eda_ml.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ysS5rgTMWpwL"
   },
   "source": [
    "# **Capstone project: Providing data-driven suggestions for HR**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pZNZpq0EotHt"
   },
   "source": [
    "## Description and deliverables\n",
    "\n",
    "This capstone project is an opportunity for you to analyze a dataset and build predictive models that can provide insights to the Human Resources (HR) department of a large consulting firm.\n",
    "\n",
    "Upon completion, you will have two artifacts that you would be able to present to future employers. One is a brief one-page summary of this project that you would present to external stakeholders as the data professional in Salifort Motors. The other is a complete code notebook provided here. Please consider your prior course work and select one way to achieve this given project question. Either use a regression model or machine learning model to predict whether or not an employee will leave the company. The exemplar following this actiivty shows both approaches, but you only need to do one.\n",
    "\n",
    "In your deliverables, you will include the model evaluation (and interpretation if applicable), a data visualization(s) of your choice that is directly related to the question you ask, ethical considerations, and the resources you used to troubleshoot and find answers or solutions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **PACE stages**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kh258TrMicdM"
   },
   "source": [
    "![Screenshot 2022-08-04 5.47.37 PM.png](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psz51YkZVwtN"
   },
   "source": [
    "## **Pace: Plan**\n",
    "\n",
    "Consider the questions in your PACE Strategy Document to reflect on the Plan stage.\n",
    "\n",
    "In this stage, consider the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gLEEr6peWcF7"
   },
   "source": [
    "### Understand the business scenario and problem\n",
    "\n",
    "The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They refer to you as a data analytics professional and ask you to provide data-driven suggestions based on your understanding of the data. They have the following question: what’s likely to make the employee leave the company?\n",
    "\n",
    "Your goals in this project are to analyze the data collected by the HR department and to build a model that predicts whether or not an employee will leave the company.\n",
    "\n",
    "If you can predict employees likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lnRdR6eacUkK"
   },
   "source": [
    "### Familiarize yourself with the HR dataset\n",
    "\n",
    "The dataset that you'll be using in this lab contains 15,000 rows and 10 columns for the variables listed below. \n",
    "\n",
    "**Note:** you don't need to download any data to complete this lab. For more information about the data, refer to its source on [Kaggle](https://www.kaggle.com/datasets/mfaisalqureshi/hr-analytics-and-job-prediction?select=HR_comma_sep.csv).\n",
    "\n",
    "Variable  |Description |\n",
    "-----|-----|\n",
    "satisfaction_level|Employee-reported job satisfaction level [0&ndash;1]|\n",
    "last_evaluation|Score of employee's last performance review [0&ndash;1]|\n",
    "number_project|Number of projects employee contributes to|\n",
    "average_monthly_hours|Average number of hours employee worked per month|\n",
    "time_spend_company|How long the employee has been with the company (years)\n",
    "Work_accident|Whether or not the employee experienced an accident while at work\n",
    "left|Whether or not the employee left the company\n",
    "promotion_last_5years|Whether or not the employee was promoted in the last 5 years\n",
    "Department|The employee's department\n",
    "salary|The employee's salary (U.S. dollars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PoC0_CTLaK2p"
   },
   "source": [
    "💭\n",
    "### Reflect on these questions as you complete the plan stage.\n",
    "\n",
    "*  Who are your stakeholders for this project?\n",
    "- What are you trying to solve or accomplish?\n",
    "- What are your initial observations when you explore the data?\n",
    "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n",
    "- Do you have any ethical considerations in this stage?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EAfj2mxlHyO3"
   },
   "source": [
    "* HR Department of the Sailfort Motors are my stakeholders for this project\n",
    "* Trying to build a machine learning model that predicts whether an employee will quit or not \n",
    "* Naming convention of columns is not the best. There are no missing values in any of the rows, but there are 30,080 duplicated rows\n",
    "* I have used https://www.educba.com/seaborn-subplots/ for subplot troubleshooting and stackoverflow for some errors\n",
    "* Yes, dropping bias columns such as gender or personal information related to the user, which is not found in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dfnvw1HZsfbd"
   },
   "source": [
    "## Step 1. Imports\n",
    "\n",
    "*   Import packages\n",
    "*   Load dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "51UAXIOLC_8P"
   },
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "hVWGpX9As4e1"
   },
   "outputs": [],
   "source": [
    "# Import packages\n",
    "### YOUR CODE HERE ### \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import accuracy_score,precision_score,f1_score,recall_score,confusion_matrix,ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zM2P9yLWDIjN"
   },
   "source": [
    "### Load dataset\n",
    "\n",
    "`Pandas` is used to read a dataset called **`HR_capstone_dataset.csv`.**  As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Bs0cJR5BDPgQ"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RUN THIS CELL TO IMPORT YOUR DATA. \n",
    "\n",
    "# Load dataset into a dataframe\n",
    "### YOUR CODE HERE ###\n",
    "df0 = pd.read_csv(\"HR_capstone_dataset.csv\")\n",
    "\n",
    "\n",
    "# Display first few rows of the dataframe\n",
    "### YOUR CODE HERE ###\n",
    "df0.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wF_LLorPs5G_"
   },
   "source": [
    "## Step 2. Data Exploration (Initial EDA and data cleaning)\n",
    "\n",
    "- Understand your variables\n",
    "- Clean your dataset (missing data, redundant data, outliers)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3LF6h1v9FYz2"
   },
   "source": [
    "### Gather basic information about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "6XbfdPoKurMf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_montly_hours   14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   Department             14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Gather basic information about the data\n",
    "### YOUR CODE HERE ###\n",
    "df0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6JMl_rQ1Fgte"
   },
   "source": [
    "### Gather descriptive statistics about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "_5VRL-kzE8y1"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "count        14999.000000     14999.000000    14999.000000   \n",
       "mean             0.612834         0.716102        3.803054   \n",
       "std              0.248631         0.171169        1.232592   \n",
       "min              0.090000         0.360000        2.000000   \n",
       "25%              0.440000         0.560000        3.000000   \n",
       "50%              0.640000         0.720000        4.000000   \n",
       "75%              0.820000         0.870000        5.000000   \n",
       "max              1.000000         1.000000        7.000000   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident          left  \\\n",
       "count          14999.000000        14999.000000   14999.000000  14999.000000   \n",
       "mean             201.050337            3.498233       0.144610      0.238083   \n",
       "std               49.943099            1.460136       0.351719      0.425924   \n",
       "min               96.000000            2.000000       0.000000      0.000000   \n",
       "25%              156.000000            3.000000       0.000000      0.000000   \n",
       "50%              200.000000            3.000000       0.000000      0.000000   \n",
       "75%              245.000000            4.000000       0.000000      0.000000   \n",
       "max              310.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  \n",
       "count           14999.000000  \n",
       "mean                0.021268  \n",
       "std                 0.144281  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gather descriptive statistics about the data\n",
    "### YOUR CODE HERE ###\n",
    "df0.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QR7eFNU0FklJ"
   },
   "source": [
    "### Rename columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_TtE0JLPJyLF"
   },
   "source": [
