index)\n",
    "plt.title('Number of Products per Aisle')\n",
    "plt.xticks(rotation=90, fontsize = 8)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Aisle')\n",
    "plt.savefig('Images/products_by_aisle.png');\n",
    "# Uh oh, 'missing' is our most popular aisle\n",
    "# And I see there is a 'missing' value for department above as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "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",
       "        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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Ultra Antibacterial Dish Liquid</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>Organic Honeycrisp Apples</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>Uncured Turkey Bologna</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>Write Bros Ball Point Pens, Cap-Pen, Medium (1...</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>417</td>\n",
       "      <td>Classics Baby Binks Easter Chocolate Bunny</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49552</th>\n",
       "      <td>49553</td>\n",
       "      <td>Organic Plain Skyr</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49574</th>\n",
       "      <td>49575</td>\n",
       "      <td>Chocolate Vanilla Malt Custard</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49640</th>\n",
       "      <td>49641</td>\n",
       "      <td>8\\\" Pecan Pie</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49663</th>\n",
       "      <td>49664</td>\n",
       "      <td>Lemon Cayenne Drinking Vinegar</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49668</th>\n",
       "      <td>49669</td>\n",
       "      <td>K Cup Dark Blend</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1258 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id                                       product_name  \\\n",
       "37             38                    Ultra Antibacterial Dish Liquid   \n",
       "71             72                          Organic Honeycrisp Apples   \n",
       "109           110                             Uncured Turkey Bologna   \n",
       "296           297  Write Bros Ball Point Pens, Cap-Pen, Medium (1...   \n",
       "416           417         Classics Baby Binks Easter Chocolate Bunny   \n",
       "...           ...                                                ...   \n",
       "49552       49553                                 Organic Plain Skyr   \n",
       "49574       49575                     Chocolate Vanilla Malt Custard   \n",
       "49640       49641                                      8\\\" Pecan Pie   \n",
       "49663       49664                     Lemon Cayenne Drinking Vinegar   \n",
       "49668       49669                                   K Cup Dark Blend   \n",
       "\n",
       "       aisle_id  department_id    aisle department  \n",
       "37          100             21  missing    missing  \n",
       "71          100             21  missing    missing  \n",
       "109         100             21  missing    missing  \n",
       "296         100             21  missing    missing  \n",
       "416         100             21  missing    missing  \n",
       "...         ...            ...      ...        ...  \n",
       "49552       100             21  missing    missing  \n",
       "49574       100             21  missing    missing  \n",
       "49640       100             21  missing    missing  \n",
       "49663       100             21  missing    missing  \n",
       "49668       100             21  missing    missing  \n",
       "\n",
       "[1258 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_desc[products_desc['aisle']=='missing']\n",
    "# It appears that 1258 products have 'missing' aisle 100 and department 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "        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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>cookies cakes</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Mint Chocolate Flavored Syrup</td>\n",
       "      <td>103</td>\n",
       "      <td>19</td>\n",
       "      <td>ice cream toppings</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Salted Caramel Lean Protein &amp; Fiber Bar</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>energy granola bars</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>Nacho Cheese White Bean Chips</td>\n",
       "      <td>107</td>\n",
       "      <td>19</td>\n",
       "      <td>chips pretzels</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>Organic Sourdough Einkorn Crackers Rosemary</td>\n",
       "      <td>78</td>\n",
       "      <td>19</td>\n",
       "      <td>crackers</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49661</th>\n",
       "      <td>49662</td>\n",
       "      <td>Bacon Cheddar Pretzel Pieces</td>\n",
       "      <td>107</td>\n",
       "      <td>19</td>\n",
       "      <td>chips pretzels</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49664</th>\n",
       "      <td>49665</td>\n",
       "      <td>Super Dark Coconut Ash &amp; Banana Chocolate Bar</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49665</th>\n",
       "      <td>49666</td>\n",
       "      <td>Ginger Snaps Snacking Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>cookies cakes</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49670</th>\n",
       "      <td>49671</td>\n",
       "      <td>Milk Chocolate Drops</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49678</th>\n",
       "      <td>49679</td>\n",
       "      <td>Famous Chocolate Wafers</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>cookies cakes</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6264 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id                                   product_name  aisle_id  \\\n",
       "0               1                     Chocolate Sandwich Cookies        61   \n",
       "15             16                  Mint Chocolate Flavored Syrup       103   \n",
       "24             25        Salted Caramel Lean Protein & Fiber Bar         3   \n",
       "31             32                  Nacho Cheese White Bean Chips       107   \n",
       "40             41    Organic Sourdough Einkorn Crackers Rosemary        78   \n",
       "...           ...                                            ...       ...   \n",
       "49661       49662                   Bacon Cheddar Pretzel Pieces       107   \n",
       "49664       49665  Super Dark Coconut Ash & Banana Chocolate Bar        45   \n",
       "49665       49666                  Ginger Snaps Snacking Cookies        61   \n",
       "49670       49671                           Milk Chocolate Drops        45   \n",
       "49678       49679                        Famous Chocolate Wafers        61   \n",
       "\n",
       "       department_id                aisle department  \n",
       "0                 19        cookies cakes     snacks  \n",
       "15                19   ice cream toppings     snacks  \n",
       "24                19  energy granola bars     snacks  \n",
       "31                19       chips pretzels     snacks  \n",
       "40                19             crackers     snacks  \n",
       "...              ...                  ...        ...  \n",
       "49661             19       chips pretzels     snacks  \n",
       "49664             19      candy chocolate     snacks  \n",
       "49665             19        cookies cakes     snacks  \n",
       "49670             19      candy chocolate     snacks  \n",
       "49678             19        cookies cakes     snacks  \n",
       "\n",
       "[6264 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just confirming the one to many relationship between department and aisle\n",
    "products_desc[products_desc['department']=='snacks']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>Sun Cups Dark Chocolate</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>Simple</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>184</td>\n",
       "      <td>Kisses Milk Chocolate Candy</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>207</td>\n",
       "      <td>Minis Candy Bars</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>227</td>\n",
       "      <td>Triple Chocolate Snack Size</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49503</th>\n",
       "      <td>49504</td>\n",
       "      <td>Dark Chocolate Peanut Butter Cups Miniatures</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49600</th>\n",
       "      <td>49601</td>\n",
       "      <td>Pomegranate Gummy Bears</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49619</th>\n",
       "      <td>49620</td>\n",
       "      <td>Milk Chocolate Mini's</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49664</th>\n",
       "      <td>49665</td>\n",
       "      <td>Super Dark Coconut Ash &amp; Banana Chocolate Bar</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49670</th>\n",
       "      <td>49671</td>\n",
       "      <td>Milk Chocolate Drops</td>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>candy chocolate</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1246 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id                                   product_name  aisle_id  \\\n",
       "123           124                        Sun Cups Dark Chocolate        45   \n",
       "135           136                                         Simple        45   \n",
       "183           184                    Kisses Milk Chocolate Candy        45   \n",
       "206           207                               Minis Candy Bars        45   \n",
       "226           227                    Triple Chocolate Snack Size        45   \n",
       "...           ...                                            ...       ...   \n",
       "49503       49504   Dark Chocolate Peanut Butter Cups Miniatures        45   \n",
       "49600       49601                        Pomegranate Gummy Bears        45   \n",
       "49619       49620                          Milk Chocolate Mini's        45   \n",
       "49664       49665  Super Dark Coconut Ash & Banana Chocolate Bar        45   \n",
       "49670       49671                           Milk Chocolate Drops        45   \n",
       "\n",
       "       department_id            aisle department  \n",
       "123               19  candy chocolate     snacks  \n",
       "135               19  candy chocolate     snacks  \n",
       "183               19  candy chocolate     snacks  \n",
       "206               19  candy chocolate     snacks  \n",
       "226               19  candy chocolate     snacks  \n",
       "...              ...              ...        ...  \n",
       "49503             19  candy chocolate     snacks  \n",
       "49600             19  candy chocolate     snacks  \n",
       "49619             19  candy chocolate     snacks  \n",
       "49664             19  candy chocolate     snacks  \n",
       "49670             19  candy chocolate     snacks  \n",
       "\n",
       "[1246 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An aisle only belongs to one department\n",
    "products_desc[products_desc['aisle_id']==45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(products_desc, open(\"Pickle/products_desc.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3421083\n",
      "1\n",
      "3421083\n"
     ]
    }
   ],
   "source": [
    "print(len(orders))\n",
    "print(orders.order_id.min())\n",
    "print(orders.order_id.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='order_hour_of_day', data= orders)\n",
    "plt.title('Number of Orders Taken by Hour of the Day.')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Hour')\n",
    "plt.savefig('Images/orders_by_hour.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='order_dow', data= orders)\n",
    "plt.title('Number of Orders Taken by Day of the Week.')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Day')\n",
    "plt.savefig('Images/orders_by_dow.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='days_since_prior_order', data= orders)\n",
    "plt.title('Days Since Prior Order')\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Days')\n",
    "plt.savefig('Images/days_since_order.png');\n",
    "\n",
    "# Spikes at 7, 14, 21, and 28 indicate people who reorder weekly, or every 2 or three weeks but on the same day\n",
    "# Does 0.0 days mean someone ordered twice in the same day?\n",
    "# Surprising how many people order more than once a week\n",
    "# 30 does seem to indicate 30 or more days since last order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "        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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3367565</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>550135</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3108588</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2295261</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2550362</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1187899</td>\n",
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      ],
      "text/plain": [
       "    order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0    2539329        1    prior             1          2                  8   \n",
       "1    2398795        1    prior             2          3                  7   \n",
       "2     473747        1    prior             3          3                 12   \n",
       "3    2254736        1    prior             4          4                  7   \n",
       "4     431534        1    prior             5          4                 15   \n",
       "5    3367565        1    prior             6          2                  7   \n",
       "6     550135        1    prior             7          1                  9   \n",
       "7    3108588        1    prior             8          1                 14   \n",
       "8    2295261        1    prior             9          1                 16   \n",
       "9    2550362        1    prior            10          4                  8   \n",
       "10   1187899        1    train            11          4                  8   \n",
       "11   2168274        2    prior             1          2                 11   \n",
       "12   1501582        2    prior             2          5                 10   \n",
       "13   1901567        2    prior             3          1                 10   \n",
       "14    738281        2    prior             4          2                 10   \n",
       "\n",
       "    days_since_prior_order  \n",
       "0                      NaN  \n",
       "1                     15.0  \n",
       "2                     21.0  \n",
       "3                     29.0  \n",
       "4                     28.0  \n",
       "5                     19.0  \n",
       "6                     20.0  \n",
       "7                     14.0  \n",
       "8                      0.0  \n",
       "9                     30.0  \n",
       "10                    14.0  \n",
       "11                     NaN  \n",
       "12                    10.0  \n",
       "13                     3.0  \n",
       "14                     8.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(15)\n",
    "# Appears to be sorted by user_id and then order_number\n",
    "# Most recent order_number per user_id is in the train(or test) set, others in prior\n",
    "# First order per user has NaN in days_since"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
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      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "1868044         1   112108    train             4          4   \n",
       "3355525         2   202279    prior             3          5   \n",
       "3417191         3   205970    prior            16          5   \n",
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       "...           ...      ...      ...           ...        ...   \n",
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       "2903252   3421082   175185    prior            23          2   \n",
       "419472    3421083    25247    prior            24          2   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "1868044                 10                     9.0  \n",
       "3355525                  9                     8.0  \n",
       "3417191                 17                    12.0  \n",
       "2958007                  9                     7.0  \n",
       "2593373                 16                     9.0  \n",
       "...                    ...                     ...  \n",
       "1809968                 13                     0.0  \n",
       "877324                  11                     2.0  \n",
       "1949724                 11                     NaN  \n",
       "2903252                 18                     4.0  \n",
       "419472                   6                    21.0  \n",
       "\n",
       "[3421083 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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   ],
   "source": [
    "orders.sort_values('order_id') # Just checking that it has all the order_ids starting at 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
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      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "38        2774568        3     test            13          5   \n",
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       "3421018   2821651   206207     test            17          2   \n",
       "3421068    803273   206208     test            50          5   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "38                      15                    11.0  \n",
       "44                      12                    30.0  \n",
       "53                      16                    22.0  \n",
       "96                      11                     8.0  \n",
       "102                     20                    30.0  \n",
       "...                    ...                     ...  \n",
       "3420918                 17                     6.0  \n",
       "3420929                 14                    14.0  \n",
       "3421001                 20                     0.0  \n",
       "3421018                 13                    14.0  \n",
       "3421068                 11                     4.0  \n",
       "\n",
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     "execution_count": 27,
     "metadata": {},
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   ],
   "source": [
    "orders_test = orders[orders['eval_set']=='test'] # 75000 orders in our test set\n",
    "# So basically for these test orders, I don't have the answers.  I don't know what products were ordered.\n",
    "# If I do market basket analysis I will get to these\n",
    "orders_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <th>34</th>\n",
       "      <td>3160850</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>676467</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>521107</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1402502</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2774568</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "26   1374495        3    prior             1          1                 14   \n",
       "27    444309        3    prior             2          3                 19   \n",
       "28   3002854        3    prior             3          3                 16   \n",
       "29   2037211        3    prior             4          2                 18   \n",
       "30   2710558        3    prior             5          0                 17   \n",
       "31   1972919        3    prior             6          0                 16   \n",
       "32   1839752        3    prior             7          0                 15   \n",
       "33   3225766        3    prior             8          0                 17   \n",
       "34   3160850        3    prior             9          0                 16   \n",
       "35    676467        3    prior            10          3                 16   \n",
       "36    521107        3    prior            11          0                 18   \n",
       "37   1402502        3    prior            12          1                 15   \n",
       "38   2774568        3     test            13          5                 15   \n",
       "\n",
       "    days_since_prior_order  \n",
       "26                     NaN  \n",
       "27                     9.0  \n",
       "28                    21.0  \n",
       "29                    20.0  \n",
       "30                    12.0  \n",
       "31                     7.0  \n",
       "32                     7.0  \n",
       "33                     7.0  \n",
       "34                     7.0  \n",
       "35                    17.0  \n",
       "36                    11.0  \n",
       "37                    15.0  \n",
       "38                    11.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['user_id']==3] # But I have all of this user's order/products in prior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Order_products_train and _prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         1       49302                  1          1\n",
       "1         1       11109                  2          1\n",
       "2         1       10246                  3          0\n",
       "3         1       49683                  4          0\n",
       "4         1       43633                  5          1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1384617\n",
      "1\n",
      "3421070\n",
      "131209\n"
     ]
    }
   ],
   "source": [
    "print(len(order_products_train)) # Has 1,384,617 order/products\n",
    "print(order_products_train.order_id.min()) # starts with order number 1\n",
    "print(order_products_train.order_id.max())\n",
    "print(order_products_train.order_id.nunique()) # Has 131,209 unique orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         2       33120                  1          1\n",
       "1         2       28985                  2          1\n",
       "2         2        9327                  3          0\n",
       "3         2       45918                  4          1\n",
       "4         2       30035                  5          0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32434489\n",
      "2\n",
      "3421083\n",
      "3214874\n"
     ]
    }
   ],
   "source": [
    "print(len(order_products_prior)) # Has 32,434,489 order/products\n",
    "print(order_products_prior.order_id.min()) # starts with order number 2\n",
    "print(order_products_prior.order_id.max())\n",
    "print(order_products_prior.order_id.nunique()) # Has 3,214,874 unique orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
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       "  </thead>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>17794</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>40141</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1819</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>43668</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         2       33120                  1          1\n",
       "1         2       28985                  2          1\n",
       "2         2        9327                  3          0\n",
       "3         2       45918                  4          1\n",
       "4         2       30035                  5          0\n",
       "5         2       17794                  6          1\n",
       "6         2       40141                  7          1\n",
       "7         2        1819                  8          1\n",
       "8         2       43668                  9          0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior[order_products_prior.order_id == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3355525</th>\n",
       "      <td>2</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
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       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "3355525         2   202279    prior             3          5   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "3355525                  9                     8.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders.order_id == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
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       "      <th>days_since_prior_order</th>\n",
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       "      <th>3355523</th>\n",
       "      <td>2894949</td>\n",
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       "      <td>prior</td>\n",
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       "      <th>3355524</th>\n",
       "      <td>1500071</td>\n",
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       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3355526</th>\n",
       "      <td>3058248</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
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       "      <td>30.0</td>\n",
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       "      <th>3355527</th>\n",
       "      <td>2808715</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>3355528</th>\n",
       "      <td>132412</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>3355529</th>\n",
       "      <td>2382766</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>3355530</th>\n",
       "      <td>104690</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355531</th>\n",
       "      <td>1050357</td>\n",
       "      <td>202279</td>\n",
       "      <td>train</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "3355523   2894949   202279    prior             1          5   \n",
       "3355524   1500071   202279    prior             2          4   \n",
       "3355525         2   202279    prior             3          5   \n",
       "3355526   3058248   202279    prior             4          3   \n",
       "3355527   2808715   202279    prior             5          5   \n",
       "3355528    132412   202279    prior             6          5   \n",
       "3355529   2382766   202279    prior             7          1   \n",
       "3355530    104690   202279    prior             8          6   \n",
       "3355531   1050357   202279    train             9          1   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "3355523                  9                     NaN  \n",
       "3355524                 13                    20.0  \n",
       "3355525                  9                     8.0  \n",
       "3355526                 14                    30.0  \n",
       "3355527                 13                     9.0  \n",
       "3355528                  9                    30.0  \n",
       "3355529                 14                    24.0  \n",
       "3355530                 10                    30.0  \n",
       "3355531                 14                    30.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders.user_id == 202279]\n",
    "# This user's third order was order number 2.  Need to be sure not to think order_id is chronological."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>429686</th>\n",
       "      <td>1050357</td>\n",
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       "      <td>1050357</td>\n",
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       "      <th>429692</th>\n",
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       "      <th>429693</th>\n",
       "      <td>1050357</td>\n",
       "      <td>4461</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429694</th>\n",
       "      <td>1050357</td>\n",
       "      <td>45002</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>429695</th>\n",
       "      <td>1050357</td>\n",
       "      <td>8693</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        order_id  product_id  add_to_cart_order  reordered\n",
       "429686   1050357       33120                  1          1\n",
       "429687   1050357        1003                  2          1\n",
       "429688   1050357       21543                  3          1\n",
       "429689   1050357       20995                  4          1\n",
       "429690   1050357       46064                  5          0\n",
       "429691   1050357       20168                  6          0\n",
       "429692   1050357       42824                  7          1\n",
       "429693   1050357        4461                  8          1\n",
       "429694   1050357       45002                  9          1\n",
       "429695   1050357        8693                 10          0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train[order_products_train.order_id == 1050357]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So what do I know about this user...\n",
    "\n",
    "# They have ordered 9 times.\n",
    "len(orders[orders.user_id == 202279])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# They usually order on day 5 (presumably Friday)\n",
    "orders[orders.user_id == 202279].order_dow.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.625"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# They average 22.6 days between orders\n",
    "orders[orders.user_id == 202279].days_since_prior_order.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.666666666666666"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# They order around 11:30 am.\n",
    "orders[orders.user_id == 202279].order_hour_of_day.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2894949, 1500071, 2, 3058248, 2808715, 132412, 2382766, 104690, 1050357]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are their order numbers\n",
    "list(orders[orders.user_id == 202279]['order_id'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>product_id</th>\n",
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       "      <th>3</th>\n",
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       "      <td>45918</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>28997578</th>\n",
       "      <td>3058248</td>\n",
       "      <td>27531</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28997579</th>\n",
       "      <td>3058248</td>\n",
       "      <td>2944</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28997580</th>\n",
       "      <td>3058248</td>\n",
       "      <td>38226</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28997581</th>\n",
       "      <td>3058248</td>\n",
       "      <td>35106</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28997582</th>\n",
       "      <td>3058248</td>\n",
       "      <td>12545</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id  product_id  add_to_cart_order  reordered\n",
       "0                2       33120                  1          1\n",
       "1                2       28985                  2          1\n",
       "2                2        9327                  3          0\n",
       "3                2       45918                  4          1\n",
       "4                2       30035                  5          0\n",
       "...            ...         ...                ...        ...\n",
       "28997578   3058248       27531                  4          0\n",
       "28997579   3058248        2944                  5          0\n",
       "28997580   3058248       38226                  6          0\n",
       "28997581   3058248       35106                  7          0\n",
       "28997582   3058248       12545                  8          0\n",
       "\n",
       "[90 rows x 4 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# But what products have they ordered... merging tables will help here\n",
    "order_products_prior[order_products_prior['order_id'].isin(list(orders[orders.user_id == 202279]['order_id'].values))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge order_products together\n",
    "\n",
    "Output: merged_order_products, order_products_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1384617\n",
      "32434489\n"
     ]
    }
   ],
   "source": [
    "print(len(order_products_train))\n",
    "print(len(order_products_prior))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33819106"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The dataframe of all order products should be the length of these two together\n",
    "len(order_products_train)+len(order_products_prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         1       49302                  1          1\n",
       "1         1       11109                  2          1\n",
       "2         1       10246                  3          0\n",
       "3         1       49683                  4          0\n",
       "4         1       43633                  5          1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These two dataframes shouldn't have any overlap.  I need an outer join to merge them together.\n",
    "merged_order_products = pd.merge(order_products_train, order_products_prior, how = 'outer')\n",
    "merged_order_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33819106"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_order_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_per_order = merged_order_products.groupby('order_id').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5 is the most common number of products per order\n",
    "\n",
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='product_id', data = products_per_order)\n",
    "plt.title('Number of Products per Order')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Number of Orders')\n",
    "plt.xlabel('Products')\n",
    "plt.savefig('Images/product_count_by_order.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "        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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>816049</td>\n",
       "      <td>49302</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1242203</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1383349</td>\n",
       "      <td>49302</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1787378</td>\n",
       "      <td>49302</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Bulgarian Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "      <td>yogurt</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered      product_name  \\\n",
       "0         1       49302                  1          1  Bulgarian Yogurt   \n",
       "1    816049       49302                  7          1  Bulgarian Yogurt   \n",
       "2   1242203       49302                  1          1  Bulgarian Yogurt   \n",
       "3   1383349       49302                 11          1  Bulgarian Yogurt   \n",
       "4   1787378       49302                  8          0  Bulgarian Yogurt   \n",
       "\n",
       "   aisle_id  department_id   aisle  department  \n",
       "0       120             16  yogurt  dairy eggs  \n",
       "1       120             16  yogurt  dairy eggs  \n",
       "2       120             16  yogurt  dairy eggs  \n",
       "3       120             16  yogurt  dairy eggs  \n",
       "4       120             16  yogurt  dairy eggs  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This has an entry for each ordered product.  If we combine it with aisle and department we can see which products\n",
    "# are ordered the most.\n",
    "order_products_desc = pd.merge(merged_order_products, products_desc, on = 'product_id')\n",
    "order_products_desc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Banana                             491291\n",
       "Bag of Organic Bananas             394930\n",
       "Organic Strawberries               275577\n",
       "Organic Baby Spinach               251705\n",
       "Organic Hass Avocado               220877\n",
       "                                    ...  \n",
       "Anarchy For Her Daily Fragrance         1\n",
       "Pumpkin Spice Almond Nog                1\n",
       "Frittata, Farmer's Market               1\n",
       "Flavor Snacks                           1\n",
       "Water With Electrolytes                 1\n",
       "Name: product_name, Length: 49685, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Very cool.  People really like their bananas.\n",
    "order_products_desc['product_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='product_name', data= order_products_desc, \n",
    "              order = order_products_desc.product_name.value_counts().index[:20])\n",
    "plt.title('Most Ordered Products')\n",
    "plt.xticks(rotation=70)\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Product Name')\n",