    "As a data cleaning step, rename the columns as needed. Standardize the column names so that they are all in `snake_case`, correct any column names that are misspelled, and make column names more concise as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "kEn21u2bqrEI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'Department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all column names\n",
    "### YOUR CODE HERE ###\n",
    "df0.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "npUQA8jMFJQD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_performance', 'numberof_projects',\n",
       "       'average_monthly_hours', 'yrs_in_company', 'accident_onwork', 'left',\n",
       "       'promotion_last_5years', 'department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns as needed\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "df0.columns=['satisfaction_level', 'last_performance', 'numberof_projects',\n",
    "       'average_monthly_hours', 'yrs_in_company', 'accident_onwork', 'left',\n",
    "       'promotion_last_5years', 'department', 'salary'] \n",
    "# Display all column names after the update\n",
    "### YOUR CODE HERE ###\n",
    "df0.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e-G2QlQxBq__"
   },
   "source": [
    "### Check missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GeiUUqeaBt-I"
   },
   "source": [
    "Check for any missing values in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "EN9MvN0GByVV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_performance         0\n",
       "numberof_projects        0\n",
       "average_monthly_hours    0\n",
       "yrs_in_company           0\n",
       "accident_onwork          0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "department               0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "### YOUR CODE HERE ###\n",
    "df0.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hBvrijItKQI9"
   },
   "source": [
    "### Check duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q7ystBsdsGaL"
   },
   "source": [
    "Check for any duplicate entries in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "CFFLc5AOZ7-x"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30080"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "### YOUR CODE HERE ###\n",
    "df0[df0.duplicated()].size\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "ZHGlDbKAcBLM"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>14211</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_performance  numberof_projects  \\\n",
       "0                    0.38              0.53                  2   \n",
       "12000                0.38              0.53                  2   \n",
       "14211                0.38              0.53                  2   \n",
       "\n",
       "       average_monthly_hours  yrs_in_company  accident_onwork  left  \\\n",
       "0                        157               3                0     1   \n",
       "12000                    157               3                0     1   \n",
       "14211                    157               3                0     1   \n",
       "\n",
       "       promotion_last_5years department salary  \n",
       "0                          0      sales    low  \n",
       "12000                      0      sales    low  \n",
       "14211                      0      sales    low  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect some rows containing duplicates as needed\n",
    "### YOUR CODE HERE ###\n",
    "df0[(df0['satisfaction_level']==0.38) & ( df0['last_performance']==0.53) & (df0['numberof_projects']==2) & (df0['average_monthly_hours']==157) & (df0['yrs_in_company']==3) & (df0['accident_onwork']==0) & (df0['left']==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "wCr34Rppdjay"
   },
   "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>satisfaction_level</th>\n",
       "      <th>last_performance</th>\n",
       "      <th>numberof_projects</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>yrs_in_company</th>\n",
       "      <th>accident_onwork</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_performance  numberof_projects  \\\n",
       "0                0.38              0.53                  2   \n",
       "1                0.80              0.86                  5   \n",
       "2                0.11              0.88                  7   \n",
       "3                0.72              0.87                  5   \n",
       "4                0.37              0.52                  2   \n",
       "\n",
       "   average_monthly_hours  yrs_in_company  accident_onwork  left  \\\n",
       "0                    157               3                0     1   \n",
       "1                    262               6                0     1   \n",
       "2                    272               4                0     1   \n",
       "3                    223               5                0     1   \n",
       "4                    159               3                0     1   \n",
       "\n",
       "   promotion_last_5years department  salary  \n",
       "0                      0      sales     low  \n",
       "1                      0      sales  medium  \n",
       "2                      0      sales  medium  \n",
       "3                      0      sales     low  \n",
       "4                      0      sales     low  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop duplicates and save resulting dataframe in a new variable as needed\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "df=df0.drop_duplicates()\n",
    "# Display first few rows of new dataframe as needed\n",
    "### YOUR CODE HERE ###\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4knHIoTIFu83"
   },
   "source": [
    "### Check outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EaVx2fk8GC_m"
   },
   "source": [
    "Check for outliers in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pilaGYgh4LHM"
   },
   "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"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# Create a boxplot to visualize distribution of `tenure` and detect any outliers\n",
    "### YOUR CODE HERE ###\n",
    "    \n",
    "for i in range(5):\n",
    "    sns.boxplot(x=df[df.columns[i]])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "ohctgiHyFykI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(824, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of rows containing outliers\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "df[df['yrs_in_company']>5].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eP_rPN31Kmx0"
   },
   "source": [
    "Certain types of models are more sensitive to outliers than others. When you get to the stage of building your model, consider whether to remove outliers, based on the type of model you decide to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mA7Mz_SnI8km"
   },
   "source": [
    "# pAce: Analyze Stage\n",
    "- Perform EDA (analyze relationships between variables)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gmk6oBT3jgbA"
   },
   "source": [
    "💭\n",
    "### Reflect on these questions as you complete the analyze stage.\n",
    "\n",
    "- What did you observe about the relationships between variables?\n",
    "- What do you observe about the distributions in the data?\n",
    "- What transformations did you make with your data? Why did you chose to make those decisions?\n",
    "- What are some purposes of EDA before constructing a predictive model?\n",
    "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n",
    "- Do you have any ethical considerations in this stage?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mbosyCrJCWcd"
   },
   "source": [
    "- Variables such as `average number of hours` and `number of projects` is linear, similar to `Years in company` with `number of projects`. These are the varaibles with highest correlation.  \n",
    "- Continous variables such as `average monthly hours` and `number of projects` are normally distributed, whereas `last performance` seems to be uniformly distributed, `years in company` and `satisfaction level` are left & right skewed.  \n",
    "- I changed categorical variables such as `department` and `salary` in order to use them in model training with the help of hot encoding.  \n",
    "- Exploratory Data Analysis can help us understand the data and correlations between different variables in order to select apporpriate variables for model training and also extract insights about what is happening.  \n",
    "- Seaborn Library was really helpful and old labs of course 3 came in handy. Links[https://www.coursera.org/learn/go-beyond-the-numbers-translate-data-into-insight/ungradedLab/AVJSd/exemplar-course-3-waze-project-lab/lab?path=%2Fnotebooks%2FExemplar_Course%25203%2520Waze%2520project%2520lab.ipynb, https://seaborn.pydata.org/]  \n",
    "- I have to represent the data accurately, make sure no bias is causing the data to be skewed or mispresentation of the data for the sake of desired outcome."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KDcWrk57kao2"
   },
   "source": [
    "## Step 2. Data Exploration (Continue EDA)\n",
    "\n",
    "Begin by understanding how many employees left and what percentage of all employees this figure represents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    83.39588\n",
       "1    16.60412\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get numbers of people who left vs. stayed\n",
    "### YOUR CODE HERE ###\n",
    "df.left.value_counts()\n",
    "# Get percentages of people who left vs. stayed\n",
    "### YOUR CODE HERE ###\n",
    "df.left.value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DmVMzXPSuYk1"
   },
   "source": [
    "### Data visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_hw4qkZzBr-W"
   },
   "source": [
    "Now, examine variables that you're interested in, and create plots to visualize relationships between variables in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Qf0VbjX8-DBQ",
    "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>satisfaction_level</th>\n",
       "      <th>last_performance</th>\n",
       "      <th>numberof_projects</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>yrs_in_company</th>\n",
       "      <th>accident_onwork</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_performance  numberof_projects  \\\n",
       "0                0.38              0.53                  2   \n",
       "\n",
       "   average_monthly_hours  yrs_in_company  accident_onwork  left  \\\n",
       "0                    157               3                0     1   \n",
       "\n",
       "   promotion_last_5years department salary  \n",
       "0                      0      sales    low  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4948a2d90>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.average_monthly_hours,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4948721d0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.numberof_projects,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4945c0f10>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.last_performance,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af49a75acd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.yrs_in_company,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af494511490>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.satisfaction_level,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "F8HlhjMy9X3A"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af496759990>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "sns.lineplot(y=df.satisfaction_level,x=df.numberof_projects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4967dac50>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x=df.left,y=df.satisfaction_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4969c6a50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x=df.left,y=df.yrs_in_company)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af496802310>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(x=df.satisfaction_level,y=df.salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "NUyBruMee-EI",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4968d6f50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "sns.lineplot(x=df.average_monthly_hours,y=df.satisfaction_level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af49a3535d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.barplot(y=df.average_monthly_hours,x=df.left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "3v1uJR5y3MEy"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "fig,ax=plt.subplots(figsize=(15,8))\n",
    "sns.barplot(y=df.satisfaction_level,x=df.department)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "UCVs81NILbhn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4963c4810>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "sns.histplot(x=df.last_performance,hue=df.left,multiple='dodge',shrink=0.9,bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "cGitCvzvdbjF"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af49a959850>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAOBe5nsk/nn931eOtLUkj1nccgAAAIDZzCvEt9b2H3chAAAAwNzmFeKr6rkztbfW3rm45QAAAACzme/p9IeNdO+e5BeSfCmJEA8AAABLZL6n0//W6POqenCSvx1LRQAAAMCM5nt3+um+m+TAxSwEAAAAmNt8r4n/cLq70SfJLkken+SccRUFAAAA3Nd8r4n/i5HuO5P8S2ttagz1AAAAALOY1+n0rbVPJ/lakr2SPDTJ98dZFAAAAHBf8wrxVfVrSS5N8uwkv5bki1V1/DgLAwAAAO5tvqfT/2GSw1prNyVJVe2T5O+TvG9chQEAAAD3Nt+70z9gW4Dv3bKAYQEAAIBFMN8j8RdU1ceTnNU/PyHJ+eMpCQAAAJjJnCG+qh6b5JGttVdW1X9M8jNJKsklSd69BPUBAAAAve2dEv9XSW5NktbaB1prv9Na++10R+H/atzFAQAAAPfYXohf21r7yvTG1tqGJGvHUhEAAAAwo+2F+N3neG2PxSwEAAAAmNv2QvxlVfXi6Y1V9aIkl4+nJAAAAGAm27s7/SuSfLCqnpN7Qvu6JD+S5FnjLAwAAAC4tzlDfGvtG0mOrKqfT3JI3/zR1tonx14ZAAAAcC/z+p341trFSS4ecy0AAADAHLZ3TTwAAACwTAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAyEEA8AAAADIcQDAADAQAjxAAAAMBBCPAAAAAzE2EJ8VZ1RVTdV1VUjbQ+rqk9U1cb+70NHXnt1VV1XVddW1TEj7U+sqiv7106rqhpXzQAAALCcjfNI/JlJnjqt7VVJLmqtHZjkov55quqgJOuTHNwP86aq2qUf5s1JTkpyYP+YPk4AAADYKYwtxLfWPpPkW9Oaj0vyjr77HUmeOdL+ntbaHa21G5Jcl+TwqnpUkge11i5prbUk7xwZBgAAAHYqS31N/CNbazcmSf/3EX37qiSbR/qb6ttW9d3T22dUVSdV1Yaq2nDzzTcvauEAAAAwacvlxnYzXefe5mifUWvt9Nbautbaun322WfRigMAAIDlYKlD/Df6U+TT/72pb59Ksmakv9VJtvbtq2doBwAAgJ3OUof485I8r+9+XpJzR9rXV9UDq2r/dDewu7Q/5f7WqnpSf1f6544MAwAAADuVXcc14qo6K8nRSfauqqkkf5TkdUnOqaoXJfl6kmcnSWvt6qo6J8k1Se5Mckpr7a5+VL+R7k73eyT5WP8AAACAnc7YQnxr7cRZXvqFWfo/NcmpM7RvSHLIIpYGAAAAg7RcbmwHAAAAbIcQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAMvYqjX7paqW7WPVmv0mPYsAdiq7TroAAABmt3Vqc0546+cnXcaszj75yEmXALBTcSQeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAghHgAAIABWbVmv1TVsnysWrPfpGfPirfrpAsAAABg/rZObc4Jb/38pMuY0dknHznpElY8R+IBAABgIIR4AAB2Wk5LBobG6fQAAOy0nJYMDI0j8QAAwKJzlgOMhyPxbFdVTbqEGT169b7ZtHnLpMsAAGAGznKA8RDi2a729qdPuoQZ1Qs+OukSAAAAlpTT6QEAAGAgJhLiq2pTVV1ZVVdU1Ya+7WFV9Ymq2tj/fehI/6+uquuq6tqqOmYSNQMAAMCkTfJI/M+31g5tra3rn78qyUWttQOTXNQ/T1UdlGR9koOTPDXJm6pql0kUDAAAAJO0nE6nPy7JO/rudyR55kj7e1prd7TWbkhyXZLDJ1AfA+SuqAAAwEoyqRvbtSQXVlVL8tbW2ulJHtlauzFJWms3VtUj+n5XJfnCyLBTfdt9VNVJSU5Kkv32E5BwV1QAAGBlmVSIP6q1trUP6p+oqq/N0e9Mv2/WZuqx3xlwepKsW7duxn4AAABgqCZyOn1rbWv/96YkH0x3evw3qupRSdL/vanvfSrJmpHBVyfZunTVAjNxqQIAACy9JT8SX1U/muQBrbVb++6nJPkfSc5L8rwkr+v/ntsPcl6Sv6uqv0yyb5IDk1y61HUD9+ZSBQAAWHqTOJ3+kUk+WFXbpv93rbULquqyJOdU1YuSfD3Js5OktXZ1VZ2T5JokdyY5pbV21wTqBgAAgIla8hDfWrs+yU/N0H5Lkl+YZZhTk5w65tIAAABgWVtOPzEHAAAAzEGIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIEQ4gEAAGAglvx34gEYtqqadAmzevTqfbNp85ZJlwEAMDZCPAAL0t7+9EmXMKt6wUcnXcJOa9Wa/bJ1avOky5jVvqvXZMvmr0+6DADYYUI8ALDDtk5tzglv/fyky5jV2ScfOekSAGBRuCYeAAAABkKIBwAAgIEQ4gEAAGAghHgAAAAYCCEeAAAABkKIBwAAgIHwE3MAsExU1aRLmNWjV++bTZu3TLoMANjpCfEAsEy0tz990iXMql7w0UmXAADE6fQAAAAwGEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBA7DrpAgAAGLaqmnQJM3r06n2zafOWSZcBsKiEeAAAdkh7+9MnXcKM6gUfnXQJAIvO6fQAAAAwEEI8AAAADIQQDwAAAAPhmniYIDcCGh/zFoCVwPfZ+Ji3DJUQDxPkRkDjY94CsBL4Phsf85ahcjo9AAAADIQj8QAAACwalyqMlxAPAADAonGpwng5nR4AAAAGQogHAACAgXA6PQCwU3CNJgArgRAPAOwUXKMJwEowmNPpq+qpVXVtVV1XVa+adD0AAACw1AYR4qtqlyT/N8kvJzkoyYlVddBkqwIAAIClNYgQn+TwJNe11q5vrX0/yXuSHDfhmgAAAGBJDSXEr0qyeeT5VN8GAAAAO41qrU26hu2qqmcnOaa19l/657+e5PDW2m9N6++kJCf1Tx+X5NolLXRl2jvJNyddxApl3o6PeTs+5u34mLfjZf6Oj3k7Pubt+Ji342PeLo5Ht9b2memFodydfirJmpHnq5Nsnd5Ta+30JKcvVVE7g6ra0FpbN+k6ViLzdnzM2/Exb8fHvB0v83d8zNvxMW/Hx7wdH/N2/IZyOv1lSQ6sqv2r6keSrE9y3oRrAgAAgCU1iCPxrbU7q+qlST6eZJckZ7TWrp5wWQAAALCkBhHik6S1dn6S8yddx07I5QnjY96Oj3k7Pubt+Ji342X+jo95Oz7m7fiYt+Nj3o7ZIG5sBwAAAAznmngAAADY6QnxzKqqzqyq4yddx86qqtZW1X8aeb6uqk6bZE3LVT+vrpp0HctJVT2kqn7zfg674M9+Vf1NVR10P6Z1dFV9ZKHDcV9V9Yqq+neTroN7TF+PrxRVdVtV/URVXdE/vlVVN/Tdfz/p+mDUQr9nqur5VbXvyPP79f02ZFV1V/95vqqqPlxVD1mk8T6/qt7Yd7+mqrb009lYVR/Y2ebzjhDiYflam+Tujb/W2obW2ssmV86wVdVg7gGySB6S5H6F+PujtfZfWmvXLNX0uLeq2iXJK5II8cvL2oysx1eS1tqVrbVDW2uHpvvFoFf2z39x0rUtV6MB5n4Ou+/2+2TU/fzuf36Su+f1Tvr99r3+83xIkm8lOWVM03lDP50Dk5yd5JNVNePvonNvQvxOpqp+tKo+WlVf7veunVBV/72qLuufn15VNcNwT6yqT1fV5VX18ap6VN/+sqq6pqq+UlXvWfp3tPiq6kP9+7y6qk7q255aVV/q59tFfdueVfX2qrqyf/+/2ref2LddVVV/NjLe20a6j6+qM/vuM6vqtKr6fFVdP3IE9HVJfrbfQ/nbo3uS+72XZ1TVp/phXjYy7v9WVV+rqk9U1VlV9XtjnmXLxS5V9df9/+3Cqtqjnz9/UlWfTvLySRe4xF6X5IB++Xl9Vb2y/5x/par+eFtPVfXcvu3LVfW3I8M/efoy2S+Dn6qq9/XL2Lu3rS/69nV990yfl8P78f1j//dxSzcrxmuW9eqmqtq7f31dVX2q735NVf1tVX2yP/Lw4r796Kr6TFV9sF+nvqWqHtC/Nus6par+R1V9MckfptvovLiqLl7qebA91R2R/lp1R7Su6pedX6yqz/Xz4fDZlpHqwssHquqCvt8/Hxnvm6tqQ/+5H12un9ZP77P9+nXbuvNH+3XnZf10jhuZxoeqO+J0Q1W9tKp+p+/nC1X1sL6/A/o6Lq+qf6iqH+/b57UeX5q5zVKrbifauD0/I8FypVuEdcZ7q+rDSS6cNt7D+v4fUzNs2/af3XVJ3t1/brdtS2z7frutqk7t1/dfqKpH9u0H9M8v69fLt2XluCTJqmT27/LtrKdfUFX/VN222FGzTaS1dna6/9eK3PG56FprHjvRI8mvJvnrkecPTvKwked/m+QZffeZSY5PsluSzyfZp28/Id3P/CXJ1iQP7LsfMun3t0jz6GH93z2SXJXkkUk2J9l/2ut/luSvRoZ7aLov2K8n2Sfdrz98Mskz+9dvG+n3+CRnjszn96bbqXZQkuv69qOTfGRkmLufJ3lN/z95YJK9k9zS/5/WJbmir32vJBuT/N6k5+kS/M/WJrkzyaH983OS/Ockn0rypknXN8F5clXf/ZR0d4qtfjn7SJInJzk4ybVJ9u7727Zsz7VMfifJ6v61S5L8TP/ap/rlb59ZPi8PSrJr3/2LSd4/fbke6iMzr1c3jczXdUk+1Xe/JsmX+8/o3v282refD7cneUy6n1L9RL+emGud0pL82sh0757mcnuMfEZ/ol92Lk9yRr9MHpfkQ3MsI89Pcn0/X3dP8i9J1kxbvnbpl8Gf7PsZXQbPyj3rzj9J8p/77ock+ackP9pP47p06819+uX8JX1/b0jyir77oiQH9t0/neST8/jMDHr5nuX/edu052cmOX7SdY1x2f1aknck+UqS96U742VTkv+e5LNJ1ic5McmV6bYb/mxk+Bf0y9mnk/x1kjfONM9y722E3+/H9eV0O4KOT3JbuvX1FUn2mKXWTf0yfkmSDUn+fbqfZ/7nkeX56P6z8r7+fb0799zo+ml922eTnDbJZTc7vs6Yyj3rh6PTfe8d2Y9nv8y9bfupJOtGarn7ebr17rbt5D9P8v/13R