    "plt.savefig('Images/ordered_products_by_name.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='product_name', data= order_products_desc, \n",
    "              order = order_products_desc.product_name.value_counts().index[:200])\n",
    "plt.title('Most Ordered Products')\n",
    "plt.xticks([])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Product Name')\n",
    "plt.savefig('Images/ordered_products_by_name_200.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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doIFKkiRJ0mx6nYGKiMWALwD7ZuZWwGeAd9+b35WZuyzI2CRJkiRpfvqegdoe+G1mTmvfnwicMvudIuJE4ApgR+Bk4Gpgf2rGaufMvC0ibs7MB/QStSRJkiTR/xqoDYBLIuL9EXEucDyw8hzutxFwDLA5sCewRGZuCdwA7NBXsJIkSZI0rO8EaklgV+BLmfl44Azg6Dnc76bMvDgzpwPXAue02y8HVuojUEmSJEmaXd8J1JXALzPzQoDMPBr4nzncb/ps398+9PWk0YQmSZIkSfPWdwL1LWDtiFgZICKeBPyy5xgkSZIk6V7pNYHKzNuA1wCnRsQ5wKuAF/cZgyRJkiTdW5Nmzpw57hh6M23atLWoMsK1N9xww6vazTNvOvK43mNZ5YA9wHJESZIkaaHSdwmfJEmSJC20TKAkSZIkqSMTKEmSJEnqyARKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGagGZOv/MGYGbff9pxJUmSJM3FlHEHoP9v0pTFp9545Ht6P+7UA940tfeDSpIkSQsRZ6AkSZIkqaNOCVRErDuH21664MORJEmSpImr6wzUp4a/iYjFgAMWfDiSJEmSNHHNcw1URBwEvAqYGhFXtJsntX939EgjkyRJkqQJZp4JVGZ+BPhIRJydmdv1E5IkSZIkTUxdu/A9NSKWAlYGJg9uzMxrRhKVJEmSJE1AXROol1GlfFdSJXxQewdtP4qgJEmSJGki6ppA7QWsl5l3jjIYSZIkSZrIunbhuxGYMcpAJEmSJGmi6zoD9UPgjIj4GnDr4MbMPHYkUUmSJEnSBNQ1gZpCJVFTRxiLJEmSJE1onRKozHzHqAORJEmSpImuUwIVEWdTXffuJjPtwidJkiRpkdG1hG/voa9XBPYEfrOgg5EkSZKkiaxrCd/VQ99eDfwqIn4IHDWSqCRJkiRpAuraxvxuIuIRwDILOBZJkiRJmtC6roG6kloDNQm4i5qFesUI45IkSZKkCadrCd/aow5EkiRJkia6rjNQk4D9gKcCM4Cvu4muJEmSpEVN1zVQHwS2Az4PHAs8OSLeNbKoJEmSJGkC6trG/DGZudXQ91+PiHNGEZAkSZIkTVSdu/BFxNJDXy8FLDWSiCRJkiRpguo6A3UE8LOIOJPqxLcDVdYnSZIkSYuM+SZQEfE8qnX5rsCT2s3XZ6ab6EqSJElapMyzhC8i3kB137s0M3+XmR8HLgB2iIgD+whQkiRJkiaK+a2BehbwlMz85eCGzLwAeDrwwhHGJUmSJEkTzvwSqMUyc/rsN2bm7cDk0YQkSZIkSRPT/BKo6yPiRbPfGBEvBm4eTUiSJEmSNDHNr4nEgcCpEfFGYBrVTGIj4O/Ac0YcmyRJkiRNKPNMoDLzmojYjGpbvgEwHTgiM3/QR3CSJEmSNJHMt415Zs4Ezmx/JEmSJGmRNb81UJIkSZKkxgRKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGSJEmSpI5MoCRJkiSpIxMoSZIkSerIBEqSJEmSOjKBkiRJkqSOTKAkSZIkqSMTKEmSJEnqyARKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGSJEmSpI5MoCRJkiSpIxMoSZIkSerIBEqSJEmSOjKBkiRJkqSOxpZARcTHIuLm/+Lfn9r+3jsiDl1ggUmSJEnSXIwlgYqIrYG7/pvfkZm7LKBwJEmSJKmTKX0fMCKWBN4JPBPYYy73ORG4AtgROBm4Gtgf+Bewc2beFhE3Z+YDeglakiRJkhjPDNTbgMzMf8zjPhsBxwCbA3sCS2TmlsANwA6jD1GSJEmS/r9eE6iI2Bh4ZGaeOJ+73pSZF2fmdOBa4Jx2++XASqOMUZIkSZLmpu8Svj2Ah0bEj9v392tf75yZNw3db/ps/+72oa8njTJASZIkSZqbXhOozHwd8LrB920d02P7jEGSJEmS7i33gZIkSZKkjibNnDlz3DH0Ztq0aWsBVwJrb7jhhle1m2fedORxvceyygF7wNzLEWfeeOR7eoymTD3gTWCJpCRJkjRXzkBJkiRJUkcmUJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAIlSZIkSR2ZQEmSJElSRyZQkiRJktSRCZQkSZIkdWQCJUmSJEkdmUBJkiRJUkcmUJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAIlSZIkSR2ZQEmSJElSRyZQkiRJktSRCZQkSZIkdWQCJUmSJEkdmUBJkiRJUkcmUJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAKlTu6afvsNwMy+/7TjSpIkSRPClHEHoIXDYlOWnHrZx3fp/bjrHnjq1N4PKkmSJM2FM1CSJEmS1JEJlCRJkiR1ZAIlSZIkSR2ZQEmSJElSRyZQkiRJktSRCZQkSZIkdWQCpYXajDHsTzXDvakkSZIWWe4DpYXa5ClLTv3BZ3bq9Zjb7Heae1NJkiQtopyBkiRJkqSOTKAkSZIkqSMTKEmSJEnqyARKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGSJEmSpI5MoCRJkiSpIxMoSZIkSerIBEqSJEmSOjKBkiRJkqSOTKAkSZIkqSMTKEmSJEnqyARKWsBmTL/9BmBmn3/aMSVJkjRiU8YdgHRfM3nKklNPOeqpvR7zmS/59tReDyhJkrSIcgZKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGSJEmSpI56b2MeEc8G3gDcDlwHvDQz/34vfs+pmblLROwNrJWZhy7QQCVJkiRpNr3OQEXEasDbgR0ycxvgJ8A7783vysxdFmRskiRJkjQ/fc9ALQ4cMjTj9B3gE7PfKSJOBK4AdgROBq4G9gf+BeycmbdFxM2Z+YB+wpYkSZKknmegMvOazDxz6KanA+fP4a4bAccAmwN7Aktk5pbADcAOIw9UkiRJkuag9zVQAxHxRGBXYPs5/PimzLy43e9a4Jx2++XASv1EKEmSJEl3N5YEKiJ2A14D7JSZ/5zDXabP9v3tQ19PGllgkiRJkjQPvbcxj4g3ArsDO2bmjX0fX1oUTZ9++w3AzL7/tONKkiTdZ/Q6AxURz6W67p0PfCMiAK7MzBf3GYe0qJkyZcmpnzt2x96Pu89eZ07t/aCSJEkj1GsClZknACd0uN8T5vL1oUNfP6D9ffQCDFGSJEmS5qr3Ej5JkiRJWliZQEmSJElSRyZQkiRJktSRCZQkSZIkdWQCJUmSJEkdmUBJkiRJUkcmUJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAIlSZIkSR2ZQEmSJElSRyZQkiRJktSRCZQkSZIkdWQCJUmSJEkdmUBJkiRJUkcmUJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlKSxuHP67TcAM/v+044rSZJ0r0wZdwCSFk2LT1ly6uFffnLvx3397mdM7f2gkiTpPsMZKEmSJEnqyARKkiRJkjoygZKk5o4Z41mX1Y4rSZIWAq6BkqRmiclLTn3xyU/p/biff9bp81yXdfuMO29YcvLiva7dun3GnTcuOXnxVfs8piRJCwMTKEma4JacvPjUp53yhl6P+a1nvs9mG5IkzYElfJIkSZLUkQmUJEmSJHVkAiVJkiRJHZlASZIkSVJHJlCSJEmS1JEJlCTpHrt9xvTe98xqx5QkaaxsYy5JuseWnDxl6tNP+kivx/zmsw+ytbokaeycgZIkSZKkjkygJEmSJKkjEyhJkiRJ6sgESpJ0nzCOxhbY3EKSFjk2kZAk3ScsOXnK1J2+9vnej3vari+ea3OL22fMuGHJyZN7b35x+4wZNy45efKqC0tMkrQwMYGSJGlElpw8eepOJ3619+Oe9pzd5pogLTl58tRdTjyjz3AAOPU5T7aLoqT7BEv4JEmSJKkjEyhJkiRJ6sgESpIkSZI6MoGSJEmSpI5MoCRJkiSpIxMoSZIkSerIBEqSJEmSOjKBkiRJkqSOTKAkSdLY3THjrhuAmX3+aceUpHtkyrgDkCRJWmLyYlN3/dpPez3m13bdYuq8fn7HjJk3LDF50jzvs6DdMWPmjUtMnrRqn8eUdM+YQEmSJM3BEpMnTX3lyX/o9Zgfe9YavSZsku45S/gkSZIkqSMTKEmSJEnqyARKkiRJkjoygZIkSZKkjkygJEmSFhLTZ8zsvd07MLMdVxJ24ZMkSVpoTJk8aeoxJ93U+3Ff9OxV7A4oNc5ASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAIlSZKke23G9PE0tmjHlXpnEwlJkiTda5OnTJp61vH9N7Z44gtsbKHxcAZKkiRJ9yl3jWlW7C5nxRYJzkBJkiTpPmWxKZOmTvvUjb0fd8P9p85zVmzmnXfdMGnxxXqdOZt55103Tlp8sVX7POZ93aKWQE1ufz942rRpAKy33nrMWHKJ3gO54447uPTSS9ea088qpqV7jmj+Mc1casV+A2LeMUHFtdiSK/UYUbeYpiwx8WJaoueYYP6vqaUXn3gxrTBlYsUEFddKU5btMaKuMS3VY0RdY5p45/OVFl+854jmH9OKi0+e049Gqsvzt+KUST1G1C2m5abc1WNEHc/nU2b0GFGZ32tqscUnXkwsObFiAlhvvfWm3vTxP/YYEazy0tWnXjpt7jGt+9CH/XTSlMUe0GNIzJx+182XXXH5FnOPaZ2fTpoyudeYAGZOn3HzZVf8fjiuazfccMPps99v0syZM3sMa7ymTZu2NfCDccchSZIkacJbe8MNN7xq9hsXtRmonwLbANcD/Q9LSJIkSVpYXDunGxepGShJkiRJ+m/YhU+SJEmSOjKBkiRJkqSOTKAkSZIkqSMTKEmSJEnqyARKkiRJkjoygZIkSZKkjkygJEmSJKkjEyhJkiSNTURMGncM0j1hAiXNxhP53E20xyYiFm9/T6i4pFGJiFXGHYO0oETEkgCZObN977l8PiJimfa31/Bj5IN/HzV4Y0XERhGxfUQsNzhRac5mP5GPW0RMGveHyeD4EfEQmDiPDUBELAu8GCZWXMOGHr+l+vqwi4ilZ49hYfmgHTpvrRMRm0fE8mOIYbm+j9lVRKwGfCsijomIXSJiqQkQ0/IT+fUVEZPbe6D319LCICLWGPNr/pCI+HBE7BARSw+fy8f9+TfRDD0e+0bEQzPzrjHHMzhfrzrRri/7OCdNmjlzQl53LBQiYlJmzmwnn8cAqwI/zszfjzk0oC7agB8BbwWmAW8CHg3sm5k/G0M8GwE3Z+b1EbF4Zt7Zdwxz02YyXgfcBXwAWBe4NTOv6un4i2XmXe3i95HA1Zl50+w/7yOWucT3HmAScHhm/m1ccQyLiJWA/YBVgC9n5s/bSXPmREuoIuJjwJ+BI4CHA+sAZ2bmn0ZwrJcClwI3Ardn5hVDP5uQj8/sIuIk4O/AE4AvU+exi4AbMvPfIz52AI8DTgfOzsw/jvJ490S7gLo/NXDwDGAGdW4/MTPP6TGOJYD1MnNaRByema9vt0/JzOl9xTE3Q5/NDwZeQn3u/TQz39FjDINz+vOBW4EzgfsBywy/J8cpIiYDbwf+BFwOXAZc09dnc3s9bwVs1P4sTZ27zsrMC/qIYR5xTQbul5l/Hlccc9LO4e8AbgGWA07IzF+POabvAM/KzFva95PG8Rkz9L7fDHhIZp48yuNN2FGjhcRgNOCDwBbUhfdmEfHIliyMRTspAuxGXXh8E9gX+BfwbuqDt9dYImIn4J3AlhGxIfCuiDiorzjmZuix2oe6qP0CsCtwPvCBiPifnkIZnGw+QSUFJ0XEUW2UefK4R5oy803Ar4EXRcSDxhnLQGb+FfgodaG7Q0Ssm5l3TZTkYGh0bjMqKU7g8cDnqfPFDiM69Jcy8/vA04BvRMTJEbF3RCw3kR6f2Q09XtsDf8/MFwN/AH5DPc9v7imUc4BTge2Aj0TE+yPiWRNlBqNd0K0CHAkcClwMvDkifhQRT+kpjBWAvSPib8ALI2KDFtt0gIjYdcyzB4NjvxG4CvgFsFRErBkRH2+z1yPTLuTuioiVgYOBvwEbU+f3YyPiSaM8/j2wBJWALwtsD+wF7BUR2/Zxns/MmZn5Q+BzwGHAcdRj9aKI+GxEvGzUMQwbmrXYr8WzakQ8OCIOiYhH9xnLnAxdC/yaGhS4P3BkRKwYEYtHxFYR8bGIeEAPsQyqKx4IXAnsMjhHjuszZui4ywHPjIh1I+JB7bHZNSLWXpDHM4H6L7QT5JLAhpn5PuBc4JfAmsA+45rSzMwZ7cu7gOnUBeYy1EzUHcBDobf62cGF/0HA4cAVwIHAg4B1ImLHHmKYq6HHahPqwnY9YFvgf6gP3SePOoahUZO1gTUzc//M3AY4GdgZuCAiVh91HHOKq/29ZkTcH/gK9UGb4/owGYpp1fba2R1YixqJPzEiDoqIFcYR27DBBVT79jHAWVSC/hxgb+BbwAtGcNyHAtu111ICTwJOAHYEvhsRJ06URGAOBh9+WwEXRcSewHcz8wvAq4DrM/Pfo74wz8yLqfPUrcAPgN9SF5efjIhD2+zLWLTzxJrAkzLzS5l5TmZ+Evhf4CfA73qK4+bMfC3wVWpm5ZiImBYR746Id1Kj0WNL1Ifee/drMa4AfDszrwGWpwY0+vBc4ALgkvb1hcAh1Dlg7DLztsw8ITMPpwYpfgqsATwdOCAiRvr5N3QNshJ13bQkNXP+GeC7wD9GefzZDb1ungecQr1uPkDNjj03ZiuP7tvQ9cquwIcy8+XUa2pT4Nnt9unUNcyoYxm8v7egZg6fS5UWPiMi1u/p+vI/IuKBEbFHRGwM/JA6L72XOjceB2xNVYktMCZQ/70HAT+NiFcBy2bmZZn5bWBLYCwlahHx8Ij4cGZ+kRoZ+B7wfuAhwKupkctetA/8KVR5wArUi/mCzNyLejGPdWZlyPeoN9mxVDnY76kRnp9Ab8nmw4C/D43mfiMz9wWeMI4yoqET5E7AicDxVKnHg6lRr+ePMaanAi9tX69PfYB8B1iNHmdY52H49XI8lQgfCXwjM39MlaadBXebBV0QNqJGkQ+mRlHXacfZB3gRcGpm/nMBHm+BGXpuPwn8kZoxX7clhbtSAxowa3ZhgYvWlIQq+7o0Mz9GDWQcRSVUN2fmHaM6fof4JgE3A2dGxPOGfnQT8IC+So5bLJOBQzPzJZn5aGpw4E7qAu6dfcUxH18Bvk69337ZbtsMuHqUBx16LV9EzaicSJWEH0bNRN0wyuPfExGxWESsRw22XJSZhwIfA66hyo5HddzBLN1KwEeo2dRtqMHMB2fml6nnr1dt8GlpqkTuQGoQ5SXUwM4CvQC/N9r77hfAWyLiBcC2mXk2NUj2SWApagamF5n5TWB/4NPUAOu2wCupUvU+rUM9Ry8AXk4NnqwCHAC8NDNfnZk/WpAHNIH6L0TVOF/JrBHey9o04RHAT9rJYUFeHHWJaRJwPbBsRDyRGpn/InUS35Jaw3Ie3G20ZZSxDMo6jgfeA3wfOLWVxj0sM787yhi6xNd8m0qYdqRmfI4C/paZZ8FoH6uhD9vByfkNEfGaiHhKRKyWmf8cVzlMRDyGOk88mZrRuIpKUA4Fto6IZ44jLmBl4AWZeUxmHpKZL6DW+L2NKud7zpjiGnhaRNwSEUdSCec2VCL8lYj4LLAicEy77wJ7bWXmqcDzqQu2pakk8w3U6OD9qHPVhBMRU9sI4nbAxpl5AnXhexM16LM6VYo86vfiYNDrXOAB7ba/ZeaFwO3U+oyxaSVPt1LP4wERcUZEvBt4C7MShJEa+kzbE3hNRDw2ItagSlK/lpnvzMyxPU4xqxT0udTzeDL12HwkIs6k1ovd2MegWPusnQ58GDgsIrahZjeOG/Wx52fo//8c6tz5MKp0fFC+ekqOdq304DNtT2pt41OoxO2XwEER8eihGZc+XUslbocB/8rMT1CzF39u13tj0645ZwAfB75BPU+XRcSHqPPmpdTs6ldHHUf7e/2IOJh6Pf+rDRB8kEo6LxtlDHPwaypx+iqwODWTuhg1s7rhKMoaTaDuhaGL2clR3cm2pNbOTKKy4EuomRaYVZbSi/YB+w9gc2oN1IbA87IWXv8MWK1dpPRhsP7pCOBHmbk5lUStDuxBlfWNTc5qm/oWauTmJ1T5wCrUeovBwui+3idfomYOTqUuqh8PHBwRy/ZdDjP0Gl8X+Etm3tnKhb6bmddl5jeohgiv6jumNkX/DCpBGDY9M28H/s34L3S/QZ3Al6FKaM+nuk2tAbwsMw9oa1kWWL344HXakoCLqeYLBwHnUaOBr6HWPExEq1NlTR8Dtml19Xdl5muArwFPzsw7RzmQEBFPiIitADLzK8CDI+KPEfGFiHgFNbr5w1Edfz6xDV77y0XEM6hR+g8Dp1Gv9YOoC4U+DJc5nUwN0r2PKnvePyJW7CmOOWoDl4tRs75LUjO/R1Hnq5dTpeQwos/moYvLZ0XEy6lR+X9RpYOPAvbPzF+O4tj30k7U2qybqEGyT1IzL68b5UGHBkKuo2Z7yMzrM/MkqsT5saM8/uxaMrAOlUyeDBySmS+NiN2o2bnj2/16HRQf1l7ba1PJ7umZuR9VEjoNuDAiPk01Abl1xKEM3jsfphLe5anz5Y7AU1t5cW/NZCLikdRn7BHU4ORHgHcBr6Wqn/YApizo45pA3TuDx+0AapTiN226+fPUi/fjmfkHGP0sz7ChD9lHArdk5suoEflt2l1eS63xeXP0sKZm6A10KbNqvtemLnC/RiuPG4ehx+pRwFaZ+UKqPO0vVPK7WmbeCKN9Doc+bJ9ErVF7JbVo/nPUKPO3M/Nfozr+3LTSyyWpsq8dWpnF3WJuDu0zpvbl/aiLtnMj4vUtoRp+nl6ZmRf1FdfcZDW5uJ4qP3sNdWL/PnBaS6RGIiLeRc3aXMasbnJvB149Ucv3qHPE5VRSPJl6vA5uCc37qYvzUS9Ofjjwzog4LSKCegw3oj6YbwGe3cOFydwM3nPvpi7mtmx/1gN+nZnXZk+d09q5YVWqJPtW6kL7PKoz4MbUjOtYDCXYj6cSl1dSXe/OzcwLW4n9v2F0r6Wh89ABwO+pxPtaqtT4osy8bFwVBcNyVtfX+1PNWp4AnNOWIJxEXYT24YfAIyLiyIh4ZUQ8nRogW6DlVvPSHofNqOR6ZyohGLzXz6e6Ah4Pd1uH1JvZBg+/TA1ePDMiDqTOUadTa7a+Q11HjFQ7BywP3NmqdP5JlYp/H9h2FLM984nnYmAXapLgpdTj8Gbg35n5buAtmbnAy2ZtY/5faFPdb8nWQrJdjO9B1X/f0uesQcxqRDCof3031U1qB6om9s3AOzJzz4g4PzMfN+J4DqHWEJzavv82dUK6iFpUf0GrtR6LmNVi9qXUB+01wHaZeWBLZp6amQf3FMtk6iT9OqqsawuqrOK8cSYCUWvX9qVKwh5AlVAdN+7kJGqdyprUBe9jqaYoywMHjbvEYlhE7EyNNu80dNuzqBHfl47ig7g9Z+dm5uMi4qvUAMqW1IDAuxf08RakiJhKPa+XUhfA61Br2h7cBjj6iGGwJcWO1AXVoJznR5n59z5imEdsk6hzwuPa98tRVQabAa/vK7kb+qw5AHgiNeh0APW4HZqZY20MBBARm1BrITalytfPpsr5Ls0e1rC1478uM18QEWdm5o5R3SVfCuwzjkGxuYkq9Z9MlbD/hkqGz8rMDUd4zLu1uY5qjLI19f7fiFqH/I1RHX8uMa1MlcZNpqqI7qSasjwK2CAzn91nPLPFNrhe2YsaEPhkVCOntagBiz9n5jHz/CULPqYVqRndR1Lr/3eJ2uD3R5n5qD5jmUNsG1JdaB9HfYYcmJk/XdDHcQbqXmovlGuobHvZdkL4FXWxskrfJVftA20xKqG7mVoDsTlVF3oRtW/At1pdeB9lKLdSa3kuidpD6LIWx3sy82lUycfYtJPREtTIzbJU8vL1dpHyDOrCqa/p+sdTJ51zqBHKm6jFu88fx0hlRDy0vZb2ycxPZua21EzUssDZEXHoGGIazNQ9jOoQ9WTgNuBD1IzhCdT6rInk18A1EfHM9uEMNXt2ZWbOGNFz+wjgN1Hds5bNzN9m5lHAk2JWg4SJ6ulUd7m/U7PTpwPvGiRPIy7fG6zXvKWNqH6CWjj+FWoA4YujOvY9sALwq6hOk1NbrJ+nLhJ6q3TIWXsffpZW5kQ9dzszMR4nWoncG4GnAJ+iStmPp3Wg7cHFwHUR8S1mrU27E1hsgiVPUzLzrMw8k+pa9n6qLGvUF+OD8/mHI+LjwLOosrDTgL36Tp7gP9sDHEHN7nyaugYYJAIfbfGOpXxvaFZzK+ozhMy8IGut6InMakjU2/VC1n6QX6Bm52+KiPdSs2Nf6CuGYRGxcUR8pg0IXJWZ76cGmF7HiLqTOgN1LwyNBqxB1VleTZWfbAyslZm7jSmuB1AjzpdSpQNnZeYdEXEYVUq0ZovxbaPIxucS00OoEqbdqZGl9wOfycxr+zj+POJ6ArB1Zh7WSgZeRpXwzaTq5l+TQxvZjjiWqVSJ1UbAGS2mXai1awu81fV8YlmMaof6QmqdxUupGY3b289XBe7IzL9Ej5v7Do16H0ddkOxNjX79CVh83LNicxO1XmVParBlOjW7+NbMPG9BPn6t9v3szPxSe22/nBqw+Dz1WD0kq15+Qhl6Xv+Hukh5CTVieDzV/vnDmXluj3G8nJrRuYVaiPzlzDw3IlbOMW6oOfSZsxF1Lr2OGn1eFpiUmQf0HMOrqEYuPwbOoAZ8bs8RbAx9D+Kb3AYmXkAlSstR77sfZ+YvotaSjix5iYj7UY1iTm0zwetR62lWpwZ7/gZ8Pavcf2yGXusPojp1PoGqgPgM9bqip1m6jan3/Lup65LBINPNFUJ95vQQx+B1/VhqucPLqBndE/s4/vxExPJZjaQGXYwfT72uj8paq9lnLIPHailgAyrJXJ6aPfwntf3DZX3Nhg/FNYmqkjmQum5ZgppN/Spw4aheS85A3QvtBXQAVbrwKuqksz7VWvId0GvjAdrxprSZp2WpsoX3UItBoS7OoVoZ56iTp5i1ee5K1AnpK1ltbrem2r5/eJTH7+gPVIvkd1CP2T7A0dTeCnuOOnmKWTXNS1MJ29HUB8f9IuIkaj3Bp0cZw1zMpLqfXUOdHJ9C7X/z2oh4EVXC9xfod31f+8BfjUoEPkC956ZRJ/GXxwTY+wnuNlO2dkTsQ40WXkTtZ/I94Lm5gLtgtmNeSC3g/x21dmEl6rHZg1pX9PEFcawR2p6avbiZSjj3oWr69xj1gYcuKNenBg8+T33wnkdt6LnOOJOngTaLOegstRz1ujqfHtchNq+m1shMo8pk/peasdi45zjuZqgkdi8q+d4ImEq9L75IlRiO0oOoWad1qMYVG1AzmQdSMxuvGHfy1AxmKYJ6LT2Dap70ceo9N9JBu4j4fFTjpocDR2c1JvoQ9Xl3EVWO1kvy1AxmEQ6juvGeS226vHPUnmbjNtigdgUquduQaoqyZ0T8JSLe0EcQcfe9DV9Bdds7l1pneDPVefpXfSdP8J/maTdl5tszcxfqnLgBNRs2snbqJlD3Qpvp2Y0aCf8k8PvMfHNW55Fp0O/FZTve9HYxvnlm7pOZmwL3b2+8j1KNG75CbQo56lhmtCTqZOoCctAtbaPM3G9cM3TDsvZ52oAaNXkFsGtm/gBYLiK+1mqNR2nw3nsJVX72a6rd9AVUycnLMvP7I47h/2knojtaDE+jFqSeQF2wPYNKrsZVynAHcF5EvJ1qe/tnarPFh2Z1nhy7off9B6hyuocDf6ee74sz8+YFPbiSmXdl5pFUK+tTqURt0IVsK+D9rbx4whkqdf419T68mPogPp3at+5yGO3rbSiGx1CltD9sf77Y4tl9VMeen6H/967UDMF21JrWR1Dlvsdla3Yz4jgGe/ZMop6XwcbQz83MHagBgj4veucoIh4BzGwlaYtTFQ8/oqoyftHuM5Iyp8z8LfBzqtvXNOpCd1dqPeti9NyRd26GzlHLU00j/pmZH8taq/lWRl8K/XnqtXMYcGhUI6ApmXl1Zn4lM48d8fHvpg2ePIDqlHgFNQP+zVZG+LBWITIWrez6+qzmP6+nZut2p86RO1GfLye1+476M3lyRGzZvt6Euh7YEPgVNdD17agufL2L6qB6ckQ8rc1E/yRrqcjPGeHm4iZQ90LWLuzbUyNcZ1Obiv4tInpZ6DwPU4CfR8T7WgKwanvjrU9tELsFlUiNzNCH05OAqzPzE5l5ebv9Qa00beyi9i+6qs1mvAzYos1ivJ0aTX1qm6YeiaHR0idQ5R0fAC7PzK9SHeaWGdWx52ZoVuz+VHJ5MLBGZn47Mw/NzF2pkVQYwwbILWE6lzpxL9kSqQ8ya7PjsbWXHRYRD6fWQR6cmW+hOk5OAl4cEYsv6MGVoffcG6jy2Ne089Mu1IX2/gvyeKPQEqYdgd0y8zMRcTi1qP2odpc+Xm+nA49vF3SDLon3p0ovx2LoPLEfdY54O3XheT7w3IhYflQJwWy2j+ruuhS1TuWp1ODTg4bed72Uhc/HdOoz8GnU+f1f1Gf0xllrNkbSfS9mraG7i5q9P4HqCPZTqrzx2dRs2ITQSrGXAj4UEQdGxKMjYonM/PmoB+6yOiK+ODMfTs02bwRcGhGntOetV+25+ys1wPxx6rrlr1Eb2q/dxwDF3GRtH3JW+/YLVHK7CXBgm8XbItueazn67oDrAx+OiNOo5SDbZOaMzPx81lKDZ1Kz9uPwK6qc+BXANyLigxHxPqoL38jKUU2g7qGImBIRL4+IDTLz35n56cxcl5r6/kO7z1ge15YsvZMqSdsK+EPUAs2fUZu0XpsjXns09OF0B7BiRGwbESu226+hLuomgp8Cy7Tp78OpUcpN2+2XAytma3c7KlG7v6+QmdtRJ/DBhe7r6XEn8Tl4B5Vs3wL8b0ScFLVw/YGD53cUFyHzEhHrRsSHMvNb1Ifut6gR769S5aowhqRuLv4GXBsRz46IpTPzj1Rb+sfkCFpNtxHUpan31x4RsWYb0f0e1c51pJsq3ltD5Y67RURQHR+XjerIdSTVxOTmmK1j16i0C6UDqffepyLip9RodK8j4gNRjRoGf19JVTrcmpl/zsxPUSVzfTUsejtVNvsYar+6s6kk7m1UM4m/jfp82UW7mPw0dTH1gIj4LtWw6IcwukGWwXPQBgi/TF2Mb0rNgh1DNU8aefXHPfBPas+1w6kYdwHeEdV8ZmSGBunWjupIuhHVIXh9avPVbUd5/Dlpz93mVDe7JwBPjirtfy/tvT+uwbmYtRxiR6r73qepQcxfUut8Vm4/H/kgSquu2pY6F/ycmjg4KSJeGtXU5kpqMLgXQ6+l+1GDvRtQFSnTqE6Kf6euh0dmgW8stQhYg3qiNo+IQbeoX1FlDT+E/vd+ylntyzekRro+lZnTovZQeRa1iPVYRt9Z5z8y8+w2irsL8PCoZhKbMmuD4bHKzD+2k+Su1CLoHaja75e1v/tYnDmdmkGBGtU9JCLuBP6UY2iKMHQhNhXYO6uW+QMRsSk1Ar41rVxgDK6hyiufRZWo/Ysaibu8xdl7Ujc3mXljRBxNlUA+Nmot4BJUl6v/LHZfwMe8LSLeTa1P2Q24rSXoD8kJ1Np9WCsJW5LqlnYANcOyLPX++xdtPekon9eYtSh6U+oCahnq9fUpYOnMvGxUx+7g/e119GtqEOyciDiBOjetCqycmVeMOoiWnP+MGrRYlyoTvwE4JiKmU+sffjPqOOYR3+A5fAhVEj0zMw+NiP2o8/ttVKeykY3SR8RKWfu+7Uutv7iJakayHzUL/JZRHPe/sDJ1zfInasDnH9Sg2UibgLRrlftTA0rfo95vXwK+m5lvG+Wx5yaq5fzh1IX3F6gkaiq1Dnmwb17vg3Pt2m5Gu456I/DZNiv2HmYtH7m+p1gmU8sxbgVWz8yD2vXTdlRH3AMj4uVZyyB6MfS5sBv1fH2FmlVdnhrs+dior8WdgeooIg6OiLUy88rMPJCqrf4Vtfj5o8BpOWsH9D4Njvcqan+ZVYDXtpPUOpn5WmoE+uOMuMXs0IjyyhGxCnVi/AG1oP33wAcy8+xRxjCf+AajOeu0EZ0HU12+jqDqsk+hTko7UiUYI9Uufq5pX/+Yeh19itqDYizaRchkICNi83axf2FmRtYO8X3GskxEfKQlTUtQzUf2pkZ4D6RKBh7T7jvWjSmHXvtLtcfwQVR3znOoWc1jqVmVkV3EtZmud1PNP5aj1ny8aRTH+m8NPV/PpWYz/kgtHj+Euvj8Q/t7pIbO2V+guls9nBpM2QvYJGoNae/auWo96gLzg9S59NnU83pCi7GXi/LMvC0zX0O9jv9EjTyfEBHPAU7OzDOybRw/Zu+jmiGc2r5/CtU2/AuZObKR8ajmI5+P2q4DalDnmsw8JTOfTg0cjr18b+jzbycqYXglVQ69HVUxckRmXjjC4w+uVXajOqMdlplvotZqLhURTxnVsecSz+ActB3w6XZd90FqNuPBVJfe6TC2wblBfHsA385ak7kfVcZ3F9XNta+KkNWoZRkfo6oc7gf8IzNPysz9qeewj+1x/iMijo+Il1GbrB+RtdTgZGprk22oMuORcgaqg1ZCsQmwX0RcSXVq+Wybkv98u89gj5W+938aXIxtSb25XkMtMPxLRDwuIk4HvtFTCcwg2387dQG5ElXecRJwY455of/QY3UUlajsCewaEddTzRuOp0r5/p0j6iQzNFq6GbW30pZt9vAsqiX+1vTQ6GMelqHWgmxEfdA9OSL+QLXE/2PPsSxLlVNuTc2kXgc8ENgzMy8fvuNEmX2iRp9XoS7+t6DKCF6WPbV3bqPgvba2vTeGnq9bqRmWJ1EDUlCv/yVGPXoY1Y77T9TFwRmZeUhLfh/W/mxEDar0ro08H0adzzelBsEua4lMr4ZmTJOqILiZej/uBLwtIt6fmcf1HddAO59OpkaePzu05uHbVLJ32ohL12+iyqoeRu0bdFabOTyPtp41M3uZKZiPwXtuL+DjWe35H0gl5m+kZs/+MqqDD72fb6HWhQ1u/3NE/I16nZ8+quPPIZ7B47ElMCMizm2vk59FxGXUeeE/n9l9xTUU3+CYvwX2jYitqcYxb2klz4OEeIFXNMzBP9ufx1HXSs8H/hURP6fOA0tn5jtGHMN/RG0T8FVqqcojga+2GbFTMvMfbdZ85FvlOAPVQdaGhXtS5TF/p05AF0XEaW0UjsHahnFcyEXEslS3qC2p8opT2o8eSa3lmTnqEfqhetQNgIdn5nOoMpONqFH497VRi7EYim9T6kPiZCpZeC01Nf0s6qLtgp7K5/YBfpPV3n03qg798Mw8nyqpGIusjVc/Q124ncisRGDlef7D0cRyE1Xq8QGqA9nV1En8lKg1WSNrT3pPtYu4pakPmNdl5huopOBsapZl7LNkE9RpwA3UTPVGUYvIn091wxr1etI9qQTpYGBqRDwmqxPYWdS2Ap/MEaxZ6yprMf9PgG9SpWjvbJUQT4yeOoMNlRGtTe0t9sc2I3V8Zu7d4vpmH7HMS7uA/DZV5jhYe/gXauDzhhEf+89Dr5k9qEGUFahqhrcw4nUYXQ3Ntv4O2CxqX6w/ZeZgu5P1egrleGoJxPciYo+WGOzMGF5HbeD781SFw1ER8ZWo5gMPylpv1HtH5Tk4lSqf/TXwrojYg7peGQyUjTy+zPx7Zn67xbIh1Vny6dQs73bUesjeZOb0Ntt0CLXx8UeoarAfR8Q5wKXZQ+dZN9LtIGZt0HcatTbk5ohYglpAezBwYGZ+bswxbkF1Z1qSuvh9FvCIrM5pfRx/sBbrlVRi8iOqNfgrWwnWtpl5UB+xzEuLZU1qtPsFmfnSdtG2RmZ+KnpYsN4upL9CdUz7ztDtX6T26eq1k83QrNj21EnocVS78q9n5hVD9f1jERHLZeYt7esVqOn5ranyoQvGFdfs2uzFu4F9sy2oj2qI8BngqRPgg3hCGHq93Y8aQbyNmgnamVp/cGJmfranWFalLgK2BdamylC+Dfw0e9hMdH7a6/2uzLylDf48iSo1fE9m/qjnWA6nBsN+zKwufEvkBNlwFKBdXG5NPUbTgPMy86OjGqUfei0vR11Y/iVbV7SIWJ1aB3XGuF9LUZvm/jkzb4+Ih1Glvb9kVofJ52XmyBo4DD1Oa1EbQF9BfdY8j5ph+W5Wg6CxidpncCcqOZhMldIf1/f7rMUyuOZ8CjUQ/vEW0wrUjOGv+rpOGIrlIGqG9RdU04+tqP0HXzXu6iK4W9nzNsD3B+/DUXIGqoP24rk/VSf8lHZBeUdWi+KjqSYEYxthjuq49VMqcfo1lZVfxqxNfUfeQWYo6fgJNT2/JjClHXsTxrjIeDZXUWUVtwN3tNm7p1InJphVd7zADZ6H9li9D3hWRGwa1TVtM6ru+uejOv48DJ671zNrr7BtgC9HxA/pb2TyP4bq9Z8KHB4RP43a1PBBmflN4B0TLHnaPjOvpj5cvhMRe7aLlpcAV+Z41kdOSEOJ5LHU7Oub25/bgJ37SJ6G3os3ULPRQc1I3UqdN48edQzziG2wnm47qrnG6yPiFdTA1IeoWZ8f9xDHkhHx2Zi198snqAXsb6YugnemreEcp4hYbOj5PI5KDrYCXpuZH213G/XgxWHUhe1ZEXFWRLy63f6dcSdPzQupMrXHU0s3jqP2Z7wfVSnyxlEefOg9fxhVfXG/zDyaWrv9tsz81rhn6DPz+lZ98VRqtvUGWhnfGGIZJPvvp66pBuXEJ1KfJ70Nsg7FshVVJXNEZr6c2o/qH1TjhrHLaqn+W2rZSC/Nf/xA76Bl4H+h6r83BnaIiN0j4s3AmoP66nGU77XjDhY6XpyZr8jM52fmsYNStB7qY4dj+Qm1782gdfL7qYWqp/UVw+yGLki2pPYu+Dg1crIS9UZbjJolgBGuYRs8D22maWlq1/X3UJsX7g+cmmNoBdxmDldvMf2WKsHclbr4v4LqiNb3AMHgA/cganr+J9Ro7kkRcRE1EjYhtJHLvaOazHyIuoB7EXWR8mfqokHcrZR2PWBK1n4wT6IGFDanEpmRG3ovvp96vq4H7qQWIz+JKsMat32p5hprUOtrdqM6qe7S02fNYtT2Ch+LiF9RCeb5wBWZ+e7M3I8x7v3UZlIGG0n/5zMuM/+SmX/NzGuGbhvJ45Wz1l9tkZmvox6fz1GNiH5CjdRPBCdR1SmvbH/WpaowvgYcmlU6PhJDn79bUwNgewK/i+pO+jbq3LnYuK6fZtdeT5dk5ruy58ZJw6K6KP8uM39IdQT8JpXoHhQRve4T2SquLgH2jFnbZPyO+kx+YJ+xzE97/np5LZlAddBmoJai1qbcSZVRrEfNVrwX+t8nYOhC5EWttIOIWDxqcV2vF7tDJ8hdIiKozmjrUKMlZwM7ZeZ1fcUzB4M301ZUAkzWHl4vpC5M3pgj3GQRICJWiYiHRq2TuQBYvNWe7051Z3tFu/gel+lU0vt44Ip2wrwDuDVr/4deBwhaUrcq9aF/NZXU7QQ8lqrh/yeMf11R1Ma411Mj8R+NiBuBh7aL8KdTSdRSMCFq6SeStYA7I2KrqPUqF1FNVJ4Bo31eh86d6wIbZebrqQGVvwJvj4it+yj/mJt2Ub4i9To6jlp/+GGq0cz1VHl0H3HclpmHZOYW1GDKStQ5NCLiVTGmDoVD3hMR342IN0d1wvuPNivV17lha2rft/WBZTPzeGod39