9JcmLf/ZLpn5GhPbbVn27d+t4kT+2fL2g9neRRuec77EeSfG7kM/CaTNtGTXdG2Zsn/f6H8NjZTi+l+1L4i/5ozkdaa/9QVb9aVb+f7kvpYUmuTvLhkWEel+SQJJ+o7qDbLklu7F/7Srq9lR9Kt0JdCV5WVc/qu9ckOSnJZ1prNyRJa+1b/Wu/mO6LO337v1bVk9NtrN+cJFX17nRhaXvz5kOttR8muWbbXt15+Ghr7Y4kd1TVTel2NvxMknNba9/rp//huUawwtzQWrui77483QZA0p2etbN7Sv/4x/75nkkOTPJTSd7XWvtmcq9lO5l9mby0tTaVJFV1Rbr5/NmR15+UmT8vD07yjqo6MN1G0G6L9/Ymbqb16lz9b/uMfq8/an54km+nm7fXJ0lVnZXu8/yDzL5OuSvJ+8f0nsbhhtbalUlSVVcnuai11qrqynTL0VzLyEWtte/0w16T5NHpgvqvVXfG1K7pNhYPSrfBf/22ZcxZbGEAAAoRSURBVDBdiD+p735KkmPrnjOUdk+3QZ8kF7fWbk1ya1V9J/d8D16Z5Ceras90IeC9I//fB47UeH/W4wzD45K8qLX2uao6I/dcqnR7a+1nqjvN/QtJnpjkX5NcWFXPTPLFJH/ct38nycW5Zz08o6r65STPTPLTrbXvVtXDWmvfqqqXpgs8G7ZT6+bW2hFV9YZ0OwqOSrecX53kLX0/T0i3E3drulB1VFVtSPLWJE9urd3Qr4MmbUfWGZ+Y9p32+HQ7s5/SWttaVYdk9m3buXw/XWBPum2NX+q7j0j3f0uSv0vyFwt5o8vQHiPf8Zen27GcLHw9vXfu/R12dpIfm2O6c355cg8hfifTWvunqnpiur2tf1pVF6a7zmVda21zVb0m3cp+VCW5urV2xAyjfHq6Dcpjk/y3qjq4tXbn+N7BeFXV0enC+RH9l+en0u0Jn+nU30q3ApveNpvRfqfP4zvmOY7Zhrkr3ed5Z175TZ8fe/Td/28CtSw3leRPW2tvvVdjdxnGbL8zOtsyOdNyN31aM43ztelC0rOqam26Ixsrwizr1TtzzyVr0z/v0+dPm6N9rs/07a21u+5HyZMyuuz8cOT5D9MtR3MtI/dZ7qpq/yS/l+Swfifqmenm9VzzrJL8amvt2ns1Vv30POp7QJJvt+4a8O29v515XbwSbW6tfa7vfleSbZewbdtJfFhm3tmWLCzAJN02yNtba99N7rNzdT7O6/9emWTPkR1Tt9c9NyebaWfsbZl959ek7Mg6Y/p3/43p1g9PSLfzYq5t27n8oLW2bV0903fgSvG91tqhVfXgdDstTkl3dsaC1tN990J+z/wJ6c4iYTtcE7+T6fcWf7e19q50ewn/ff/SN/ujDDPdkfraJPtU1RH9OHarqoOru15zTWvt4nSnfj0k3RG+IXtwkn/tA/yPpzuq+MAkP9dvMKb6ayPTXbfz0m0DVtVD0+11/7mq2ru6a+ROTHcKXZJ8o6oe38+3bUf653JrulM7F+KzSZ5RVbv3/8+nL3B4Vo7R5efjSV7YLxOpqlVV9Yh0pwb/WlU9vG9/2IxjWphLMvPn5cFJtvTdz1+E6Swbs6xXN6U7+pZ0p9uPOq7/jD483Wmel/Xth1fV/v064oR0n+e51inT3Z91xnKy0GXkQek21L/TH/n+5b79a0ke029gJt283ObjSX6r6u57OTxhvsW11v4tyQ1V9ex+2Kqqn9rOYEP/n9CZbcfbtqA43x34o+7e0dcvjz8yMq6FhJ7pRoPu9BC867R+kmEfBFjIOuPb6baJ/qQ/YDPjtm3f7/353H4h96zr18/V45D0R9ZfluT3qmq3LHw9/cUkR1fVw/vhnz1bj9XdW+op6XYgsR1C/M7nJ5Jc2u95/cMk/zPdNVpXpjs987LpA7TWvp8u3P9ZVX053fVYR6Y79ehd/WlN/5juDpPfXpJ3MT4XpDvC85V0exu/kOTmdHujP9C//2173v9nkodWd8OVLyf5+dbajUlene6UuS8n+VJr7dy+/1el25v5yczvlK2vJLmzupunzOuGSK21y9Lthf9ykg+k25v5nfkMy8rSWrslyeeq++m9X0p3et8l/ef1fUn2aq1dneTUJJ/ul+G/XITpzvZ5+fN0R6k/l27dsZLMtF794yT/u6r+Id1G8qhLk3w03frlta21rX37Jemuf70qyQ1JPriddcp0pyf5WC3DG9vN04KWkdbal9N991yd7lrZz/Xt30t3uvMFVfXZJN/IPevB16Y7/fMr/WfjtQus8TlJXtQv21enuzZ3Lgtej7Ms7bct7KXbkfbZaa/PtrNtrgCzKffs6Dsu95yWfGG6na7/LrnXjtBx7xCaa+fXcrXQdcY3kjwjyf9Nd8R3pm3bpLsM4S3V39hunrW8IsnvVNWl6S7tWTHbXq21f0z3/bM+C5/nN6a79v2SJH+f5EvTevntfj5vTHcvo/+w7cwV5lb3nBECrARVtWdr7bZ+A+AzSU5qrU1faQIT0F+ydFtr7S+mtR+d7nrXX5lEXSvNyHqw0m2wb2ytvWHSdTE8fag9P9336ZHpbhj760muSXcp4jf7/v5Tuh1uleT81trv9+0v6NtvTBcUd2mtvbQ/e+TcdAfULkryW621bWdLvSrJc9Ndf31+a+0P+qOUf5Lke+ku+fveDLVu2lZTVT2/737p6GvprgO/e11T3U/ebWitnVlVz0jy+iTfTLez8ZGttecswmxc8fptru/11+yvT3eTu+3t5IP7TYiHFaaq/i7dzZ12T/KO1tqfTrgkoCfEL43+qPfz0p2i/I9JXrztGmNYiD7Ef6R1v5e9otn5df9V1c8meWO6nTjfTvLC1tp1k62KlUyIBwCAGexkId7OLxgIIR4AAAaiqj6YZP9pzf+1tfbxSdQDLD0hHgAAAAbC3ekBAABgIIR4AAAAGAghHgBWmKp6TVX93hjH/5Cq+s1xjX+ppwMAQyLEAwDzVlW7JHlIkqUI10s1HQAYDCEeAFaAqvrDqrq2qv4+yeP6tgOq6oKquryq/qGqfrxvP7Oq3tK3/VNV/UrfvrZv+1L/OLJvP7qqLq6qv0tyZZLXJTmgqq6oqtf3r3+6qs7px/e6qnpOVV1aVVdW1QH9ePapqvdX1WX946i+/TVVdUZVfaqqrq+ql/Vv617TWcr5CQDL1a6TLgAA2DFV9cQk65M8Id13+5eSXJ7k9CQvaa1trKqfTvKmJP+hH2xtkp9LckCSi6vqsUluSvJLrbXbq+rAJGclWdf3f3iSQ1prN/S/nX1Ia+3QfvpHJ/mpJI9P8q0k1yf5m9ba4VX18iS/leQVSf53kje01j5bVfsl+Xg/TJL8eJKfT7JXkmur6s1JXjU6HQBAiAeAleBnk3ywtfbdJKmq85LsnuTIJO+tqm39PXBkmHNaaz9MsrGqrk8Xom9I8saqOjTJXUl+bKT/S1trN8xRw2WttRv76f9zkgv79ivThfMk+cUkB43U86Cq2qvv/mhr7Y4kd1TVTUkeOe93DwA7ESEeAFaGNu35A5J8e46j2NP7b0l+O8k30h1Vf0CS20de/3/bmf4dI90/HHn+w9yzvfGAJEe01r43OmAf6keHvyu2UQBgRq6JB4Dh+0ySZ1XVHv2R7Wck+W6SG6rq2UlSnZ8aGebZVfWA/nr1xyS5NsmDk9zYH6H/9SS7zDK9W9Od9r5QFyZ56bYn/RH/udzf6QDAiiXEA8DAtda+lOTsJFckeX+Sf+hfek6SF1XVl5NcneS4kcGuTfLpJB9Ld9387emumX9eVX0h3an0Mx59b63dkuRzVXXVAm8497Ik66rqK1V1TZKXbOd93d/pAMCKVa1NP5sOAFjJqurMJB9prb1v0rUAAAvjSDwAAAAMhCPxAAAAMBCOxAMAAMBACPEAAAAwEEI8AAAADIQQDwAAAAMhxAMAAMBACPEAAAAwEP8/eykJ25O2IMMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1224x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "fig,ax=plt.subplots(figsize=(17,6))\n",
    "\n",
    "sns.histplot(x=df.department,hue=df.left,multiple='dodge',shrink=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "6TyBo1uxsSpc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4964daad0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "sns.histplot(x=df.salary,hue=df.left,multiple='dodge',shrink=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "lfo96dwwruZd",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4964ac910>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Create a plot as needed\n",
    "### YOUR CODE HERE ###\n",
    "sns.histplot(x=df.promotion_last_5years,hue=df.left,multiple='dodge',shrink=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af47b1ca650>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.histplot(x=df.accident_onwork,hue=df.left,multiple='dodge',shrink=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7af4961ef6d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=df.corr()\n",
    "f,ax=plt.subplots(figsize=(11,9))\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DeTmNVlAANLd"
   },
   "source": [
    "### Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sQT8YqymD-yL"
   },
   "source": [
    "We can extract various insights from the given data:\n",
    "- Starting with the low satisfaction levels of the employees that left, average satisfaction level less than 0.5 is leading people to leave the company.  \n",
    "- Another interesting insight is increase in `number of projects` i.e greater than 5 leads to exponential decrease in `satsifaction levels` which can result in employee leaving the company.  \n",
    "- `satisfaction level` across all department is constant but, large number of employees who leave the company are from `sales` department followed by second largest department being `technical`.  \n",
    "- `satisfaction level` across all salary is same,but `low` salary department has the most people leaving.  \n",
    "- `Number of years in the company` had suprising insights, an employee with average time of more than 4 years leaves the company.\n",
    "- `Last Performance` revealed that members who left either has an incredible performance level reaching 1 or just above average at 0.6 but never worse.\n",
    "- Correlation matrix also provided some insights about the data:\n",
    "    - `Accidents` has a -ve correlation with number of projects which is expected, but a -ve correlation with `left` was surprising to see\n",
    "    - Positive correlation of `Accidents` with `Satisfaction level` was quite unexpected, maybe some employees think if they are part of accidents maybe they will get less projects hence less number of projects which may lead to less monthly hours which makes their satisfaction level go up \n",
    "    - `Number of years in the company` column has the highest correlation with `left` column, which verifies our earlier assumption about how employees that spent 4 years or more in the company leaves it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lca9c8XON8lc"
   },
   "source": [
    "# paCe: Construct Stage\n",
    "- Determine which models are most appropriate\n",
    "- Construct the model\n",
    "- Confirm model assumptions\n",
    "- Evaluate model results to determine how well your model fits the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bf2BU6B1QmTg"
   },
   "source": [
    "🔎\n",
    "## Recall model assumptions\n",
    "\n",
    "**Logistic Regression model assumptions**\n",
    "- Outcome variable is categorical\n",
    "- Observations are independent of each other\n",
    "- No severe multicollinearity among X variables\n",
    "- No extreme outliers\n",
    "- Linear relationship between each X variable and the logit of the outcome variable\n",
    "- Sufficiently large sample size\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F3v7IVv2_ai5"
   },
   "source": [
    "💭\n",
    "### Reflect on these questions as you complete the constructing stage.\n",
    "\n",
    "- Do you notice anything odd?\n",
    "- Which independent variables did you choose for the model and why?\n",
    "- Are each of the assumptions met?\n",
    "- How well does your model fit the data?\n",
    "- Can you improve it? Is there anything you would change about the model?\n",
    "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n",
    "- Do you have any ethical considerations in this stage?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "moS7voiMOW67"
   },
   "source": [
    "- The `department` and `salary` variables seem to throw errors when involved in model training even after converting them into nominal variables, this might be due to some incorrect formatting, contact leadership team to know about the issue.  \n",
    "- `satisfaction_level` `last_performance` `numberof_projects` `average_monthly_hours` `yrs_in_company` `accident_onwork`  `promotion_last_5years` were chosen as they had a correlation with our target variable.  \n",
    "- Outcome `left` is categorical, observations are independent of each other since data of one employee does not affect the other unless there was an issue is data collection, there is no multicollinearity among X variables as correlation for highest correlation is simply 0.3 which is too low for assumption to be violated, only outliers in the data are present in number of years in the company which should be treated as legit values and are not global outliers so this assumption is ignored.  \n",
    "- We have trained 3 models in order to retrieve the best result, to improve the model we can restructure our data based on `department` and `salary` variables to enhance the performance.  \n",
    "- Documentation of Scikit-Learn, Course 6 Lab Exemplar.  \n",
    "Links:  \n",
    "- https://www.coursera.org/learn/the-nuts-and-bolts-of-machine-learning/ungradedLab/XPgRn/exemplar-course-6-waze-project-exemplar-lab/lab?path=%2Fnotebooks%2FExemplar_Course%25206%2520Waze%2520project%2520lab.ipynb  \n",
    "- https://www.coursera.org/learn/the-nuts-and-bolts-of-machine-learning/ungradedLab/gXAhh/exemplar-build-a-random-forest-model/lab?path=%2Fnotebooks%2FExemplar_Build%2520a%2520random%2520forest%2520model.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZDG9v-NCS69j"
   },
   "source": [
    "## Step 3. Model Building, Step 4. Results and Evaluation\n",
    "- Fit a model that predicts the outcome variable using two or more independent variables\n",
    "- Check model assumptions\n",
    "- Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QxCXjdpSaB8A"
   },
   "source": [
    "### Identify the type of prediction task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YcCoHhnKcEov"
   },
   "source": [
    "As we are predicting if an employee will stay or leave the company,it is a binomial classification task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yeUlo3W0ais-"
   },
   "source": [
    "### Identify the types of models most appropriate for this task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A0Mo6f9tOchC"
   },
   "source": [
    "We can use any of the classification models such as:\n",
    "- Logistic Classifier\n",
    "- Random Forest Classifier\n",
    "- XGBoost Classifier\n",
    "- Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OrW2oXy4OfD1"
   },
   "source": [
    "### Modeling\n",
    "\n",
    "Add as many cells as you need to conduct the modeling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "UePZZyi_Okdz"
   },
   "outputs": [],
   "source": [
    "# We will start with converting all categorical values into ordinal for modeling and store it in a another variable\n",
    "### YOUR CODE HERE ###\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "dff=df.copy()\n",
    "dff['department']=le.fit_transform(dff['department'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11991 entries, 0 to 11999\n",
      "Data columns (total 7 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     11991 non-null  float64\n",
      " 1   last_performance       11991 non-null  float64\n",
      " 2   numberof_projects      11991 non-null  int64  \n",
      " 3   average_monthly_hours  11991 non-null  int64  \n",
      " 4   yrs_in_company         11991 non-null  int64  \n",
      " 5   accident_onwork        11991 non-null  int64  \n",
      " 6   promotion_last_5years  11991 non-null  int64  \n",
      "dtypes: float64(2), int64(5)\n",
      "memory usage: 1007.5 KB\n"
     ]
    }
   ],
   "source": [
    "x=dff.drop(['left','salary','department'],axis=1)\n",
    "y=dff.left \n",
    "\n",
    "xtra,xtest,ytra,ytest=train_test_split(x,y,random_state=42,test_size=0.2)\n",
    "\n",
    "xtrain,xval,ytrain,yval=train_test_split(xtra,ytra,random_state=42,test_size=0.25)\n",
    "\n",
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getval(name,model,yval,ypred):\n",
    "    train_accuracy = accuracy_score(yval, ypred)\n",
    "    train_f1 = f1_score(yval, ypred)\n",
    "    train_precision = precision_score(yval, ypred)\n",
    "    train_recall = recall_score(yval, ypred)\n",
    "    table=pd.DataFrame({'model':[name],'accuracy':[train_accuracy],'f1':[train_f1],'precision':[train_precision],'recall':[train_recall]})\n",
    "    return table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  accuracy        f1  precision    recall\n",
       "0  LogisticReg Train  0.831943  0.292569   0.488281  0.208855"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing Training Metrics For Logistic Regression\n",
    "\n",
    "logmodel=LogisticRegression().fit(xtrain,ytrain)\n",
    "\n",
    "result=getval('LogisticReg Train',logmodel,ytrain,logmodel.predict(xtrain))\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Train</td>\n",
       "      <td>0.833611</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.681704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model  accuracy        f1  precision    recall\n",
       "0  LogisticReg Train  0.831943  0.292569   0.488281  0.208855\n",
       "0     Gaussian Train  0.833611  0.576882   0.500000  0.681704"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing Training Metrics For Naive Bayes-Gaussian Model\n",
    "gnc=GaussianNB().fit(xtrain,ytrain)\n",
    "result=pd.concat([result,getval('Gaussian Train',gnc,ytrain,gnc.predict(xtrain))],axis=0)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.983, f1=0.946, precision=0.982, recall=0.912, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.988, f1=0.963, precision=0.996, recall=0.933, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.981, f1=0.939, precision=0.986, recall=0.896, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.983, f1=0.946, precision=0.982, recall=0.912, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.988, f1=0.963, precision=0.991, recall=0.937, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.981, f1=0.939, precision=0.986, recall=0.896, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.985, f1=0.954, precision=0.991, recall=0.921, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.980, f1=0.937, precision=0.986, recall=0.892, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.983, f1=0.947, precision=0.995, recall=0.904, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.986, f1=0.957, precision=0.987, recall=0.929, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.980, f1=0.937, precision=0.986, recall=0.892, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.983, f1=0.948, precision=0.991, recall=0.908, total=   0.3s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.985, f1=0.952, precision=0.987, recall=0.921, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.989, f1=0.966, precision=0.996, recall=0.937, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.983, f1=0.946, precision=0.986, recall=0.908, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.986, f1=0.957, precision=0.991, recall=0.925, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.985, f1=0.953, precision=0.982, recall=0.925, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.989, f1=0.966, precision=0.996, recall=0.937, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.982, f1=0.943, precision=0.986, recall=0.904, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.986, f1=0.957, precision=0.991, recall=0.925, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.985, f1=0.952, precision=0.991, recall=0.916, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.988, f1=0.963, precision=0.991, recall=0.937, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.980, f1=0.937, precision=0.986, recall=0.892, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.985, f1=0.954, precision=0.991, recall=0.921, total=   0.2s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.985, f1=0.952, precision=0.991, recall=0.916, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.987, f1=0.961, precision=0.987, recall=0.937, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.981, f1=0.939, precision=0.986, recall=0.896, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.4s\n",