eotcljl5m/py50D6EGM9ehyucOohLzUR57cN77I/D7iFg3a4PYv1IDl5t4bpxlqFLhWmDViLiaGlD8BDVDfntm3tpnRUObRT2CWqu9G/DyiDgCuH5wfbAocg3UPMTdO6a9hnoRX05dEJ2Ymb8Ya4BAROxOnQyPyjF2+mkJ5PlU0rQUlbRcSSWdZ41jZmVY+zDdj2oEMuikeGZm9tIyPCI+QrUiXpOqZ/4ddVF0bpvdHKuYtWfYtdQJ8ntU+cLfM/PgGEMnonaBtif1fD2DmgGeQpV87NFnLPMTET+iXl+Dzfv2o0qcHkdduLwyM/8+vggnloh4LrXeCWqLg2Wp/eouyJ7Wk0bEi6nn5hLgaVnrIbcGXpqZe/URwzxiW50qIzob2KuN2hMR38nMHXqMYwq1wH6ZzLy53bYtNRL+ucwc2ybNrfx6E+p99j/UoMoZwElZDWj6jOU51OO0HVX+uRmwXma+os845hDXJGrLh9Vp+9Jl6zDbkvSXA79sZdGjOP79qMfkrMz8ZytF3Z/6nPkeNet8emYeNYrjL2yiuiLunZnvj2qS9GeqMdjpEbEPtXXHpzPzG9FP973Z41uRml1djSpx/FWbiVokOQPVzeuo9ppvoupQ/w08L2qB71hl5pepGY13R8Tz2szBqLtX/cfQzNtmwDcz838z811Ut5alqA+2sXWyGmizJ1+i1g98jkpk3hsRn+gphBnUpsK7tz+HU3saHR8RH4uIh/YUx/8TtZHgu6jna2mqzexzqRHUd7W79TbSMvTa3YRqh78FNTvwKqq06trZ7jdWUXue3QjclLUfzuatrPYlVMepBzKGdWQT3Fep5/Jr1IXvetQi6VUi4ukxa1uIUcewGHV+H+yRtxu1uH6ssrYM+AA12/S3iDg/Ij5JNUYYuaH31pOphOCtEfHBiHgBNYj41HEmTwCZ+a/MPC+r4+VuLc6NgNOiNpUemaFZzPtFtZvfm9pD7GrgpdSs1Klz/QU9aZ97x1KDrGsC34+It0bE1FZ1cX+qA+aorE0NzH0kIt5EXUc9jlrL9wDqc/DoER5/YXMn8KWorpcfoSqcVoqIdagZ6Fdk5jeg36UZA5n5t8w8ITM/mplfWZSTJzCBmqectfD7n1SXmGsz86LMfCd1MhrbRS9AROzYPtAupk5MB9Km4/uaLRh6E+8NvKyNkpCZv24jye8Zxxt9YOiD7slUd52nUY0aPkitz/pa+/mo3wvvBjagFqi+lkpWdqUW1P6JETavmJu4+0aCn83aSPCTwBuoC7UlR13aOCftfbcptXZoGWoW8+9UeeEnGENSNx9/Ac4FfhER51H1/Y+gZu8upEbvx7ZWZKKJ1ukyq4XyoP3zm6lmDncC6+eI24e3GdVbqDU8f6VahH+IWlQ/lj2Nhs5Vy0XEk6hmFltQsz2fptasvLfnsF5MXchNoQZXHkvtoddLd9f5iYinRcQzgRUy8+vtHLY77TkcVWn90PlwN2qT3I9Sifcy1Hk+MvO7ozj2PdVmvt9JDYrtQyVTF0TEd4EH5mg7qP2OWuN7LLWP3/5U8rRahZY/Z+Kcx8cua+3eH6jE9hjqc2Vz6jPvHdReZ5ogLOGbi5jVlntFqrvOIdRalW9RH7jnUvXzY6ndHSpv2oRax3MGtRHdI6hOYG/OnjY+bcnHg6iR3MdRycEJ1O7ZP40eWoPPJa7Bc/hg6gLkDGph9qOoZOr0EX94zB7Pw6iypUupx2p94GfA+zLzl33FMYe4TqBmyA7J1hCllRxenJmf6bN8LyL2okZEn0IlIMdHLTZel0o8j83MsTUkGTb76zpqYe+6wAuoRP37tM1/M/PVc/wli7A5va5ayc9SmXnjKI/ZBp4eSg2kLEaNkt8wzovemNUu+HBqUGVLarb1/cCTM/PzPcezPPDVzHxK1BYee1CdEj9FNR64qs94huIaPIdPphKCGVRidyrVmOfHmTnSrq8RcTx1DfAo4Mi2ho+oNa5HUo/b2PfHgrt9Pi+emVcO3b4+ta/XP0d8/MWppPYxVJn/ddQayKdQ3VTHuXH8hDF0vbI68AWqcuFaqrPyb9r3X8vMy8d1TaW7cwZq/g6mup/sCzyE6mbzCWpmZdCBpzdDswa7AXdk5kszc3vg85m5e2ZuTM1GPb+HWAb/95dS5Y3vphpsXE3VVn8UxtedcMhgs8evUDW7d1EXJ3v2GURmXp6Zn83Mc7MaImxMJbsb9RnHsJj7RoKrZbV07W02M2bt4/BJ6mJtl4hYo43KXUBLRtp9x74p7eB1HRFT2kXdrVmb972duqD7C/Xc9rKn0UQWsxrNPDgi1ovaCuKu2Uv1sjZsHEny1H7/4Jz9WuA71ADUd6iZ4RVbjGN5bQ3N1G8AfIzacuGbWQ14to6eNs8dMgU4MyJeRK1DfAC1DnjdcSVPs3kO1cX0BGqG4wfUxrVbj/KgbV3YV6mBy0cCX42IF0TEMpl5GzUjde0oY+hiqKriaUACH27n9/2i1nXfOcrkaej64NXU4zSdWvd7ODWQ+GWTp1mGrpN2BH6QmRsya8Z5X6qR0uWz3VdjZAI1F0OzT48HHtxqrQ9uCcrLM/OL7X7jKk8btJilxfGnoYuRP1EzUSM19H/flbpIXI+a6fkJdZLcadQxzMvQSebH1GjzUczavftG2t5P41pL0y6435+ZXxjH8VsMd2bmt7Palr8I+C41k7lyRHwiZu0B00csM7L2VtuNKjlZmllJ3VupfbJObvcd6wdIRDw/IgYdHae3C/NJ7aLh9sz8FzWKuHdmTohOXGM2eL7eR73OHtBmGz8ZtZF1n7YHLsracmE/qoTw01RnqcXH+dqKWlf7K2r/p81z1kbbG1MJTW+yuqR9PDOPaTGdSQ1EndFnHHOI666WxCxHJb27UjPTX6POD5fA6M7r7f1+MlWV8jyqxHF74McRcQ5wSRtIGbfB6/gl1IDBBcBN1LXBZxjxusy8+/5B/0sNKL2IGlzdhOouqf/vImCxNst0TmYeQpXZfxD67/isubOEbx4iYg2q9nRbqqb6TGpn87G23IyIh1DlA38C3pKZZ8z28wdSu9ff3EMsq1ALsN9HrZ05LjOPjYjzgZcNShv6NjQdvgw18vVCaqbsgvb9RtQO2r91Ovzu2oXHulTN/MU53r0wBrvD7wgcn5knj/v5aq+pN1JlMbcA51HnhV5KZhc2s5XSnpKZm0fE3tR78gQqOXhzX+W0bSbnm9QAykmZ+Ya25uhF2brdjcPQ4/QIan+cwezwztR6xL17jGF1quPX84ELM/MVbcBgMWojz7F1lBwq4Vufaq3+KODR1GvprcDj+x7YHJo93wb4fo6xDf6wqE6Fx1MzGEcDz8zMOyPiOKp0fKRNSVr59Uepss9XAwdm5g1RLbA/kLX5+CJv6H23KbUe9JFUZ9KzqG6XPxxrgJojE6h5iIinUyfmf1CLHlcDVqFOPN8bQzxbUSVot7TE5XlUDfhiVEvQjw3XOPcY1/OpD/k/UBeWT6D2Vuqt3e7cRMSe1IXaZ6g9FFam6q9PzEW8g8zCJGoftsWy9r8YdwI1hWr6sRPVXn1pqjPnDdT78PysBgWLvOHnKiIeTQ1i/Jg6dwVwO/ClzNyqp3iWphLfG6gZnnOiuoNtBnwiM8/uI455xLdJi21Fanbs31Sji4sz87Iejj9ITg6j9u35N9VJ9VDg8MzcZ9QxdDH0HpxMfSa/gvoc/FtmHhZjaPHc4loMmDlRBuQiYknqHHUndS1zFXAO1cDhiT3FsA61n98TqU2hfwu8JDO36eP4C4Oh993rqbW/n2wD+C+mZu0uysxnjTdKzc4SvtnErE5Im1Btk6dSpRM3USefpDZd7LVWPqo9+SNb8nQQdTI8NjM3pRKE+1Plhr2XpGXml4D9sjakfCY1CndknzHMTSuPezN10XRdZh6XmYeZPC1csjY+vrV9PdaLk1bCcyfVXOar1LqLQTOCN1Lrt8R/SqEf3b6+gFpLsyM1Y30dNTL+A+itNGUzqvNmAmu22ajPAQeMK3ka/L8jYicqUdkS+BeVSP01M0/pI3mCu6133JjacPVxwNdbNcOdUftkjcXQZ/Mq1Jqnj1HvtYdS+/r9b2Ye1u4+liqRzLxr3OenYZl5O7X1yqntz45UU5Jzeozh91lNPb5ODQw8hOrEp2bofbcWsE5EPCAz/5CZ78zMdajuhZbvTTAmUP/fcGvnYzLzZdT6nguosq/JrTa879bOd1B7Bi1PjbjtSLXefRVVC/6SVqve26L/2eL7V/vyZOCwMZd9De/RsVRWq9TLgNdFxJui9liQ7rWobnFrAj/Pag7ya+rC5CKqU+eEaHQxbq3M6qSI+FlEHAJ8JjOfm9W+/DXUWsSPtLuP/LyVmedR5/aTqOTgK1Qyt8qojz0Pg//3q1osV1IlfI+ikrynjiGmT1NNdtbIzDPbDPAGLbZxGbyfdqGaNHyYWufzBGpAcdvBHSdSEtO3oc+/B0fEe6g1Ry+gZoE+TW058r6+42rlevtTnfdO7vv4E11bw34S1Sjp8Ig4PCJ2ioilM/NPMNY195oDS/jmIqq183TgdXn31s7TMvOz0WNr57nEtzzVbWgLal+HH2brmqYSES8BXgn8niqtegJVRvD2zDxijKHpPiAiXkg1vMh2kbkFVQb26DGHNuFExOuoUpQ1qM6TJ1C1/bf3GMPS1MXJNW1mbDEqiVufGvS5qq9Y5hDbFGpG5RSqOcGXMvOoiPgq8Knsob360DqMHam9D/elGgB8hZrBn9EGFMcqIv4XOCfbdgYRsRz1WXhLZv5w3CW+4zb0PA4a75xKve6Xp5LQSzPz9HHGqHmLiMEG8jtQaw4/OOaQNAfOQM1BzGrtvCT/v7XzZ2E8szwRsUREvDYiTgIOAq7O2tT3rbTOSI56381VtHapVMONt1EJ1aBkyNe/7pV2jvgK1bXwQxHxG2rE/pPt55ZacLf32BbUmqMVqA6d+wB/jYg+10FsRxuNj+ouOZlqYf6XcSZPUGWhwDeorSC+D5wc1Wr6oX0kTy2GQVL5AuCCtt5pX2oT649QW3qMVWtKsDXwtaguoRtk5i2Zefpgof2inDzB3f7/f65v81vAiVTZ3k3tjyawzLwuM0+lGm98Cry2m4icgZqPoS5gz6E+cC+hOs39qMcYBgsM9wU2pEYpP0qdCJenNvI7uq94JrKYtRnl1sCTqeftsGxt56V7a+h9uCZ1kfkMqsz3UxGxMrX4d/p4o5x4ImKwt9c+gyY37WLgKOCg7KmjW+tutwk14zS4GHk8taH2e+f278YhIh5Pbe57Wit37Ou4/wN8CTgqM3sv85qX4aqPiHgosBe15nYJqsHFMWMMb0Jpr/WzqPXar87MS4Z+tkjP0EkLiiPw85GZ17fSuKdSo5c3UN34xuEp1N4yW1Lty58E/JJq5eqMShnMDL4LOIaabZoUEU9vI5aLz/2fSp28EbiGWhT98JYgHE5dyGlIu+i9lGq28bKI2C4iHkethZiSmX8f5cjqbL/7H9Qata9SM4fnUZ0APzKq498Tw+emrM22X91n8tRcQVVf7B0RP4mID0XEE1t8Yx0Bb4MXj2sDic+hWuJvArweuBn8DBz6/99EbaB7KXBaRJzbqleWGV900n1LrxvzLczayNcl1IV578dui3h/Se0NsHb7euC09vciP6rUylDuRy1Ov4la6P+NdqH2IibILvFa+AyV7S5H7Qn3Lmo2+tJ24bI+tb5HzdBjdi7VLW3n9v2K1IJ2qNmgUZ27FgNmRMShVIerDYHfUYlU5hjbzUfEA6jNRbcHrsjMK9rtvbbgHlozcz9qw9UHUjOGqwK3Ae+LiJe3Loq9G5r53Zbaa/AHwN+A10TE9zPzqMF9x7kueSJoj9NiwDfbWswLgYOitmQ5hCr7/+pYg5TuIyzhm+Da6N9PM/Mf7cS4DrXJ6W7UFP0bMnPDccY40bSR0p2p0eUbMnPviNgQODozNx9vdFqYtffg84GnUwnBjtRmuj8HdsxM1xdwt4veLahtFgYJwe3AFzPz4h5jmUyt6fmf9v2mVPOICzNzLO2U2zlqe2rDzL2orRbObt1WiYgVsqeNhYdi2hd4DNVJdUmqlfk/gS+M83U9lOB9Hvh2Zp7QZlIeSVWFfDozzx9XfBPF0HtuHepx+TVVAuo5SRqBRXq6eyGxL3BxRHwR2DkzL2uLQn8PrE5ttuiide5WvvB4ar3aqsDuUbuuH0Z1ufKx0r2Wtc/LF6mZpquBN1GL/7+emTct6iVEQwYjc28CplGt3U8DlqLac/dZbrU2cHVEbBQRUzLzwszcE9hpjCW9k6hyubWpzb0fA0REPC0iNgJOiYj79xFIRBwfES+jNsw9IjO/1dpMf5hq9b5FH3HMzdB6nR8BK7Tbbs3Mn1Kvs2Vh/CWG4zY0+/Y44GFUu/fnRsQTImK9VsUiaQFxBmoh0NZYfAJ4BDWCexrwyazN6TSkjS5/jCpx/AO1buw3VDv6W8cYmhZy7YJ/O2pU/mqqHGwStc7g5sy81QXas7SSsJMy84lDtz2S2nj44My8rsdYng48lupwtwTVTGKdzNy3rxjmpJ2vVqI6pu1Ira9dBlguM0e+IXNU+/SdqXblj6ZKnN9BrS+6NSK+BbwxM3816ljmpzV0OpEaHPse8Ctq/dMWfZY8LixaI6VdqOf0TuBtmfnH8UYl3Xc4WjqBDY2OvoDqtLc61UBiBjAtIj42tuAmmIjYK2qD3MdSaxteASTV7n1NqlxGuseGZkqeR72eXk+VXD0E+Cvwp0FybvI0S+uud35EnNjahgP8kWrNPdLkKWZtJrpCRLyBapu+ETVjvyOVpIx1b5X2uvo7NVN+C/ABaqb8tcCL+4ghM6e32aZDqNf3R6hz5Y8j4hxqz6CJkDw9DFgxM7eiSrMnAesBe7Suq4v0tcygqiIito6It0bENGBmZh4CHACcafIkLViL9ElnosvMO9uXawKrthrnGzPz1dQo4XfAkrT2/x+0St4DeGZErNH2B7mIKnd8YLvvIl3moXtlkBRtQY3G7wocCSwNvA7YfVyBLQTeRc2Y7x0RF1Ozw8fCaM9bQ4ns7lRzj29TycraVMnXCZn521Edf16GLvafQ+37tDbwceAz1GzQekPn/l5k5szWcfZIqkPi84AvUoNQYzGUBK9Bbdvx6IhYBXgJcDHVuvw3YPOIoRm491L7Hl4FrBQRewB7ZeZXxhWbdF9lF76Fw0eAV1Drea6jShieTFvTs6iXL7T//1siYgVqhu7l1AbIf6HWX6wBvLPd1xkC3SNtAfuK1OzmzIh4W7v4/i3wiYhYDtxfBe624H9lqiTs+VTJ41eo9+DkzLwGRnveiojjqc5/6wMfzcwLgc+0cuiXAttSi+zHaVdqFmxHKsG7jlrz+kDG2M2xPS+/jYhLGG9n10F3xucDP8jMYyLivdTj82yqG+YnxxjfhNISzT9R5cWLA2dk5p0R8YWIOL6v/dakRYVroBYSEfEoYG9q1nAJ4LeZ+bEY2lxQs8SsDZB3BI7PzJO9wNW91ZKkPahy2ruoxPzrmXnmWAObYIY6gb2HWtvzDeo9+CTgPZl5fA8xDK/r+R9qBv+dwFcnyjrI1kXuC8CewPHAqzLz6va4fbzP9WETXUS8m0qWVgCuz8w3RcT+wIMy8+1+BpaIWJoqUd0MuC0zD4iITajGINuMNTjpPsgEaiETEctm5r+GvjcpmIfWeWgxF/hrQYmI9YEdqBKsL2TmZ8cc0oQTEWcC+2TmH9r3a1Brx96dmdf3FMMkan3Rs6jOZKtSo/NHZeZ5fcQwj9gWozY6XQzYgErIv09tWfGIMYY2IQzNZC5L7Rn2amo2cU+qvfpbgQMz83eL+nk9IpZpn28bU10uX0vN3F0CbAqcPLxXlqQFwxK+hcxw8tS+X2Q/OLrIzH8Pfe1jpc6GZlMeDLyMajxwCnBRZh4REZ8Zuu8ifRE3MDQb8FNqBujLAJn5hzaLfntfsbTn43ogI+KTzNo/b9W+YpiHxYFz2teTgC8B5zHGNUcT1FuA32fmawFaaejmVJfA38GifV5va8K2iIjNgPUzc4+IeAk1wLMYVSL6z3HGKN1XOQMlSXMwlEAdDtwBXE6VxyzZvv6WWwmUoRmDAI6hkpQEbqYShY2AFTLzRYtqydXQY7QW1UluR6oU+/kRsQQ10/LnRX1N67CI2I+aofsocF3fzTUmupZAbQe8nRosOB64lmrz/iTg4Zn50fFFKN132YVPkuZg6CL//tS6lGMy81VUU5clgQeBnR3hP402Fqc6yg0W+D+HupBbj9q/Z79290X18Rr8v19ONbB4K/C3iFgV+Dpwh8nTLBGxJpUcbEQ113h6RKzf1wbDC4PMvCkzT6ASp/8F7kftffhOKqm6ZYzhSfdpzkBJ0ly02YJTqcGmd1GlQ3eMNagJKCKmZOb0Vj70AuDBVOnV09vPHwE8EfjUov74RcRHqVbu+1Ht1L8XEUdQM5rfHm90E09rCPRCaiZqOaoZyInjjWr8hmbIp1Drw/5NbdmxErXJ93XAFYvibK/UB2egJGkuMvMqYBfgs+3vb0bEZ9uCbTWZOb19+Tzg9Zm5PvCHiNgoIp5H7d2zHbWX1qLuZOBg6rG4pq2xezTVSEJARCwREftGxPeAQzLzA5m5D/Bp4HftPovqTObsPkUlmK+kHp9dgZsz83LG24Zeuk9zBkqS5mDQxW3QNa51TtuAukA5KzPPs3nELG1j3HdSi9a/AJybmetExHHU7N2rqf18vjjGMCeEiHgO1R3wRuChwPmZ+f7xRjV+Q7Mq+1GzKt+lXje7AMdm5m5jDXCCafvTfZ0anBicn7YGtgHe5RpNaXRMoCRpSERMzswZEfEMakZlHeBH1IXKBcBdmXnbOGOcqCJiI6p5xK3tz5eBfTNzh4j4PvDURfWxawn53tQ6lW9SG6IvQZVd/dv1T7NExMepJHxr4NbMPLLtkXVeZn5zvNFNHG0mfD/gaGBaZt7ebl8uM13/JI2QJXySdHeDNQMHAG8Cfgw8gGqpfC61OavmIDN/3TbtfBrVje95wD/bBfE1i2Ly1GYuoR6LvYBNqCTzYOBRwMomT//P2VQzhOdTAxdQj9UtYPleRBzcvtwDWIVq2PKEiHhERKxs8iSNngmUJA1pHeVWoWYIbgIekZl7ZuYTqdmCm8CLuGFtITsRMaU1lJiRmadk5rOphgnXAItkidrQIv4HURsJ7w08AziTKgfdZ0yhTWSnUF0LbwKeExHHAH/LzHNgkd/7aVngRxGxNPBwahZzQyqZ2gN4QUQsN8YQpUWCJXySNJuIWIlZa1R2pkpk/gq8tyUFGhIRTwL+mZk/ad9PAhZrpZCLL6r79wzt/bQ4NaNyELU25ftD91liUe9MOCwitgNWo7YKeBS1v9F3gD9k5k2L6j5is4uIpYDHUCXGj6A6X64K/C4zXzbO2KRFwZRxByBJE01m/jUifk+V7K1CrTNYGbgQZq2TGmOIE81twAsjYgOqNfetETETYFFNnppJVCe0VwFbUZ32Plb7DfMd4BOZecX4wpsYhhLNTYC3UTO9lwM3UKWfvxjc1+SpZOa/gXMi4jzggVTTjccCPxtrYNIiwhI+SWLWWpWI2CAiPkKtWZlCdQK7EngD8MF2dy/ihmTmecBHqQYJz223LfKP0dBjsAbwhsx8VWZuTLV1X50qudKsTYa3Az6dmftSLbl/CrwoIp41tsgmuMycnpnXZeb3gA9R5ytJI+YMlCTd3YuBqzPzw/CfNQerZebvBuueFuU1GANDLacfBywNrElt3rlLROwMHJaZFy7qJVcRsQI1i3lEa6bxncz8GbD7eCObOIZeH1sC0yPiB5l5LXBWROxENXFhUX8tzU+blZLUA9dASdKQiDgC+E1mHjl023HAl2yhfHdtbc/JVHv3f1Jlav9oX/8L+GRmXjO+CMev7Y+1GvBEYD1qncp1wMnDpWmLuvZaehKwL7A8tebwSmDtzHzeOGOTpNmZQEnSkIh4JPAaqhTmOmqm/m3AszPzz+OMbaIYmn16ArBUZp4+9LMlMvOOiNgeeBnwokWxffmctCRhbWAH4NLM/M6YQ5qQImI1YCeqPfdk4BLguMz80VgDk6TGEj5JGpKZF0fER6lSvklUN7DjM/PPg8Xu441wQnkmtdD/9KHbBk0jbqY2QTV5alpDjUvbH81FZl4PfCYiPgesS62rW228UUnSLM5ASdJcRMQymXnr0PcmUE1bG/Zaapbp21Qp31mzPV4rZubfxhOhJEmjYQIlSeqsleb9LDP/ERFLABsB27a/VwZ+mJmL5Ka5kqRFg23MJUn3xH7AxRFxLLBTZv48Mz8EHEi1nv45zGoLL0nSfY0zUJKkeyQiHg58HHgE8G/ge8BRLvKXJC0KHCGUJHXSusgBPB84MjNXBx5HtS7/YWu+IUnSfZoJlCSpk9ZFDmrT3FVbO/MbM/Ng4J3AWfCfvY8kSbpPsoRPknSPRMTGwCuAs6m9siYD7wKenpl/HWdskiSNmjNQkqR7JDMvotZAbQE8i9rw9MuZ+VebR0iS7uvcSFeSdI9l5q+AV0fEspn5r6EfWdYgSbpPs4RPkiRJkjqy1EKSJEmSOjKBkiRJkqSOTKAkSZIkqSObSEiSJpSImAmcC0wCVgROAQ7NzLt6OParM/PDIz7G/YBdM/OoUR5HkjQazkBJkiaiJ2bm44HNgIcCb+3puK/q4RgrAXv1cBxJ0gjYhU+SNKG0GajFM3N6+/7+wKXAmpl5a0S8FXgisDRwema+PSLWAj4E/B1YD/gLsGdm/i0i9gP2BmYASwL7ZeZFEXEoMB3YATgKeDHwWODHwP7A84Hbga2pJO4V7bg7ADdQs0i3R8T6wIeBZdoxDszM30TE0cAvgacDU4H3tt/9JWD99rNdMvPvC/YRlCSNkjNQkqQJLTP/AvwBeHhE7AGskplPoJKdjSLiKe2u2wPvycytgF8Bh0bEJGB1YLs2o/VG4LVDv/75wLMz85j2O2/IzCdk5iXt55sBOwHPBr4CfCMzNwf+CewQEVOA44D9M3Nb4DVUMjawKbAjsG2L7Upgd+CX7TgmT5K0kHENlCRpYTAFuAt4BrB+RHy/3b4CNZvzO+BXmXlZu/0E4IuZOTMizgZObDNZiwO3DP3e72Tmn+dx3DMzcybw24i4MzPPb7dfAqxMzXatBxwbEYN/s3JETG5ff6X9+7+2mTVJ0kLOBEqSNKG1xGc14DJgMnBYZp4w233WoppODEwC7oqIVahk6umZ+bOI2Ag4Yuh+w8nUnNw59PW/Z/vZpBbPtW32ava4AW4dusmqD0m6D/BkLkmasFqJ3EeAT2XmrcDpwPMHMzwRcXREbN3uvllErNu+3gX4PpV4/QP4RSvn228+h1x8aPaoi98BS0TEo1s8G0fEyfP5N7cC978Hx5AkTSAmUJKkieisiPgBlaD8BXh7u/1zVPnc+RFxHnBJZv6w/ezXwNva7VtQrc8vAr4F/Az4bvt7pXkc9/NUsvWyLkFm5p3AbsAHWlnh/wKvm8+/+RNwYUT8PCKe2eU4kqSJwy58kqSFXivhO3pOpXSSJC1IzkBJkiRJUkfOQEmSJElSR85ASZIkSVJHJlCSJEmS1JEJlCRJkiR1ZAIlSZIkSR2ZQEmSJElSRyZQkiRJktTR/wHRc/e6wBkT+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='department', data= order_products_desc, order = order_products_desc.department.value_counts().index)\n",
    "plt.title('Most Ordered Departments')\n",
    "plt.xticks(rotation=70)\n",
    "plt.yticks([2000000,4000000,6000000,8000000,10000000],['2 mil','4 mil','6 mil','8 mil','10 mil'])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Department')\n",
    "plt.savefig('Images/ordered_products_by_department.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,7))\n",
    "sns.countplot(x='aisle', data= order_products_desc, order = order_products_desc.aisle.value_counts().index[:20])\n",
    "plt.title('20 Most Ordered Aisles')\n",
    "plt.xticks(rotation=70)\n",
    "plt.yticks([1000000,2000000,3000000,4000000],['1,000,000','2,000,000','3,000,000','4,000,000'])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Aisle')\n",
    "plt.savefig('Images/ordered_products_by_aisle.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge user orders\n",
    "\n",
    "Output: merged_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "        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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26088</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Aged White Cheddar Popcorn</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>popcorn jerky</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14084</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Organic Unsweetened Vanilla Almond Milk</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>soy lactosefree</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26405</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>XL Pick-A-Size Paper Towel Rolls</td>\n",
       "      <td>54</td>\n",
       "      <td>17</td>\n",
       "      <td>paper goods</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12427</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Original Beef Jerky</td>\n",
       "      <td>23</td>\n",
       "      <td>19</td>\n",
       "      <td>popcorn jerky</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0   2539329        1    prior             1          2                  8   \n",
       "1   2539329        1    prior             1          2                  8   \n",
       "2   2539329        1    prior             1          2                  8   \n",
       "3   2539329        1    prior             1          2                  8   \n",
       "4   2539329        1    prior             1          2                  8   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_order  reordered  \\\n",
       "0                     NaN         196                  1          0   \n",
       "1                     NaN       26088                  4          0   \n",
       "2                     NaN       14084                  2          0   \n",
       "3                     NaN       26405                  5          0   \n",
       "4                     NaN       12427                  3          0   \n",
       "\n",
       "                              product_name  aisle_id  department_id  \\\n",
       "0                                     Soda        77              7   \n",
       "1               Aged White Cheddar Popcorn        23             19   \n",
       "2  Organic Unsweetened Vanilla Almond Milk        91             16   \n",
       "3         XL Pick-A-Size Paper Towel Rolls        54             17   \n",
       "4                      Original Beef Jerky        23             19   \n",
       "\n",
       "             aisle  department  \n",
       "0      soft drinks   beverages  \n",
       "1    popcorn jerky      snacks  \n",
       "2  soy lactosefree  dairy eggs  \n",
       "3      paper goods   household  \n",
       "4    popcorn jerky      snacks  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now merge the orders with the ordered prodcuts, combining on order_id\n",
    "# This is a lot of info. We will trim some out before modeling.\n",
    "merged_orders = pd.merge(orders, order_products_desc, on = 'order_id')\n",
    "merged_orders.head() # Because the products showing are from the 1st order for this user, they all have Nan in days_since\n",
    "# Because pd.merge defaults to an inner join, the test orders are not in this dataframe because they had no products."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33819106"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_orders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>39190</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Vanilla Unsweetened Almond Milk</td>\n",
       "      <td>91</td>\n",
       "      <td>16</td>\n",
       "      <td>soy lactosefree</td>\n",
       "      <td>dairy eggs</td>\n",
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       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16797</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Strawberries</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh fruits</td>\n",
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       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9387</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Granny Smith Apples</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh fruits</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21903</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>packaged vegetables fruits</td>\n",
       "      <td>produce</td>\n",
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       "      <th>300</th>\n",
       "      <td>1374495</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39922</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Celery Sticks</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>packaged vegetables fruits</td>\n",
       "      <td>produce</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1402502</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18599</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Garlic Couscous</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>instant foods</td>\n",
       "      <td>dry goods pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>1402502</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21903</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "      <td>123</td>\n",
       "      <td>4</td>\n",
       "      <td>packaged vegetables fruits</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>1402502</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>47766</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Avocado</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>fresh fruits</td>\n",
       "      <td>produce</td>\n",
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       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>1402502</td>\n",
       "      <td>3</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24810</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Organic Lightly Salted Brown Rice Cakes</td>\n",
       "      <td>78</td>\n",
       "      <td>19</td>\n",
       "      <td>crackers</td>\n",
       "      <td>snacks</td>\n",
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       "    <tr>\n",
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       "      <td>23650</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Electrolyte Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>water seltzer sparkling water</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "296   1374495        3    prior             1          1                 14   \n",
       "297   1374495        3    prior             1          1                 14   \n",
       "298   1374495        3    prior             1          1                 14   \n",
       "299   1374495        3    prior             1          1                 14   \n",
       "300   1374495        3    prior             1          1                 14   \n",
       "..        ...      ...      ...           ...        ...                ...   \n",
       "379   1402502        3    prior            12          1                 15   \n",
       "380   1402502        3    prior            12          1                 15   \n",
       "381   1402502        3    prior            12          1                 15   \n",
       "382   1402502        3    prior            12          1                 15   \n",
       "383   1402502        3    prior            12          1                 15   \n",
       "\n",
       "     days_since_prior_order  product_id  add_to_cart_order  reordered  \\\n",
       "296                     NaN       39190                  5          0   \n",
       "297                     NaN       16797                  4          0   \n",
       "298                     NaN        9387                  1          0   \n",
       "299                     NaN       21903                  7          0   \n",
       "300                     NaN       39922                  8          0   \n",
       "..                      ...         ...                ...        ...   \n",
       "379                    15.0       18599                  2          1   \n",
       "380                    15.0       21903                  4          1   \n",
       "381                    15.0       47766                  5          1   \n",
       "382                    15.0       24810                  6          1   \n",
       "383                    15.0       23650                  3          1   \n",
       "\n",
       "                                product_name  aisle_id  department_id  \\\n",
       "296          Vanilla Unsweetened Almond Milk        91             16   \n",
       "297                             Strawberries        24              4   \n",
       "298                      Granny Smith Apples        24              4   \n",
       "299                     Organic Baby Spinach       123              4   \n",
       "300                            Celery Sticks       123              4   \n",
       "..                                       ...       ...            ...   \n",
       "379                          Garlic Couscous         4              9   \n",
       "380                     Organic Baby Spinach       123              4   \n",
       "381                          Organic Avocado        24              4   \n",
       "382  Organic Lightly Salted Brown Rice Cakes        78             19   \n",
       "383                        Electrolyte Water       115              7   \n",
       "\n",
       "                             aisle       department  \n",
       "296                soy lactosefree       dairy eggs  \n",
       "297                   fresh fruits          produce  \n",
       "298                   fresh fruits          produce  \n",
       "299     packaged vegetables fruits          produce  \n",
       "300     packaged vegetables fruits          produce  \n",
       "..                             ...              ...  \n",
       "379                  instant foods  dry goods pasta  \n",
       "380     packaged vegetables fruits          produce  \n",
       "381                   fresh fruits          produce  \n",
       "382                       crackers           snacks  \n",
       "383  water seltzer sparkling water        beverages  \n",
       "\n",
       "[88 rows x 15 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_orders[merged_orders['user_id']==3] # We see 12 orders, 88 products, no test order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "    order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "26   1374495        3    prior             1          1                 14   \n",
       "27    444309        3    prior             2          3                 19   \n",
       "28   3002854        3    prior             3          3                 16   \n",
       "29   2037211        3    prior             4          2                 18   \n",
       "30   2710558        3    prior             5          0                 17   \n",
       "31   1972919        3    prior             6          0                 16   \n",
       "32   1839752        3    prior             7          0                 15   \n",
       "33   3225766        3    prior             8          0                 17   \n",
       "34   3160850        3    prior             9          0                 16   \n",
       "35    676467        3    prior            10          3                 16   \n",
       "36    521107        3    prior            11          0                 18   \n",
       "37   1402502        3    prior            12          1                 15   \n",
       "38   2774568        3     test            13          5                 15   \n",
       "\n",
       "    days_since_prior_order  \n",
       "26                     NaN  \n",
       "27                     9.0  \n",
       "28                    21.0  \n",
       "29                    20.0  \n",
       "30                    12.0  \n",
       "31                     7.0  \n",
       "32                     7.0  \n",
       "33                     7.0  \n",
       "34                     7.0  \n",
       "35                    17.0  \n",
       "36                    11.0  \n",
       "37                    15.0  \n",
       "38                    11.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders[orders['user_id']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1376945</td>\n",
       "      <td>11</td>\n",
       "      <td>test</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1356845</td>\n",
       "      <td>12</td>\n",
       "      <td>test</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "38    2774568        3     test            13          5                 15   \n",
       "44     329954        4     test             6          3                 12   \n",
       "53    1528013        6     test             4          3                 16   \n",
       "96    1376945       11     test             8          6                 11   \n",
       "102   1356845       12     test             6          1                 20   \n",
       "\n",
       "     days_since_prior_order  \n",
       "38                     11.0  \n",
       "44                     30.0  \n",
       "53                     22.0  \n",
       "96                      8.0  \n",
       "102                    30.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the test set of orders\n",
    "test = orders[orders['eval_set']=='test']\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vanilla Unsweetened Almond Milk                    10\n",
       "Organic Avocado                                     9\n",
       "Organic Baby Spinach                                8\n",
       "Unsweetened Chocolate Almond Breeze Almond Milk     5\n",
       "Granny Smith Apples                                 5\n",
       "Garlic Couscous                                     4\n",
       "Organic Peeled Whole Baby Carrots                   4\n",
       "Organic Whole String Cheese                         3\n",
       "Crackers, Crispy, Cheddar                           3\n",
       "Strawberries                                        3\n",
       "All Natural No Stir Creamy Almond Butter            3\n",
       "Organic Lightly Salted Brown Rice Cakes             3\n",
       "Electrolyte Water                                   2\n",
       "Original Rotisserie Chicken                         2\n",
       "Wheat Gluten Free Waffles                           2\n",
       "Chocolate Ice Cream                                 2\n",
       "Green Beans                                         2\n",
       "Brussels Sprouts                                    2\n",
       "Birthday Cake Light Ice Cream                       2\n",
       "Cucumber Kirby                                      1\n",
       "Crunchy Almond Butter                               1\n",
       "Dried Sweetened Cranberries                         1\n",
       "Organic Baby Carrots                                1\n",
       "Organic Strawberries                                1\n",
       "Whole Almonds                                       1\n",
       "100% Recycled Paper Towels                          1\n",
       "Part Skim Ricotta Cheese                            1\n",
       "Chopped Walnuts                                     1\n",
       "Organic Shredded Mozzarella                         1\n",
       "Feta Cheese Crumbles                                1\n",
       "Blueberry Pint                                      1\n",
       "Celery Sticks                                       1\n",
       "China Green Tips Green Tea                          1\n",
       "Name: product_name, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I can see everything a particular user has ordered, and how often\n",
    "merged_orders[merged_orders['user_id']==3]['product_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(merged_orders, open(\"Pickle/merged_orders.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "See separate notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Surprise recommendation model\n",
    "\n",
    "The name SurPRISE (roughly :) ) stands for Simple Python RecommendatIon System Engine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for recommendation system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ok so I need user_id, product_id, and 'number of times it was purchased'\n",
    "user_item = merged_orders[['user_id', 'product_id', 'reordered']] # Maybe I can use the reordered flag to increment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>26088</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>12427</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>10258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819094</th>\n",
       "      <td>206209</td>\n",
       "      <td>16168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819098</th>\n",
       "      <td>206209</td>\n",
       "      <td>24852</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819099</th>\n",
       "      <td>206209</td>\n",
       "      <td>39216</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819100</th>\n",
       "      <td>206209</td>\n",
       "      <td>9405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33819101</th>\n",
       "      <td>206209</td>\n",
       "      <td>6846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19955360 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  reordered\n",
       "6               1         196          1\n",
       "7               1       26088          1\n",
       "10              1       12427          1\n",
       "11              1         196          1\n",
       "13              1       10258          1\n",
       "...           ...         ...        ...\n",
       "33819094   206209       16168          1\n",
       "33819098   206209       24852          1\n",
       "33819099   206209       39216          1\n",
       "33819100   206209        9405          1\n",
       "33819101   206209        6846          1\n",
       "\n",
       "[19955360 rows x 3 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# About 20 million duplicates.  That is a person ordering an item more than once.\n",
    "user_item[user_item.duplicated(subset = ['user_id', 'product_id'], keep = 'first')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196      11\n",
       "12427    10\n",
       "10258    10\n",
       "25133     9\n",
       "46149     4\n",
       "13032     4\n",
       "26088     3\n",
       "26405     3\n",
       "49235     3\n",
       "38928     2\n",
       "13176     2\n",
       "39657     2\n",
       "41787     1\n",
       "27845     1\n",
       "10326     1\n",
       "17122     1\n",
       "35951     1\n",
       "30450     1\n",
       "14084     1\n",
       "Name: product_id, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ah this is what I need.  The value counts of each product id for each user.\n",
    "\n",
    "user_item[user_item['user_id']==1].product_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Started at 8:51pm, ended 11:13pm\n",
    "\n",
    "# Creates list of data tuples of user, product, count\n",
    "\n",
    "# data=[]\n",
    "# data_columns=['user_id','product_id','count']\n",
    "# for user in user_item.user_id.unique():\n",
    "#     counts = user_item[user_item['user_id']==user].product_id.value_counts()\n",
    "#     for i in range(len(counts)):\n",
    "#         data.append([user, counts.index[i], counts.values[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Began 11:13pm, ended 11:24\n",
    "\n",
    "# Makes dataframe for recommender\n",
    "\n",
    "# Now I am consolidated down to 13.8 million rows\n",
    "\n",
    "# rec_df = pd.DataFrame(data, columns=data_columns)\n",
    "# rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(rec_df, open(\"Pickle/rec_df.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13863746"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df = pickle.load(open(\"Pickle/rec_df.p\", \"rb\"))\n",
    "len(rec_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>10258</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25133</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>46149</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>13032</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>26088</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>26405</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>49235</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>38928</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>13176</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>39657</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>41787</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>27845</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>10326</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>17122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>35951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>30450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>14084</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_id  product_id  count\n",
       "0         1         196     11\n",
       "1         1       12427     10\n",
       "2         1       10258     10\n",
       "3         1       25133      9\n",
       "4         1       46149      4\n",
       "5         1       13032      4\n",
       "6         1       26088      3\n",
       "7         1       26405      3\n",
       "8         1       49235      3\n",
       "9         1       38928      2\n",
       "10        1       13176      2\n",
       "11        1       39657      2\n",
       "12        1       41787      1\n",
       "13        1       27845      1\n",
       "14        1       10326      1\n",
       "15        1       17122      1\n",
       "16        1       35951      1\n",
       "17        1       30450      1\n",
       "18        1       14084      1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_df[rec_df['user_id']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Hmm. No one bought anything more than 100 times.  Is that coincidental?\n",
    "\n",
    "rec_df['count'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      8299266\n",
       "2      2296606\n",
       "3      1074345\n",
       "4       612369\n",
       "5       384418\n",
       "        ...   \n",
       "94           5\n",
       "97           4\n",
       "99           4\n",
       "100          3\n",
       "98           2\n",
       "Name: count, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8.3 million user/product combinations happened only once\n",
    "\n",
    "rec_df['count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In case I want a smaller subset of users I'll build users_150K\n",
    "# But I wonder if pulling out items only ordered once or users who only ordered once would be better\n",
    "\n",
    "users_150K = rec_df[rec_df['user_id'] < 150000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>num_orders</th>\n",
       "      <th>mode_order_dow</th>\n",
       "      <th>median_order_hour</th>\n",
       "      <th>mean_days_since</th>\n",
       "      <th>aisle_air fresheners candles</th>\n",
       "      <th>aisle_asian foods</th>\n",
       "      <th>aisle_baby accessories</th>\n",
       "      <th>aisle_baby bath body care</th>\n",
       "      <th>aisle_baby food formula</th>\n",
       "      <th>aisle_bakery desserts</th>\n",
       "      <th>...</th>\n",
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       "      <th>aisle_tofu meat alternatives</th>\n",
       "      <th>aisle_tortillas flat bread</th>\n",
       "      <th>aisle_trail mix snack mix</th>\n",
       "      <th>aisle_trash bags liners</th>\n",
       "      <th>aisle_vitamins supplements</th>\n",
       "      <th>aisle_water seltzer sparkling water</th>\n",
       "      <th>aisle_white wines</th>\n",