      "[CV] max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=10, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.985, f1=0.954, precision=0.991, recall=0.921, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.983, f1=0.948, precision=0.986, recall=0.912, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.987, f1=0.961, precision=0.996, recall=0.929, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.981, f1=0.941, precision=0.986, recall=0.900, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.985, f1=0.954, precision=0.991, recall=0.921, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.984, f1=0.950, precision=0.986, recall=0.916, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.988, f1=0.963, precision=0.991, recall=0.937, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.981, f1=0.939, precision=0.986, recall=0.896, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.985, f1=0.954, precision=0.991, recall=0.921, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.987, f1=0.959, precision=0.991, recall=0.929, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.980, f1=0.937, precision=0.986, recall=0.892, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.976, f1=0.925, precision=0.981, recall=0.875, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.983, f1=0.948, precision=0.991, recall=0.908, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.984, f1=0.950, precision=0.991, recall=0.912, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.987, f1=0.959, precision=0.991, recall=0.929, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.981, f1=0.939, precision=0.986, recall=0.896, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.5, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.983, f1=0.948, precision=0.991, recall=0.908, total=   0.3s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.001, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=50, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.1s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.833, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=0.5, min_samples_split=0.01, n_estimators=100, accuracy=0.834, f1=0.000, precision=0.000, recall=0.000, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.985, f1=0.955, precision=0.987, recall=0.925, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.990, f1=0.968, precision=1.000, recall=0.937, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.981, f1=0.941, precision=0.991, recall=0.896, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=50, accuracy=0.987, f1=0.961, precision=0.996, recall=0.929, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.985, f1=0.955, precision=0.987, recall=0.925, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.989, f1=0.966, precision=0.996, recall=0.937, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.982, f1=0.943, precision=0.991, recall=0.900, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.977, f1=0.927, precision=0.981, recall=0.879, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.001, n_estimators=100, accuracy=0.986, f1=0.957, precision=0.991, recall=0.925, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.985, f1=0.952, precision=0.991, recall=0.916, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.988, f1=0.963, precision=0.996, recall=0.933, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.981, f1=0.941, precision=0.991, recall=0.896, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=50, accuracy=0.985, f1=0.954, precision=0.995, recall=0.916, total=   0.2s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.985, f1=0.952, precision=0.991, recall=0.916, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.989, f1=0.966, precision=0.996, recall=0.937, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.981, f1=0.941, precision=0.991, recall=0.896, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.978, f1=0.930, precision=0.981, recall=0.883, total=   0.4s\n",
      "[CV] max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100 \n",
      "[CV]  max_depth=50, max_features=sqrt, max_samples=0.9, min_samples_leaf=1, min_samples_split=0.01, n_estimators=100, accuracy=0.987, f1=0.959, precision=0.995, recall=0.925, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 160 out of 160 | elapsed:   32.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=None,\n",
       "                                              oob_score=False, random_state=42,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'max_depth': [10, 50], 'max_features': ['sqrt'],\n",
       "                         'max_samples': [0.5, 0.9],\n",
       "                         'min_samples_leaf': [0.5, 1],\n",
       "                         'min_samples_split': [0.001, 0.01],\n",
       "                         'n_estimators': [50, 100]},\n",
       "             pre_dispatch='2*n_jobs', refit='recall', return_train_score=False,\n",
       "             scoring={'recall', 'accuracy', 'precision', 'f1'}, verbose=3)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(random_state=42)\n",
    "\n",
    "scoring={'accuracy','precision','recall','f1'}\n",
    "\n",
    "cv_params = {'n_estimators' : [50,100], \n",
    "              'max_depth' : [10,50],        \n",
    "              'min_samples_leaf' : [0.5,1], \n",
    "              'min_samples_split' : [0.001, 0.01],\n",
    "              'max_features' : [\"sqrt\"], \n",
    "              'max_samples' : [.5,.9]}\n",
    "\n",
    "model=GridSearchCV(rf,cv_params,scoring=scoring,cv=5,refit='recall',verbose=3)\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getrr(name,model,metric):\n",
    "    _dict = {'precision': 'mean_test_precision',\n",
    "                   'recall': 'mean_test_recall',\n",
    "                   'f1': 'mean_test_f1',\n",
    "                   'accuracy': 'mean_test_accuracy',\n",
    "                   }\n",
    "    results=pd.DataFrame(model.cv_results_)\n",
    "    best_=results.loc[results[_dict[metric]].idxmax(),:]\n",
    "    \n",
    "    f1 = best_.mean_test_f1\n",
    "    recall = best_.mean_test_recall\n",
    "    precision = best_.mean_test_precision\n",
    "    accuracy = best_.mean_test_accuracy\n",
    "\n",
    "   \n",
    "    table = pd.DataFrame({'model': [name],\n",
    "                          'precision': [precision],\n",
    "                          'recall': [recall],\n",
    "                          'f1': [f1],\n",
    "                          'accuracy': [accuracy],\n",
    "                          },\n",
    "                         )\n",
    "    return table\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Train</td>\n",
       "      <td>0.833611</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.681704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Train</td>\n",
       "      <td>0.983876</td>\n",
       "      <td>0.949623</td>\n",
       "      <td>0.987331</td>\n",
       "      <td>0.914822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy        f1  precision    recall\n",
       "0   LogisticReg Train  0.831943  0.292569   0.488281  0.208855\n",
       "0      Gaussian Train  0.833611  0.576882   0.500000  0.681704\n",
       "0  RandomForest Train  0.983876  0.949623   0.987331  0.914822"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing Training Metrics For Random Forest Model\n",
    "result=pd.concat([result,getrr('RandomForest Train',model,'recall')],axis=0)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Train</td>\n",
       "      <td>0.833611</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.681704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Train</td>\n",
       "      <td>0.983876</td>\n",
       "      <td>0.949623</td>\n",
       "      <td>0.987331</td>\n",
       "      <td>0.914822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Val</td>\n",
       "      <td>0.838198</td>\n",
       "      <td>0.319298</td>\n",
       "      <td>0.514124</td>\n",
       "      <td>0.231552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Val</td>\n",
       "      <td>0.834862</td>\n",
       "      <td>0.579618</td>\n",
       "      <td>0.497268</td>\n",
       "      <td>0.694656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Val</td>\n",
       "      <td>0.987907</td>\n",
       "      <td>0.961892</td>\n",
       "      <td>0.994565</td>\n",
       "      <td>0.931298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy        f1  precision    recall\n",
       "0   LogisticReg Train  0.831943  0.292569   0.488281  0.208855\n",
       "0      Gaussian Train  0.833611  0.576882   0.500000  0.681704\n",
       "0  RandomForest Train  0.983876  0.949623   0.987331  0.914822\n",
       "0     LogisticReg Val  0.838198  0.319298   0.514124  0.231552\n",
       "0        Gaussian Val  0.834862  0.579618   0.497268  0.694656\n",
       "0    RandomForest Val  0.987907  0.961892   0.994565  0.931298"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing Validation Metrics For Logistic Regression Model, Gaussian Model, Random Forest Model\n",
    "\n",
    "result=pd.concat([result,getval('LogisticReg Val',logmodel,yval,logmodel.predict(xval))],axis=0)\n",
    "\n",
    "\n",
    "result=pd.concat([result,getval('Gaussian Val',gnc,yval,gnc.predict(xval))],axis=0)\n",
    "\n",
    "result=pd.concat([result,getval('RandomForest Val',rf,yval,model.predict(xval))],axis=0)\n",
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.9527481893247"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((0.961892-0.579618)/0.579618)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the table above for  all the three models are performing well on both training as well as validation set\n",
    "But RandomForest model is clearly the champion set here.   \n",
    "\n",
    "Training Set:  \n",
    "- Logistic Regression model with 0.292569 f1 score  \n",
    "- Gaussian Classifier model with 0.576882 f1 score (Increased f1 performance by 97.1% of Logistic Regression Model)  \n",
    "- RaForest Classifier model with 0.949623 f1 score (Increased f1 performance by 64.3% of Gaussian Classifier Model)  \n",
    "\n",
    "Validation Set  \n",
    "- Logistic Regression model with 0.319298 f1 score (Increased f1 performance by 8.3% of Training Logistic Regression Model)  \n",