       "      <th>aisle_yogurt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.357143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.314286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.173913</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26.117647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206184</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.705882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206186</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206196</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.569767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206204</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.638298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206205</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.705882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49404 rows × 138 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_orders  mode_order_dow  median_order_hour  mean_days_since  \\\n",
       "user_id                                                                   \n",
       "4                 5               4               13.0        15.357143   \n",
       "5                 5               0               16.0        12.314286   \n",
       "6                 3               4               17.0         7.800000   \n",
       "8                 4               1                0.0        22.173913   \n",
       "9                 4               5               12.0        26.117647   \n",
       "...             ...             ...                ...              ...   \n",
       "206184            5               2               11.0        24.705882   \n",
       "206186            4               6               15.0        16.200000   \n",
       "206196            5               6               15.0        25.569767   \n",
       "206204            4               5               12.5        15.638298   \n",
       "206205            4               1               16.0        14.705882   \n",
       "\n",
       "         aisle_air fresheners candles  aisle_asian foods  \\\n",
       "user_id                                                    \n",
       "4                                 0.0                0.0   \n",
       "5                                 1.0                4.0   \n",
       "6                                 0.0                0.0   \n",
       "8                                 0.0                1.0   \n",
       "9                                 0.0                0.0   \n",
       "...                               ...                ...   \n",
       "206184                            0.0                1.0   \n",
       "206186                            0.0                0.0   \n",
       "206196                            0.0                0.0   \n",
       "206204                            0.0                2.0   \n",
       "206205                            0.0                0.0   \n",
       "\n",
       "         aisle_baby accessories  aisle_baby bath body care  \\\n",
       "user_id                                                      \n",
       "4                           0.0                        0.0   \n",
       "5                           0.0                        0.0   \n",
       "6                           0.0                        0.0   \n",
       "8                           0.0                        0.0   \n",
       "9                           0.0                        0.0   \n",
       "...                         ...                        ...   \n",
       "206184                      0.0                        0.0   \n",
       "206186                      0.0                        0.0   \n",
       "206196                      0.0                        0.0   \n",
       "206204                      0.0                        0.0   \n",
       "206205                      1.0                        0.0   \n",
       "\n",
       "         aisle_baby food formula  aisle_bakery desserts  ...  aisle_spreads  \\\n",
       "user_id                                                  ...                  \n",
       "4                            0.0                    0.0  ...            0.0   \n",
       "5                            0.0                    0.0  ...            0.0   \n",
       "6                            0.0                    0.0  ...            0.0   \n",
       "8                            0.0                    0.0  ...            0.0   \n",
       "9                            6.0                    0.0  ...            0.0   \n",
       "...                          ...                    ...  ...            ...   \n",
       "206184                       0.0                    0.0  ...            0.0   \n",
       "206186                       0.0                    0.0  ...            1.0   \n",
       "206196                       0.0                    0.0  ...            1.0   \n",
       "206204                       0.0                    0.0  ...            1.0   \n",
       "206205                       0.0                    0.0  ...            0.0   \n",
       "\n",
       "         aisle_tea  aisle_tofu meat alternatives  aisle_tortillas flat bread  \\\n",
       "user_id                                                                        \n",
       "4              0.0                           0.0                         1.0   \n",
       "5              0.0                           0.0                         0.0   \n",
       "6              0.0                           0.0                         0.0   \n",
       "8              0.0                           1.0                         1.0   \n",
       "9              0.0                           0.0                         0.0   \n",
       "...            ...                           ...                         ...   \n",
       "206184         0.0                           0.0                         0.0   \n",
       "206186         0.0                           0.0                         0.0   \n",
       "206196         1.0                           0.0                         0.0   \n",
       "206204         0.0                           0.0                         1.0   \n",
       "206205         0.0                           0.0                         0.0   \n",
       "\n",
       "         aisle_trail mix snack mix  aisle_trash bags liners  \\\n",
       "user_id                                                       \n",
       "4                              0.0                      0.0   \n",
       "5                              0.0                      0.0   \n",
       "6                              0.0                      0.0   \n",
       "8                              0.0                      0.0   \n",
       "9                              0.0                      0.0   \n",
       "...                            ...                      ...   \n",
       "206184                         0.0                      0.0   \n",
       "206186                         0.0                      0.0   \n",
       "206196                         0.0                      0.0   \n",
       "206204                         0.0                      0.0   \n",
       "206205                         0.0                      0.0   \n",
       "\n",
       "         aisle_vitamins supplements  aisle_water seltzer sparkling water  \\\n",
       "user_id                                                                    \n",
       "4                               0.0                                  1.0   \n",
       "5                               0.0                                  0.0   \n",
       "6                               0.0                                  0.0   \n",
       "8                               0.0                                  0.0   \n",
       "9                               0.0                                  3.0   \n",
       "...                             ...                                  ...   \n",
       "206184                          0.0                                  0.0   \n",
       "206186                          0.0                                  4.0   \n",
       "206196                          1.0                                  7.0   \n",
       "206204                          0.0                                  0.0   \n",
       "206205                          0.0                                  0.0   \n",
       "\n",
       "         aisle_white wines  aisle_yogurt  \n",
       "user_id                                   \n",
       "4                      0.0           0.0  \n",
       "5                      0.0           3.0  \n",
       "6                      0.0           0.0  \n",
       "8                      0.0           0.0  \n",
       "9                      0.0          28.0  \n",
       "...                    ...           ...  \n",
       "206184                 0.0           0.0  \n",
       "206186                 0.0           0.0  \n",
       "206196                 0.0           0.0  \n",
       "206204                 0.0           2.0  \n",
       "206205                 0.0           9.0  \n",
       "\n",
       "[49404 rows x 138 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Might be better by eliminating users with few products ordered instead of few orders\n",
    "# Grouped_users is fromm the clustering notebook\n",
    "# Need to recalculate this\n",
    "grouped_users = pickle.load(open(\"Pickle/grouped_users.p\", \"rb\"))\n",
    "grouped_users[grouped_users['num_orders']<=5] # 49000 users with 5 or fewer orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36233</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11254</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43098</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14485</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7626</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8009</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45085</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6525</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23068</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40766</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2741 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "product_id       \n",
       "36233           1\n",
       "11254           1\n",
       "43098           1\n",
       "14485           1\n",
       "7626            1\n",
       "...           ...\n",
       "8009            5\n",
       "45085           5\n",
       "6525            5\n",
       "23068           5\n",
       "40766           5\n",
       "\n",
       "[2741 rows x 1 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_with_count = rec_df.groupby('product_id').sum().drop(columns='user_id').sort_values(by='count')\n",
    "products_with_count[products_with_count['count']<=5]  # 2741 products ordered 5 times or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling a recommendation system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import NormalPredictor\n",
    "from surprise import Dataset\n",
    "from surprise import Reader\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise import SVD\n",
    "from surprise import KNNBaseline\n",
    "from surprise import KNNBasic\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise.prediction_algorithms import knns\n",
    "from surprise.similarities import cosine, msd, pearson\n",
    "from surprise.prediction_algorithms import SVD, SVDpp\n",
    "from surprise.model_selection import GridSearchCV\n",
    "from surprise import accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A reader is still needed but only the rating_scale param is required.\n",
    "reader = Reader(rating_scale=(1, 100))\n",
    "\n",
    "# The columns must correspond to user id, item id and ratings (in that order).\n",
    "rec_data = Dataset.load_from_df(rec_df, reader)\n",
    "\n",
    "# sample random trainset and testset\n",
    "# test set is made of 25% of the ratings.\n",
    "trainset, testset = train_test_split(rec_data, test_size=.25, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with a memory-based model\n",
    "\n",
    "# These just take up too much memory for the size of my data.  Moving on to model-based.\n",
    "\n",
    "# sim_cos = {'name':'cosine', 'user_based':False}\n",
    "\n",
    "# tic = time.time()\n",
    "\n",
    "# basic = knns.KNNBasic(sim_options=sim_cos)\n",
    "# basic.fit(trainset)\n",
    "\n",
    "# fit_time = time.time()-tic\n",
    "# print('fit ', fit_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"tic = time.time()\\n\\nsvd_algo = SVD()\\n\\nsvd_algo.fit(trainset)\\n\\nfit_time = time.time()-tic\\nprint('fit ', fit_time)\\ntoc = time.time()\\n\\nsvd_algo_predictions = svd_algo.test(testset)\\n\\npred_time = time.time()-toc\\nprint('pred ', pred_time)\\n\\n# Then compute RMSE\\naccuracy.rmse(svd_algo_predictions)\""
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Can't believe I got this to work on the whole data set\n",
    "\n",
    "'''tic = time.time()\n",
    "\n",
    "svd_algo = SVD()\n",
    "\n",
    "svd_algo.fit(trainset)\n",
    "\n",
    "fit_time = time.time()-tic\n",
    "print('fit ', fit_time)\n",
    "toc = time.time()\n",
    "\n",
    "svd_algo_predictions = svd_algo.test(testset)\n",
    "\n",
    "pred_time = time.time()-toc\n",
    "print('pred ', pred_time)\n",
    "\n",
    "# Then compute RMSE\n",
    "accuracy.rmse(svd_algo_predictions)'''\n",
    "\n",
    "# 3.46 RMSE on a scale of 1-100 is pretty darn good, but my data is so skewed it may be misleading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(svd_algo, open(\"Pickle/svd_algo.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(svd_algo_predictions, open(\"Pickle/svd_algo_predictions.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd_algo = pickle.load(open(\"Pickle/svd_algo.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "svd_algo_predictions = pickle.load(open(\"Pickle/svd_algo_predictions.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 3.4616\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.4615887522647553"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy.rmse(svd_algo_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "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",
       "        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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12354</th>\n",
       "      <td>196</td>\n",
       "      <td>13176</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12355</th>\n",
       "      <td>196</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  product_id  count\n",
       "12354      196       13176      2\n",
       "12355      196        1957      1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Picking a random user to look at one of their ratings\n",
    "\n",
    "rec_df[rec_df['user_id'] == 196]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 196        item: 13176      r_ui = 2.00   est = 1.77   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "uid=196\n",
    "iid=13176\n",
    "# actual value was 2, prediction is 1.77\n",
    "pred = svd_algo.predict(uid, iid, r_ui=2, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127895, 6101, 2.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examining the first record in my test set for its actual value\n",
    "testset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 127895     item: 6101       r_ui = 2.00   est = 1.15   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "# Prediction is 1.15\n",
    "pred = svd_algo.predict(127895, 6101, r_ui=2, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid=127895, iid=6101, r_ui=2.0, est=1.1531549806442887, details={'was_impossible': False})"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd_algo_predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform a grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Started at 6:45pm, ended at 9:30\n",
    "\n",
    "# param_grid = {'n_factors':[20, 100],'n_epochs': [5, 10], 'lr_all': [0.002, 0.005],\n",
    "#               'reg_all': [0.4, 0.6]}\n",
    "# gs_model = GridSearchCV(SVD, param_grid=param_grid, n_jobs = -1, joblib_verbose=5)\n",
    "# gs_model.fit(rec_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(gs_model, open(\"Pickle/gs_model.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_model = pickle.load(open(\"Pickle/gs_model.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse': {'n_factors': 100, 'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4},\n",
       " 'mae': {'n_factors': 100, 'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_model.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame.from_dict(gs_model.cv_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit  323.62454080581665\n",
      "pred  107.7913568019867\n",
      "RMSE: 3.2478\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.2478170587039923"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the model from the best params\n",
    "\n",
    "tic = time.time()\n",
    "\n",
    "svd_best_gs = SVD(n_factors = 100, n_epochs = 10, lr_all = 0.005, reg_all = 0.4)\n",
    "\n",
    "svd_best_gs.fit(trainset)\n",
    "\n",
    "fit_time = time.time()-tic\n",
    "print('fit ', fit_time)\n",
    "toc = time.time()\n",
    "\n",
    "svd_best_gs_predictions = svd_best_gs.test(testset)\n",
    "\n",
    "pred_time = time.time()-toc\n",
    "print('pred ', pred_time)\n",
    "\n",
    "# Then compute RMSE\n",
    "accuracy.rmse(svd_best_gs_predictions)\n",
    "\n",
    "# RMSE went down on grid search model from 3.4616 to 3.2483"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try a different rating scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41356, 38652, 100.0)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I am curious how the model predicts one of the items purchased 100 times\n",
    "\n",
    "max(testset,key=lambda item:item[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 41356      item: 38652      r_ui = 100.00   est = 22.26   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "# This one was predicted to be 22.27, that's pretty far off\n",
    "\n",
    "pred = svd_best_gs.predict(41356,38652, r_ui=100.0, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[141736, 14947, 96.0, 52.345649285613014]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is my highest prediction?  53.221 on a true rating of 96\n",