    "- Gaussian Classifier model with 0.579618 f1 score (Increased f1 performance by 81.5% of Logistic Regression Model)  \n",
    "- RaForest Classifier model with 0.961892 f1 score (Increased f1 performance by 65.9 % of Logistic Regression Model)  \n",
    "  \n",
    "Therefore, choosing Random Forest Model is our best option to predict an employee leaving the company. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Train</td>\n",
       "      <td>0.833611</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.681704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Train</td>\n",
       "      <td>0.983876</td>\n",
       "      <td>0.949623</td>\n",
       "      <td>0.987331</td>\n",
       "      <td>0.914822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Val</td>\n",
       "      <td>0.838198</td>\n",
       "      <td>0.319298</td>\n",
       "      <td>0.514124</td>\n",
       "      <td>0.231552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Val</td>\n",
       "      <td>0.834862</td>\n",
       "      <td>0.579618</td>\n",
       "      <td>0.497268</td>\n",
       "      <td>0.694656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Val</td>\n",
       "      <td>0.987907</td>\n",
       "      <td>0.961892</td>\n",
       "      <td>0.994565</td>\n",
       "      <td>0.931298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Test</td>\n",
       "      <td>0.979575</td>\n",
       "      <td>0.936115</td>\n",
       "      <td>0.980874</td>\n",
       "      <td>0.895262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy        f1  precision    recall\n",
       "0   LogisticReg Train  0.831943  0.292569   0.488281  0.208855\n",
       "0      Gaussian Train  0.833611  0.576882   0.500000  0.681704\n",
       "0  RandomForest Train  0.983876  0.949623   0.987331  0.914822\n",
       "0     LogisticReg Val  0.838198  0.319298   0.514124  0.231552\n",
       "0        Gaussian Val  0.834862  0.579618   0.497268  0.694656\n",
       "0    RandomForest Val  0.987907  0.961892   0.994565  0.931298\n",
       "0   RandomForest Test  0.979575  0.936115   0.980874  0.895262"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Testing our champion model on the test data\n",
    "result=pd.concat([result,getval('RandomForest Test',rf,ytest,model.predict(xtest))],axis=0)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest model is predicting values with a 0.89 recall on the test data which is very significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "401PgchTPr4E"
   },
   "source": [
    "# pacE: Execute Stage\n",
    "- Interpret model performance and results\n",
    "- Share actionable steps with stakeholders\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ex8pgn5iNzau"
   },
   "source": [
    "✏\n",
    "## Recall evaluation metrics\n",
    "\n",
    "- **AUC** is the area under the ROC curve; it's also considered the probability that the model ranks a random positive example more highly than a random negative example.\n",
    "- **Precision** measures the proportion of data points predicted as True that are actually True, in other words, the proportion of positive predictions that are true positives.\n",
    "- **Recall** measures the proportion of data points that are predicted as True, out of all the data points that are actually True. In other words, it measures the proportion of positives that are correctly classified.\n",
    "- **Accuracy** measures the proportion of data points that are correctly classified.\n",
    "- **F1-score** is an aggregation of precision and recall.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OJ7Sn8--C45C"
   },
   "source": [
    "💭\n",
    "### Reflect on these questions as you complete the executing stage.\n",
    "\n",
    "- What key insights emerged from your model(s)?\n",
    "- What business recommendations do you propose based on the models built?\n",
    "- What potential recommendations would you make to your manager/company?\n",
    "- Do you think your model could be improved? Why or why not? How?\n",
    "- Given what you know about the data and the models you were using, what other questions could you address for the team?\n",
    "- What resources do you find yourself using as you complete this stage? (Make sure to include the links.)\n",
    "- Do you have any ethical considerations in this stage?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aF1C4CBhLXbN"
   },
   "source": [
    "- Firstly, it was revealed that most important feature in predicting the possibility of an employee leaving was `Satisfaction Level`. Secondly, Random Forest model outperformed both Logistic and GaussianNB model by significant metrics on all three training, validation and testing set. Finally, the precision-recall score of Random Forest model was at 0.9~-0.9 which means chances of misclassification are very low.  \n",
    "- As per the metrics, we can use this model to make predictions due to its high precision and recall or even for exploration. But, I do reccomend testing the model and see how it performs to unseen data as we know ensemble models are not the best at extrapolation and tend to overfit the data which doosn't seem to be the case as per our metrics on testing set as well as validation set.  \n",
    "- Model wise, use new datasets to verify the consistency of the model and as per visualizations alone we can infer that increasing number of projects may cause decrease in satisfaction levels and could be the reason of departure of employees.  \n",
    "- I think if we incorporate those `department` and `salary` columns which we dropped due to data formatting error could help the model improve, asking the correlation of `accidents` and `satisfaction level` could be explained by the team.  \n",
    "- StackOverflow, Course 6 Lab Exemplar, ChatGPT  \n",
    "- Links:  \n",
    "    - https://www.coursera.org/learn/the-nuts-and-bolts-of-machine-learning/ungradedLab/XPgRn/exemplar-course-6-waze-project-exemplar-lab/lab?path=%2Fnotebooks%2FExemplar_Course%25206%2520Waze%2520project%2520lab.ipynb  \n",
    "    - https://scikit-learn.org/1.5/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve  \n",
    "    - https://chatgpt.com/share/6715f525-6c38-8010-8844-6242f50d6097\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6pBUk35yTDaL"
   },
   "source": [
    "## Step 4. Results and Evaluation\n",
    "- Interpret model\n",
    "- Evaluate model performance using metrics\n",
    "- Prepare results, visualizations, and actionable steps to share with stakeholders\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Train</td>\n",
       "      <td>0.831943</td>\n",
       "      <td>0.292569</td>\n",
       "      <td>0.488281</td>\n",
       "      <td>0.208855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Train</td>\n",
       "      <td>0.833611</td>\n",
       "      <td>0.576882</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.681704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Train</td>\n",
       "      <td>0.983876</td>\n",
       "      <td>0.949623</td>\n",
       "      <td>0.987331</td>\n",
       "      <td>0.914822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticReg Val</td>\n",
       "      <td>0.838198</td>\n",
       "      <td>0.319298</td>\n",
       "      <td>0.514124</td>\n",
       "      <td>0.231552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaussian Val</td>\n",
       "      <td>0.834862</td>\n",
       "      <td>0.579618</td>\n",
       "      <td>0.497268</td>\n",
       "      <td>0.694656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Val</td>\n",
       "      <td>0.987907</td>\n",
       "      <td>0.961892</td>\n",
       "      <td>0.994565</td>\n",
       "      <td>0.931298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest Test</td>\n",
       "      <td>0.979575</td>\n",
       "      <td>0.936115</td>\n",
       "      <td>0.980874</td>\n",
       "      <td>0.895262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy        f1  precision    recall\n",
       "0   LogisticReg Train  0.831943  0.292569   0.488281  0.208855\n",
       "0      Gaussian Train  0.833611  0.576882   0.500000  0.681704\n",
       "0  RandomForest Train  0.983876  0.949623   0.987331  0.914822\n",
       "0     LogisticReg Val  0.838198  0.319298   0.514124  0.231552\n",
       "0        Gaussian Val  0.834862  0.579618   0.497268  0.694656\n",
       "0    RandomForest Val  0.987907  0.961892   0.994565  0.931298\n",
       "0   RandomForest Test  0.979575  0.936115   0.980874  0.895262"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see from the table above for  all the three models, each model is performing well on both training as well as validation set  \n",
    "But, RandomForest model is clearly the champion model here.   \n",
    "\n",
    "Training Set:  \n",
    "- Logistic Regression model with 0.292569 f1 score  \n",
    "- Gaussian Classifier model with 0.576882 f1 score (Increased f1 performance by 97.1% of Logistic Regression Model)  \n",
    "- RaForest Classifier model with 0.949623 f1 score (Increased f1 performance by 64.3% of Gaussian Classifier Model)  \n",