    "\n",
    "# It seems like the model gets worse for higher ratings\n",
    "\n",
    "top_est = 0\n",
    "top_pred = [0,0,0,0]\n",
    "for uid, iid, true_r, est, _ in svd_best_gs_predictions:\n",
    "    if est > top_est:\n",
    "        top_est = est\n",
    "        top_pred = [uid,iid,true_r,est]\n",
    "top_pred        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      8299266\n",
       "2      2296606\n",
       "3      1074345\n",
       "4       612369\n",
       "5       384418\n",
       "        ...   \n",
       "94           5\n",
       "97           4\n",
       "99           4\n",
       "100          3\n",
       "98           2\n",
       "Name: count, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perhaps I can convert into a rating on a 1 to 5 scale\n",
    "rec_df['count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rec_df['count']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Well I'm not sure if this is the best way, but there are so few above 5 I think I can group them all together\n",
    "rec_df['rating'] = rec_df['count'].apply(lambda x: 5 if (x >= 5) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fHsJ69iDvB77V/aEREWcC/ziMXBFxBZ1X809k5ku993XXlFdExIndfT8GrJm437Bz0XmhXR4Re3T3PRp4bQi5zgW+2HP7ODqnDAZaHa++uWhpvDLz0sw8ODMXZuZCYG333y93729lvAblor3n11HArd33JaDzxvvdPbmLjFfr5x6JiGXALnTWhp4C1gGvArdk5nci4n103oCcTeeQvzP7rGsNM9fpwIV0zi3+HHBhZv534UynAX9FZ714yw/uWWAtsDIzb4uIPYHbgLnd7Wdm5gszINcxdNYlx+kcKnlh6UPFur8RXQ98hM7P6Rk6P7MltDteTXINfbymyPlKZs6NiBtocbwa5mplvCJiMZ3J5CY6byZfAlxLwfFqvbQlSc3N6OURSdJbWdqSVBFLW5IqYmlLUkUsbUmqiKUtSRWxtCWpIpa2JFXkfwED28Wd3KISswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rec_df['rating']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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",
       "    .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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13863741</th>\n",
       "      <td>206209</td>\n",
       "      <td>4461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863742</th>\n",
       "      <td>206209</td>\n",
       "      <td>20842</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863743</th>\n",
       "      <td>206209</td>\n",
       "      <td>23909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863744</th>\n",
       "      <td>206209</td>\n",
       "      <td>26209</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863745</th>\n",
       "      <td>206209</td>\n",
       "      <td>14870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  rating\n",
       "13863741   206209        4461       1\n",
       "13863742   206209       20842       1\n",
       "13863743   206209       23909       1\n",
       "13863744   206209       26209       1\n",
       "13863745   206209       14870       1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rec_df = rec_df.drop(columns='count')\n",
    "new_rec_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(new_rec_df, open(\"Pickle/new_rec_df.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_rec_df = pickle.load(open(\"Pickle/new_rec_df.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12427</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10258</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25133</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>46149</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863741</th>\n",
       "      <td>206209</td>\n",
       "      <td>4461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863742</th>\n",
       "      <td>206209</td>\n",
       "      <td>20842</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863743</th>\n",
       "      <td>206209</td>\n",
       "      <td>23909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863744</th>\n",
       "      <td>206209</td>\n",
       "      <td>26209</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863745</th>\n",
       "      <td>206209</td>\n",
       "      <td>14870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13863746 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  rating\n",
       "0               1         196       5\n",
       "1               1       12427       5\n",
       "2               1       10258       5\n",
       "3               1       25133       5\n",
       "4               1       46149       4\n",
       "...           ...         ...     ...\n",
       "13863741   206209        4461       1\n",
       "13863742   206209       20842       1\n",
       "13863743   206209       23909       1\n",
       "13863744   206209       26209       1\n",
       "13863745   206209       14870       1\n",
       "\n",
       "[13863746 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rec_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Passing pieces of dataframe in FLASK to be put back together there\n",
    "pickle.dump(new_rec_df.columns.values,open('Pickle/rec_columns.p','wb'))\n",
    "pickle.dump(new_rec_df.index.values, open('Pickle/rec_index.p', 'wb'))\n",
    "pickle.dump(new_rec_df.user_id.values, open('Pickle/rec_user.p', 'wb'))\n",
    "pickle.dump(new_rec_df.rating.values, open('Pickle/rec_rating.p', 'wb'))\n",
    "pickle.dump(new_rec_df.product_id.values, open('Pickle/rec_prod_id.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_columns = pickle.load(open('Pickle/rec_columns.p','rb'))\n",
    "rec_index = pickle.load(open('Pickle/rec_index.p', 'rb'))\n",
    "rec_user = pickle.load(open('Pickle/rec_user.p', 'rb'))\n",
    "rec_rating = pickle.load(open('Pickle/rec_rating.p', 'rb'))\n",
    "rec_prod_id = pickle.load(open('Pickle/rec_prod_id.p', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(np.column_stack([rec_user, rec_prod_id, rec_rating]), index=rec_index, columns=rec_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>196</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>12427</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10258</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25133</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>46149</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863741</th>\n",
       "      <td>206209</td>\n",
       "      <td>4461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863742</th>\n",
       "      <td>206209</td>\n",
       "      <td>20842</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863743</th>\n",
       "      <td>206209</td>\n",
       "      <td>23909</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863744</th>\n",
       "      <td>206209</td>\n",
       "      <td>26209</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863745</th>\n",
       "      <td>206209</td>\n",
       "      <td>14870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13863746 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  rating\n",
       "0               1         196       5\n",
       "1               1       12427       5\n",
       "2               1       10258       5\n",
       "3               1       25133       5\n",
       "4               1       46149       4\n",
       "...           ...         ...     ...\n",
       "13863741   206209        4461       1\n",
       "13863742   206209       20842       1\n",
       "13863743   206209       23909       1\n",
       "13863744   206209       26209       1\n",
       "13863745   206209       14870       1\n",
       "\n",
       "[13863746 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "13.450033187866211\n",
      "41.20182418823242\n"
     ]
    }
   ],
   "source": [
    "# Must start with a new reader\n",
    "\n",
    "tic = time.time()\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "readtime = time.time()-tic\n",
    "print(readtime)\n",
    "# The columns must correspond to user id, item id and ratings (in that order).\n",
    "toc = time.time()\n",
    "new_rec_data = Dataset.load_from_df(new_rec_df, reader)\n",
    "loadtime = time.time()-toc\n",
    "print(loadtime)\n",
    "# sample random trainset and testset\n",
    "# test set is made of 25% of the ratings.\n",
    "tac = time.time()\n",
    "new_trainset, new_testset = train_test_split(new_rec_data, test_size=.25, random_state=12)\n",
    "splittime = time.time()-tac\n",
    "print(splittime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(reader, open(\"Pickle/reader.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit  288.56942772865295\n",
      "pred  204.20226907730103\n",
      "RMSE: 1.2649\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.2648735882122"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Might as well start with the best params from the last model\n",
    "\n",
    "'''tic = time.time()\n",
    "\n",
    "new_svd_algo = SVD(n_factors = 100, n_epochs = 10, lr_all = 0.005, reg_all = 0.4)\n",
    "\n",
    "new_svd_algo.fit(new_trainset)\n",
    "\n",
    "fit_time = time.time()-tic\n",
    "print('fit ', fit_time)\n",
    "toc = time.time()\n",
    "\n",
    "new_predictions = new_svd_algo.test(new_testset)\n",
    "\n",
    "pred_time = time.time()-toc\n",
    "print('pred ', pred_time)\n",
    "\n",
    "# Then compute RMSE\n",
    "accuracy.rmse(new_predictions)\n",
    "\n",
    "# Not bad, RMSE went to to 1.2649'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(new_svd_algo, open(\"Pickle/new_svd_algo.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(new_predictions, open(\"Pickle/new_predictions.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_svd_algo = pickle.load(open(\"Pickle/new_svd_algo.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_predictions = pickle.load(open(\"Pickle/new_predictions.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.2649\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.2648735882122"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy.rmse(new_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 196        item: 13176      r_ui = 2.00   est = 2.12   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "# This prediction was 1.76 before, it has gone up to 2.12 with the new scale\n",
    "\n",
    "uid=196\n",
    "iid=13176\n",
    "# get a prediction for specific users and items.\n",
    "pred = new_svd_algo.predict(uid, iid, r_ui=2, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediction(uid=127895, iid=6101, r_ui=2.0, est=1.475188661747201, details={'was_impossible': False})"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This estimate was 1.498 before and now it is 1.475\n",
    "\n",
    "new_predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user: 41356      item: 38652      r_ui = 5.00   est = 3.21   {'was_impossible': False}\n"
     ]
    }
   ],
   "source": [
    "# this one was 100 on the last scale but predicted as 21.59\n",
    "# now it is a 5 on the new scale and predicted at 3.21\n",
    "\n",
    "pred = new_svd_algo.predict(41356,38652, r_ui=5.0, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:  4.5min\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed: 76.9min\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed: 133.1min finished\n"
     ]
    }
   ],
   "source": [
    "# Well lets try another grid search\n",
    "\n",
    "# Started at 9:57am, ended at 12:30pm\n",
    "\n",
    "'''param_grid = {'n_factors':[20, 100],'n_epochs': [5, 10], 'lr_all': [0.002, 0.005],\n",
    "              'reg_all': [0.4, 0.6]}\n",
    "new_gs_model = GridSearchCV(SVD, param_grid=param_grid, n_jobs = -1, joblib_verbose=5)\n",
    "new_gs_model.fit(new_rec_data)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(new_gs_model, open(\"Pickle/new_gs_model.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_gs_model = pickle.load(open(\"Pickle/new_gs_model.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rmse': {'n_factors': 20, 'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4},\n",
       " 'mae': {'n_factors': 20, 'n_epochs': 10, 'lr_all': 0.005, 'reg_all': 0.4}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_gs_model.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_results_df = pd.DataFrame.from_dict(new_gs_model.cv_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit  135.78296637535095\n",
      "pred  63.0951828956604\n",
      "RMSE: 1.2648\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.2648270072757875"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the model from the best params\n",
    "\n",
    "'''tic = time.time()\n",
    "\n",
    "new_svd_best_gs = SVD(n_factors = 20, n_epochs = 10, lr_all = 0.005, reg_all = 0.4)\n",
    "\n",
    "new_svd_best_gs.fit(new_trainset)\n",
    "\n",
    "fit_time = time.time()-tic\n",
    "print('fit ', fit_time)\n",
    "toc = time.time()\n",
    "\n",
    "new_svd_best_gs_predictions = new_svd_best_gs.test(new_testset)\n",
    "\n",
    "pred_time = time.time()-toc\n",
    "print('pred ', pred_time)\n",
    "\n",
    "# Then compute RMSE\n",
    "accuracy.rmse(new_svd_best_gs_predictions)'''\n",
    "\n",
    "# RMSE went down on grid search model from 1.2649 to 1.2648.  lol, not much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(new_svd_best_gs, open(\"Pickle/new_svd_best_gs.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pickle.dump(new_svd_best_gs_predictions, open(\"Pickle/new_svd_best_gs_predictions.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_svd_best_gs = pickle.load(open(\"Pickle/new_svd_best_gs.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_svd_best_gs_predictions = pickle.load(open(\"Pickle/new_svd_best_gs_predictions.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get top n predictions for a user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get top predictions for a user\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "def get_top_n(predictions, n=10):\n",
    "    \"\"\"Return the top-N recommendation for each user from a set of predictions.\n",
    "\n",
    "    Args:\n",
    "        predictions(list of Prediction objects): The list of predictions, as\n",
    "            returned by the test method of an algorithm.\n",
    "        n(int): The number of recommendation to output for each user. Default\n",
    "            is 10.\n",
    "\n",
    "    Returns:\n",
    "    A dict where keys are user (raw) ids and values are lists of tuples:\n",
    "        [(raw item id, rating estimation), ...] of size n.\n",
    "    \"\"\"\n",
    "\n",
    "    # First map the predictions to each user.\n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "\n",
    "    # Then sort the predictions for each user and retrieve the k highest ones.\n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:n]\n",
    "\n",
    "    return top_n\n",
    "\n",
    "\n",
    "# First train an SVD algorithm on the movielens dataset.\n",
    "# data = Dataset.load_builtin('ml-100k')\n",
    "# trainset = data.build_full_trainset()\n",
    "# algo = SVD()\n",
    "# algo.fit(trainset)\n",
    "\n",
    "# Than predict ratings for all pairs (u, i) that are NOT in the training set.\n",
    "# anti_testset = new_trainset.build_anti_testset()\n",
    "# predictions = anti_new_svd_best_gs.test(anti_testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_n = get_top_n(new_svd_best_gs_predictions, n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze popularity bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 in top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(196, 2.8803536813931596),\n",
       " (17122, 2.214499357390091),\n",
       " (30450, 2.060545637467949),\n",
       " (13032, 2.0436925955680065)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_n[1] # Recommendations for user number 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203501"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(top_n.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Banana                                                                     491291\n",
       "Bag of Organic Bananas                                                     394930\n",
       "Organic Strawberries                                                       275577\n",
       "Organic Baby Spinach                                                       251705\n",
       "Organic Hass Avocado                                                       220877\n",
       "                                                                            ...  \n",
       "Wasabi Cheddar Spreadable Cheese                                                1\n",
       "Sugar Leaf  Stevia and Raw Cane Sugar                                           1\n",
       "Piquillo & Jalapeno Bruschetta                                                  1\n",
       "1,000 Mg Vitamin C Tangerine Grapefruit Effervescent Powdered Drink Mix         1\n",
       "Chardonnay Carneros                                                             1\n",
       "Name: product_name, Length: 49685, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_desc['product_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "all_rec_items=[]  # Making a list of all recommended items in the top for each user, then count them\n",
    "for idx in range(len(top_n.values())-200000):\n",
    "    all_rec_items.extend([x[0] for x in [*top_n.values()][idx]]) # product_ids recommended for first user\n",
    "# all_rec_items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "ctr = Counter(all_rec_items)\n",
    "most_ctr = ctr.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(24852, 450),\n",
       " (13176, 374),\n",
       " (21903, 341),\n",
       " (47766, 268),\n",
       " (47209, 266),\n",
       " (16797, 244),\n",
       " (21137, 197),\n",
       " (27966, 178),\n",
       " (28204, 175),\n",
       " (27845, 158),\n",
       " (19057, 145),\n",
       " (45066, 141),\n",
       " (27086, 138),\n",
       " (21616, 133),\n",
       " (8277, 131),\n",
       " (39877, 125),\n",
       " (30489, 118),\n",
       " (39275, 113),\n",
       " (49235, 109),\n",
       " (44632, 109)]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_ctr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Banana',\n",
       " 'Bag of Organic Bananas',\n",
       " 'Organic Baby Spinach',\n",
       " 'Organic Avocado',\n",
       " 'Organic Hass Avocado',\n",
       " 'Strawberries',\n",
       " 'Organic Strawberries',\n",
       " 'Organic Raspberries',\n",
       " 'Organic Fuji Apple',\n",
       " 'Organic Whole Milk',\n",
       " 'Organic Large Extra Fancy Fuji Apple',\n",
       " 'Honeycrisp Apple',\n",
       " 'Half & Half',\n",
       " 'Organic Baby Arugula',\n",
       " 'Apple Honeycrisp Organic',\n",
       " 'Organic Granny Smith Apple',\n",
       " 'Original Hummus',\n",
       " 'Organic Blueberries',\n",
       " 'Organic Half & Half',\n",
       " 'Sparkling Water Grapefruit']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[products_desc[products_desc['product_id']==x]['product_name'].iloc[0] for x in [x[0] for x in most_ctr]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13863746"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rec_df.product_id.value_counts().sum() # 13,863,746 ratings, so the top 80% would be 11,091,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   76125,   141780,   202909, ..., 11090416, 11090809, 11091202],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_rec_df.product_id.value_counts()[:6207].cumsum().values # I admit this was trial and error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# so the top 6200 products account for 80% of the ratings (or purchases in this case)\n",
    "short_head = list(new_rec_df.product_id.value_counts()[:6207].cumsum().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(short_head, open(\"Pickle/short_head.p\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "13176 not in short_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now when I generate recommendations I will make sure 20% of the recommendations come from the long tail\n",
    "# Personalized re-ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get recommendations for a new user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_desc = pickle.load(open(\"Pickle/products_desc.p\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grocery_rater(df, num, aisle=None):\n",
    "    userID = 300000\n",
    "    rating_list = []\n",
    "    while num > 0:\n",
    "        if aisle:\n",
    "            product = df[df['aisle'].str.contains(aisle)].sample(1)\n",
    "        else:\n",
    "            product = df.sample(1)\n",
    "        print('\\n', product['product_name'].iloc[0])\n",
    "        rating = input('How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\\n')\n",
    "        if rating == '0':\n",
    "            continue\n",
    "        else:\n",
    "            rating_one_product = {'user_id':userID,'product_id':product['product_id'].iloc[0],'rating':int(rating)}\n",
    "            rating_list.append(rating_one_product) \n",
    "            num -= 1\n",
    "    return rating_list  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Peanut Butter M&Ms\n",
      "How do you rate this product on a scale of 1-5, choose 0 if you are unfamiliar:\n",
      "3\n",
      "\n",
      " Demon Treat Halloween Candy\n",
      "How do you rate this product on a scale of 1-5, choose 0 if you are unfamiliar:\n",
      "0\n",
      "\n",
      " Peppermint Bark With Dark Chocolate\n",
      "How do you rate this product on a scale of 1-5, choose 0 if you are unfamiliar:\n",
      "5\n",
      "\n",
      " Organic Royal Dark 88% Chocolate Bar\n",
      "How do you rate this product on a scale of 1-5, choose 0 if you are unfamiliar:\n",
      "4\n",
      "\n",
      " Milk Chocolate\n",
      "How do you rate this product on a scale of 1-5, choose 0 if you are unfamiliar:\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# try out the new function here!\n",
    "user_rating = grocery_rater(products_desc, 4, aisle='candy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'user_id': 300000, 'product_id': 18860, 'rating': 3},\n",
       " {'user_id': 300000, 'product_id': 48338, 'rating': 5},\n",
       " {'user_id': 300000, 'product_id': 8589, 'rating': 4},\n",
       " {'user_id': 300000, 'product_id': 6510, 'rating': 3}]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a clean copy of the rec dataframe\n",
    "new_rec_df = pickle.load(open(\"Pickle/new_rec_df.p\", \"rb\"))\n",
    "\n",
    "# add the new ratings to the original ratings DataFrame\n",
    "new_ratings_df = new_rec_df.append(user_rating, ignore_index=True)\n",
    "new_data = Dataset.load_from_df(new_ratings_df, reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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",
       "        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>user_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13863745</th>\n",
       "      <td>206209</td>\n",
       "      <td>14870</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863746</th>\n",
       "      <td>300000</td>\n",
       "      <td>18860</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863747</th>\n",
       "      <td>300000</td>\n",
       "      <td>48338</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863748</th>\n",
       "      <td>300000</td>\n",
       "      <td>8589</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13863749</th>\n",
       "      <td>300000</td>\n",
       "      <td>6510</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  product_id  rating\n",
       "13863745   206209       14870       1\n",
       "13863746   300000       18860       3\n",
       "13863747   300000       48338       5\n",
       "13863748   300000        8589       4\n",
       "13863749   300000        6510       3"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ratings_df.tail() # You can see the new user's ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x11f161bb940>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a model using the new combined DataFrame\n",
    "new_user_svd = SVD(n_factors = 20, n_epochs = 10, lr_all = 0.005, reg_all = 0.4)\n",
    "new_user_svd.fit(new_data.build_full_trainset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions for the user\n",
    "\n",
    "list_of_products = []\n",
    "for product in new_ratings_df['product_id'].unique():\n",
    "    product_name = products_desc[products_desc['product_id'] == product]['product_name'].iloc[0]\n",
    "    product_aisle = products_desc[products_desc['product_id'] == product]['aisle'].iloc[0]\n",
    "    list_of_products.append((product, new_user_svd.predict(300000, product)[3], product_name, product_aisle))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order the predictions from highest to lowest rated\n",
    "\n",
    "# List of (product_id, predicted_rating, product_name, aisle)\n",
    "\n",
    "ranked_products = sorted(list_of_products, key=lambda x:x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# return the top n recommendations \n",
    "def recommended_products(ranked_products, n, aisle=None):\n",
    "    for idx, rec in enumerate(ranked_products):\n",
    "        if aisle:    \n",
    "            if aisle in rec[3]: \n",
    "                name = rec[2]\n",
    "                print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "                n-= 1\n",
    "            elif idx == len(ranked_products)-1:\n",
    "                print('No recommended products found')\n",
    "                continue\n",
    "        else:\n",
    "            name = rec[2]\n",
    "            print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "            n-= 1\n",
    "        if n == 0:\n",
    "            break\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendation #  60 :  Authentic Asia Shrimp Wonton Soup With Noodles \n",
      "\n",
      "Recommendation #  61 :  A+ Cinnamon Sweet Potato Pancakes \n",
      "\n",
      "Recommendation #  77 :  Eggo Bites Chocolatey Chip Pancakes \n",
      "\n",
      "Recommendation #  179 :  Original French Toast Sticks \n",
      "\n",
      "Recommendation #  180 :  Eggo Chocolate Chip Pancakes 12 Count \n",
      "\n"
     ]
    }
   ],
   "source": [
    "recommended_products(ranked_products, 5, aisle='frozen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# return the top n diverse recommendations \n",
    "def recommend_diverse_products(ranked_products, n, aisle=None, percent_diverse=.20):\n",
    "    \n",
    "    num_diverse = round(n * percent_diverse)\n",
    "    recs = []\n",
    "    \n",
    "    if n < 1:\n",
    "        print('Number of recommended products must be 1 or more')\n",
    "        return recs\n",
    "    \n",
    "    for idx, rec in enumerate(ranked_products):\n",
    "        \n",
    "        if n == 0:\n",
    "            return recs\n",
    "            \n",
    "        prod_id, rating, prod_name, aisle_name = [*rec]\n",
    "        \n",
    "               \n",
    "        if aisle:                                    # Did we specify an aisle? \n",
    "            if aisle in aisle_name:                  # Is it in the aisle we want?\n",
    "                if n > num_diverse:                  # Are we looking for a long tail product? No\n",
    "                    name = prod_name\n",
    "                    print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "                    recs.append(rec)\n",
    "                    n-= 1\n",
    "                else:                                 # Are we looking for a long tail product? Yes\n",
    "                    if prod_id not in short_head:     # Is it NOT in the short_head list?\n",
    "                        name = prod_name\n",
    "                        print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "                        recs.append(rec)\n",
    "                        n-= 1\n",
    "                    else:\n",
    "                        continue\n",
    "            elif idx == len(ranked_products)-1:\n",
    "                print('No recommended products found')\n",
    "                continue\n",
    "        else:\n",
    "            if n > num_diverse:                  # Are we looking for a long tail product? No\n",
    "                name = prod_name\n",
    "                print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "                recs.append(rec)\n",
    "                n-= 1\n",
    "            else:                                 # Are we looking for a long tail product? Yes\n",
    "                if prod_id not in short_head:     # Is it NOT in the short_head list?\n",
    "                    name = prod_name\n",
    "                    print('Recommendation # ', idx+1, ': ', name, '\\n')\n",
    "                    recs.append(rec)\n",
    "                    n-= 1\n",
    "                else:\n",
    "                    continue\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendation #  2 :  Lo-Carb Energy Drink \n",
      "\n",
      "Recommendation #  11 :  Organic Grade A Raw Whole Milk \n",
      "\n",
      "Recommendation #  16 :  DanActive Vanilla Probiotic Dairy Drink \n",
      "\n",
      "Recommendation #  21 :  Alimentum Infant Formula \n",
      "\n",
      "Recommendation #  26 :  Plain Soy Milk \n",
      "\n",
      "Recommendation #  28 :  1% Milkfat Low Fat Vitamin A & D Milk \n",
      "\n",
      "Recommendation #  29 :  Ultra-Purified Water \n",
      "\n",
      "Recommendation #  30 :  Kickstart Energizing Orange Citrus Sparkling Juice Beverage \n",
      "\n",
      "Recommendation #  31 :  Purified Alkalkine Water with Minerals pH10 \n",
      "\n",
      "Recommendation #  33 :  Diet Orange & Cream Soda \n",
      "\n"
     ]
    }
   ],
   "source": [
    "recs = recommend_diverse_products(ranked_products, 10, percent_diverse=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(17469, 3.1681957655568276, 'Lo-Carb Energy Drink', 'energy sports drinks'),\n",
       " (40183, 3.0908963571678316, 'Organic Grade A Raw Whole Milk', 'milk'),\n",
       " (48041,\n",
       "  3.0446621058945316,\n",
       "  'DanActive Vanilla Probiotic Dairy Drink',\n",
       "  'yogurt'),\n",
       " (47765, 3.0188712296018654, 'Alimentum Infant Formula', 'baby food formula'),\n",
       " (43734, 3.0073112840778324, 'Plain Soy Milk', 'soy lactosefree'),\n",
       " (13806, 3.004138614968046, '1% Milkfat Low Fat Vitamin A & D Milk', 'milk'),\n",
       " (47231,\n",
       "  2.9989173893746615,\n",
       "  'Ultra-Purified Water',\n",
       "  'water seltzer sparkling water'),\n",
       " (24746,\n",
       "  2.9881899320986793,\n",
       "  'Kickstart Energizing Orange Citrus Sparkling Juice Beverage',\n",
       "  'energy sports drinks'),\n",
       " (13128,\n",
       "  2.972977386570463,\n",
       "  'Purified Alkalkine Water with Minerals pH10',\n",
       "  'water seltzer sparkling water'),\n",
       " (48670, 2.964602995997849, 'Diet Orange & Cream Soda', 'soft drinks')]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "17469 in short_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  I'd like to put everything into one function that will ask for your ratings and then return recommendations\n",
    "#  It will take a while to run though so I am adding print statements so we know where we are in the process\n",
    "\n",
    "def generate_new_user_recommendations(new_rec_df, products_desc, n_to_rate, n_to_rec, percent_diverse, \n",
    "                                      rate_aisle=None, rec_aisle=None):\n",
    "    \n",
    "    # Get user ratings\n",
    "    user_rating = grocery_rater(products_desc, n_to_rate, aisle=rate_aisle)\n",
    "    \n",
    "    ## add the new ratings to the original ratings DataFrame\n",
    "    print('Creating ratings dataset...')\n",
    "    new_ratings_df = new_rec_df.append(user_rating, ignore_index=True)\n",
    "    new_data = Dataset.load_from_df(new_ratings_df, reader)\n",
    "    \n",
    "    # train a model using the new combined DataFrame\n",
    "    print('Training recommendation model...')\n",
    "    new_user_svd = SVD(n_factors = 20, n_epochs = 10, lr_all = 0.005, reg_all = 0.4)\n",
    "    new_user_svd.fit(new_data.build_full_trainset())\n",
    "    \n",
    "    # make predictions for the user\n",
    "    print('Making predictions...')\n",
    "    list_of_products = []\n",
    "    for product in new_ratings_df['product_id'].unique():\n",
    "        product_name = products_desc[products_desc['product_id'] == product]['product_name'].iloc[0]\n",
    "        product_aisle = products_desc[products_desc['product_id'] == product]['aisle'].iloc[0]\n",
    "        list_of_products.append((product, new_user_svd.predict(300000, product)[3], product_name, product_aisle))\n",
    "    \n",
    "    # order the predictions from highest to lowest rated\n",
    "    ranked_products = sorted(list_of_products, key=lambda x:x[1], reverse=True)\n",
    "    \n",
    "    # return the top n recommendation\n",
    "    return recommend_diverse_products(ranked_products, n_to_rec, aisle=rec_aisle, percent_diverse=percent_diverse)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " No Salt Added Solid Light Tuna In Water\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "3\n",
      "\n",
      " Beef Bone Broth\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "3\n",
      "\n",
      " Slow Cookers BBQ Pulled Pork Seasoning Mix\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "1\n",
      "\n",
      " Honey Battered Breast Tenders\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "0\n",
      "\n",
      " Meatless Lentil Sage Meatloaf\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "0\n",
      "\n",
      " Flat Fillets of Anchovies in Olive Oil\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "3\n",
      "\n",
      " Lemon Herb Asparagus Seasoning Sauce\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "0\n",
      "\n",
      " Beef Bologna\n",
      "How do you rate this product on a scale of 1-5, choose 0 to rate a different product:\n",
      "4\n",
      "Creating ratings dataset...\n",
      "Training recommendation model...\n",
      "Making predictions...\n",
      "Recommendation #  2 :  Lo-Carb Energy Drink \n",
      "\n",
      "Recommendation #  6 :  Organic Grade A Raw Whole Milk \n",
      "\n",
      "Recommendation #  14 :  DanActive Vanilla Probiotic Dairy Drink \n",
      "\n",
      "Recommendation #  19 :  Diet Orange & Cream Soda \n",
      "\n",
      "Recommendation #  22 :  Plain Soy Milk \n",
      "\n",
      "Recommendation #  25 :  Alimentum Infant Formula \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get a clean copy of the rec dataframe before running\n",
    "new_rec_df = pickle.load(open(\"Pickle/new_rec_df.p\", \"rb\"))\n",
    "\n",
    "recs = generate_new_user_recommendations(new_rec_df, products_desc, n_to_rate=5, n_to_rec=6, \n",
    "                                  percent_diverse=1, rate_aisle='meat', rec_aisle=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpret Results / Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results: \n",
    "\n",
    "* Cluster data is fabulous for market segmentation and targeted marketing\n",
    "* It is important to look at the buying power of each cluster to determine how much effort to put into targeting\n",
    "* The SVD model created much better predictions when we lowered the rating scale of the products to 1-5\n",
    "* Popularity bias skewed the SVD model but it can be controlled by personalized re-ranking of the recommendation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next Steps/Future Work: \n",
    "\n",
    "* Go back and cluster again allowing more than 20 clusters to make some of the big clusters smaller?  \n",
    "* Create a dashboard using DASH for graphical representation of the clusters\n",
    "* Generate word embeddings for the search engine for more specific search results\n",
    "* Create SQL tables of the data and load onto AWS\n",
    "* Use Heroku to push local FLASK app to the web\n",
    "* Perform market basket analysis by cluster using efficient-apriori model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work - Move data in SQL tables (on AWS?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sqlalchemy import create_engine\n",
    "\n",
    "# engine = create_engine('sqlite:///grocery.db', echo=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# user_data.to_sql('users', con=engine, if_exists='replace')  generates a memory error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result = engine.execute('SELECT * from users;')\n",
    "# pd.DataFrame(result.fetchall(), columns = result.keys())"
   ]
  }
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