    "\n",
    "Validation Set:  \n",
    "- Logistic Regression model with 0.319298 f1 score (Increased f1 performance by 8.3% of Training Logistic Regression Model)  \n",
    "- Gaussian Classifier model with 0.579618 f1 score (Increased f1 performance by 81.5% of Logistic Regression Model)  \n",
    "- RaForest Classifier model with 0.961892 f1 score (Increased f1 performance by 65.9 % of Gaussian Classifier Model)  \n",
    "  \n",
    "Therefore, choosing Random Forest Model is our best option to predict an employee leaving the company. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7af4966c4e90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "cm=confusion_matrix(ytest,model.predict(xtest))\n",
    "dp=ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=model.classes_)\n",
    "dp.plot(values_format='.0f') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As you can see from the above confusion matrix, the model has very few `false positives` and `false negatives` which means\n",
    "the model is less prone to predict an employee as `Will leave` when they are not going to, or predict an employee as `Will not leave` but in actuality the employee may.\n",
    "\n",
    "- `Recall` value explains this very behaviour of the model, if the model predicts someone to leave than HR may reduce their loadwork or reduce their participation in number of projects in order to retain them. Therefore the risk for falsely predicting is not significantly high, unless the company will dedicate a large amount of resources in order to ease their dissatisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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": [
    "# Plot precision-recall curve\n",
    "from sklearn.metrics import PrecisionRecallDisplay,precision_recall_curve,average_precision_score\n",
    "avgscore=average_precision_score(ytest, model.predict(xtest))\n",
    "precision, recall, _ = precision_recall_curve(ytest, model.predict(xtest))\n",
    "display = PrecisionRecallDisplay(average_precision=avgscore,precision=precision,recall=recall,estimator_name='RFModel')\n",
    "display.plot()\n",
    "plt.title('Precision-recall curve, RFModel');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- From the above plot we can confirm our precision-recall at 0.9~ - 0.9~ which means that model will predict 90% of the time a user is going to leave with precision of 90% as well which is impressive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WEdlBRR0IWno+aOJKPVvhXt/CQytSRrB0K2ffvt1zXJGnXqmrqUVE2ltRBYJ3Pqjjy3e+yJqNdQU9H7RH19KUQvuAPXukPXkoS7t5tzJ6lGsmRRHpPIoqELy2ZhMLV6xnwkH9Gdave1iQN0/M1V7PBxUR2ZUUVSBINM1fcUI14/bdo0PzIiKyqyiqS96mcO4BDZAREWlWVIEgUSXookggIpJUVIEgWSPo4HyIiOxKiioQuGoEIiIZiioQqI9ARCRTUQWCxKghBQIRkWbFFQiSfQSKBCIiCUUWCIKfXYrqrEVE8iuqIjEx2ZtqBCIizYosEASRQNMAiYg0K6pA0NxZrEggIpJQXIFAw0dFRDIUWSAIfuqGMhGRZkUVCDTFhIhIplgDgZmdbGavmVmNmU3Nsr2XmT1kZi+b2Stm9oU486MagYhIptgCgZmVANOBU4CRwBQzG5mW7DLgVXcfA0wAfm5m5XHlSVNMiIhkirNGcARQ4+5vuns9MAs4Iy2NA1UWDOPpAbwP5H9+5E7QFBMiIpniDASDgFWR5dpwXdQtwAjgbWAJ8BV3z3gqvJldbGYLzGzB2rVrdzhDzaOGFAlERBLiDATZSltPWz4JWATsDYwFbjGznhk7uc9w9/HuPr5///47nKHmPoIdPoSIyG4nzkBQC+wTWR5McOUf9QXgfg/UAG8Bw+PKkKaYEBHJFGcgeAGoNrNhYQfwOcDstDQrgRMBzGwAcBDwZlwZ0hQTIiKZSuM6sLs3mtnlwCNACXC7u79iZpeE228DfgjMNLMlBE1JV7v7e7HlKfypPgIRkWaxBQIAd58DzElbd1vk/dvAJ+LMQ9pnAxo1JCISVVR3FuuGMhGRTEUVCDTFhIhIpqIKBKoRiIhkKqpAkKgRqEogItKsqAJBgoaPiog0K6pA0KQpJkREMhRVINAUEyIimYoqEGiKCRGRTEUVCBzdUCYikq64AkGiRqBAICKSVHAgMLPucWakPTQ1JSadUyQQEUloMRCY2UfM7FVgWbg8xsx+FXvOYpCYdE6BQESkWSE1ghsJHiCzDsDdXwY+Fmem4qIpJkREMhXUNOTuq9JWbY8hL7FTH4GISKZCpqFeZWYfATx8wMyVhM1EnY2eWSwikqmQGsElwGUED56vJXi28JfjzFRcHN1MJiKSrpAawUHufl50hZl9FHg6nizFp8ldtQERkTSF1AhuLnDdLs9dNQIRkXQ5awRmdjTwEaC/mf1nZFNPgmcQdzpNruklRETS5WsaKgd6hGmqIus/AD4dZ6bi4rhGDImIpMkZCNz978DfzWymu69oxzzFxl1DR0VE0hXSWbzVzH4GHAxUJFa6+wmx5SomTU2uu4pFRNIU0ll8J/BPYBjwX8By4IUY8xSbYPioAoGISFQhgaCvu/8WaHD3v7v7F4GjYs5XLJrc1VUsIpKmkKahhvDnajM7FXgbGBxfluKjPgIRkUyFBIIfmVkv4GsE9w/0BK6KNVcxcd1QJiKSocVA4O4Ph283AsdD8s7iTkdTTIiIZMp3Q1kJcBbBHENz3X2pmZ0GfBuoBA5tnyy2HU0xISKSKV+N4LfAPsDzwDQzWwEcDUx19z+3R+bamqaYEBHJlC8QjAdGu3uTmVUA7wEHuPua9sla2wueVKlIICISlW/4aL27NwG4ex3wemuDgJmdbGavmVmNmU3NkWaCmS0ys1fM7O+tOX7ruWoEIiJp8tUIhpvZ4vC9AfuHywa4u4/Od+Cwj2E6MJHgOQYvmNlsd381kqY38CvgZHdfaWZ77sS5tKipScNHRUTS5QsEI3by2EcANe7+JoCZzQLOAF6NpDkXuN/dVwK4+7s7+Zl5OZpiQkQkXb5J53Z2orlBQPRZx7XAkWlpDgTKzGwewQynN7n779MPZGYXAxcDDBkyZIczFExDLSIiUQU9vH4HZStzPW25FBgHnAqcBFxjZgdm7OQ+w93Hu/v4/v3773CGNHxURCRTIXcW76haguGnCYMJpqdIT/Oeu28BtpjZfGAM8HosOXLoEmfoExHphAoqFs2s0swOauWxXwCqzWyYmZUD5wCz09I8CBxrZqVm1o2g6WhZKz+nYMGkc6oRiIhEtRgIzGwysAiYGy6PNbP0Aj2DuzcClwOPEBTuf3T3V8zsEjO7JEyzLDzuYoIb137j7kt39GRazBO6oUxEJF0hTUPXEowAmgfg7ovMbGghB3f3OcCctHW3pS3/DPhZIcfbWU2O+ghERNIU0jTU6O4bY89JOwhmH+3oXIiI7FoKqREsNbNzgRIzqwauBJ6JN1vxcA0fFRHJUEiN4AqC5xV/CNxFMB1153wegW4oExHJUEiN4CB3/w7wnbgzEzdNMSEikqmQGsEvzOyfZvZDMzs49hzFSDUCEZFMLQYCdz8emACsBWaY2RIz+27cGYtDU/p9zSIiUtgNZe6+xt2nAZcQ3FPwvVhzFRN31QhERNIVckPZCDO71syWArcQjBgaHHvOYuCaYkJEJEMhncW/A+4GPuHu6XMFdSqaYkJEJFOLgcDdj2qPjLQHTTEhIpIpZyAwsz+6+1lmtoTU6aMLekLZrqjJ0fhREZE0+WoEXwl/ntYeGWkPQWdxR+dCRGTXkrPr1N1Xh2+/7O4roi/gy+2TvbalKSZERDIVMoZmYpZ1p7R1RtqDbigTEcmUr4/gUoIr//3MbHFkUxXwdNwZi4OmmBARyZSvj+Au4K/AT4CpkfWb3P39WHMVE0fPLBYRSZcvELi7Lzezy9I3mFmfzhgMmtRHICKSoaUawWnAQoLho9Ey1IH9YsxXPBy6aNiQiEiKnIHA3U8Lfw5rv+zEq8mdUjUNiYikKGSuoY+aWffw/WfN7BdmNiT+rLW9Jk06JyKSoZDho7cCW81sDPBNYAXwh1hzFRPdWCwikqnQh9c7cAZwk7vfRDCEtNNpcjRqSEQkTSGzj24ys28B5wPHmlkJUBZvtmKiKSZERDIUUiM4m+DB9V909zXAIOBnseYqJho+KiKSqZBHVa4B7gR6mdlpQJ27/z72nMVAU0yIiGQqZNTQWcDzwGeAs4DnzOzTcWcsDppiQkQkUyF9BN8BDnf3dwHMrD/wN+DeODMWh2DUkCKBiEhUIX0EXRJBILSuwP12Oe6uPgIRkTSF1AjmmtkjBM8thqDzeE58WYqPO+ojEBFJU8gzi79hZv8POIZg0M0Md38g9pzFoMldfQQiImnyPY+gGrgB2B9YAnzd3f/dXhmLg6aYEBHJlK+t/3bgYeBMghlIb27twc3sZDN7zcxqzGxqnnSHm9n2uEcjaYoJEZFM+ZqGqtz91+H718zsxdYcOLwDeTrBoy5rgRfMbLa7v5ol3U+BR1pz/B3hmmJCRCRDvkBQYWaH0nwzbmV02d1bCgxHADXu/iaAmc0imK/o1bR0VwD3AYe3Mu+t5ppiQkQkQ75AsBr4RWR5TWTZgRNaOPYgYFVkuRY4MprAzAYBnwqPlTMQmNnFwMUAQ4bs+AzYmmJCRCRTvgfTHL+Tx85W5nra8i+Bq919e74mG3efAcwAGD9+fPoxCqYpJkREMhVyH8GOqgX2iSwPBt5OSzMemBUGgX7AJDNrdPc/x5GhpiZUJRARSRNnIHgBqDazYcC/gXOAc6MJoo/BNLOZwMNxBYEE1QhERFLFFgjcvdHMLicYDVQC3O7ur5jZJeH22+L67FyaNMWEiEiGFgOBBe025wH7ufsPwucV7+Xuz7e0r7vPIW06ilwBwN0vKCjHO0FTTIiIZCpk8rhfAUcDU8LlTQT3B3Q6mmJCRCRTIU1DR7r7YWb2EoC7rzez8pjzFQtNQy0ikqmQGkFDePevQ/J5BE2x5iomuqFMRCRTIYFgGvAAsKeZ/Rh4Crgu1lzFpMk115CISLpCpqG+08wWAicSjML/pLsviz1nMXDNPioikqGQUUNDgK3AQ9F17r4yzozFQVNMiIhkKqSz+C+E/axABTAMeA04OMZ8xcLd1VksIpKmkKahQ6LLZnYY8B+x5ShGrj4CEZEMrX4IfTj9dOxTRsfB0Q1lIiLpCukj+M/IYhfgMGBtbDmKkaaYEBHJVEgfQVXkfSNBn8F98WQnXu7QRTcSiIikyBsIwhvJerj7N9opP7FSjUBEJFPOPgIzK3X37QRNQbsFTTEhIpIpX43geYIgsMjMZgN/ArYkNrr7/THnrc25Jp0TEclQSB9BH2AdwXOFE/cTONDpAkGTo7mGRETS5AsEe4YjhpbSHAASdvi5wR1JU0yIiGTKFwhKgB4U9hD6TkFTTIiIZMoXCFa7+w/aLScxcw9ilzqLRURS5buzeLcqMcM4oM5iEZE0+QLBie2Wi3aQaMtSH4GISKqcgcDd32/PjMStKdE01MH5EBHZ1bR60rnOKtE0pCkmRERSFU0gSNQIREQkVdEEggT1EYiIpCqaQJDsI1AcEBFJUTSBINlHoEAgIpKiaAJBokagpiERkVRFFAg6OgciIrumogkEJJuGVCMQEYkqmkCgzmIRkexiDQRmdrKZvWZmNWY2Ncv288xscfh6xszGxJUXTTEhIpJdbIEgfN7xdOAUYCQwxcxGpiV7CzjO3UcDPwRmxJUf1QhERLKLs0ZwBFDj7m+6ez0wCzgjmsDdn3H39eHis8DguDLTPPuoIoGISFScgWAQsCqyXBuuy+VLwF+zbTCzi81sgZktWLt27Q5lxjXpnIhIVnEGgoKfbGZmxxMEgquzbXf3Ge4+3t3H9+/ff4cyoz4CEZHsCnl4/Y6qBfaJLA8G3k5PZGajgd8Ap7j7urgyoz4CEZHs4qwRvABUm9kwMysHzgFmRxOY2RDgfuB8d389xrxoigkRkRxiqxG4e6OZXQ48ApQAt7v7K2Z2Sbj9NuB7QF/gV2EnbqO7j48jP80PplEkEBGJirNpCHefA8xJW3db5P2FwIVx5qH5s4KfahoSEUlVNHcWu6aYEBHJqmgCgTqLRUSyK5pAoOGjIiLZFU0gUI1ARCS7ogkEmmJCRCS7IgoEmmJCRCSb4gkE4U/1EYiIpCqaQKA+AhGR7IomEGiKCRGR7IomECRqBOolEBFJVTSBQDUCEZHsijAQKBKIiEQVTSBQZ7GISHZFEwg0fFREJLuiCQTJzmLFARGRFEUTCNRHICKSXREFAk0xISKSTfEEgvCnagQiIqmKJhA0NWnUkIhINkUTCJL3FSsQiIikKJpAkLyPQL0EIiIpSjs6A+1GU0zILqyhoYHa2lrq6uo6OivSyVVUVDB48GDKysoK3qdoAkGTnlAmu7Da2lqqqqoYOnSovqOyw9yddevWUVtby7Bhwwrer+iahlQjkF1RXV0dffv2VRCQnWJm9O3bt9U1y6IJBM2dxfpHk12TvpvSFnbke1Q0gUCTzomIZFc0gQBNMSGSV0lJCWPHjmXUqFFMnjyZDRs2ALB8+XIqKysZO3Zs8lVfX8/MmTMxMx5//PHkMR544AHMjHvvvbfgz503bx6nnXbaDqd56aWXuPDCC1PWnXHGGRx99NEp6y644IKMfPXo0SP5/vXXX2fSpEkccMABjBgxgrPOOot33nmn4PPI5v3332fixIlUV1czceJE1q9fnzXdTTfdxKhRozj44IP55S9/mVx/7bXXMmjQoOTvfc6cOQAsWbKECy64YKfyFlU0gaBJU0yI5FVZWcmiRYtYunQpffr0Yfr06clt+++/P4sWLUq+ysvLATjkkEO4++67k+lmzZrFmDFj2jXf1113HVdccUVyecOGDbz44ots2LCBt956q6Bj1NXVceqpp3LppZdSU1PDsmXLuPTSS1m7du1O5e3666/nxBNP5F//+hcnnngi119/fUaapUuX8utf/5rnn3+el19+mYcffph//etfye1f/epXk7/3SZMmAcHvvba2lpUrV+5U/hKKZtSQJp2TzuK/HnqFV9/+oE2POXLvnnx/8sEFpz/66KNZvHhxi+mOPfZYnnzySRoaGvjwww+pqalh7Nixye2PP/44X//612lsbOTwww/n1ltvpWvXrsydO5errrqKfv36cdhhhyXTb9myhSuuuIIlS5bQ2NjItddeyxlnnJHz8zdt2sTixYtTgs99993H5MmTGTBgALNmzeJb3/pWi+dx1113cfTRRzN58uTkuuOPP+UOUu8AAA4GSURBVL7F/Vry4IMPMm/ePAA+//nPM2HCBH7605+mpFm2bBlHHXUU3bp1A+C4447jgQce4Jvf/GbeY0+ePJlZs2a1mK4QxVcjUBwQyWv79u08/vjjnH766cl1b7zxRrJ54rLLLkuuNzM+/vGP88gjj/Dggw+m7FNXV8cFF1zAPffckyzYb731Vurq6rjooot46KGHePLJJ1mzZk1ynx//+MeccMIJvPDCCzzxxBN84xvfYMuWLTnzumDBAkaNGpWy7u6772bKlClMmTIlpbaSz9KlSxk3blyL6TZt2pTSRBZ9vfrqqxnp33nnHQYOHAjAwIEDeffddzPSjBo1ivnz57Nu3Tq2bt3KnDlzWLVqVXL7LbfcwujRo/niF7+Y0rQ0fvx4nnzyyYLOryXFUyMIfyoQyK6uNVfubWnbtm2MHTuW5cuXM27cOCZOnJjclmgayuacc85h2rRpbNy4kZ///Odcd911ALz22msMGzaMAw88EAiuiKdPn86ECRMYNmwY1dXVAHz2s59lxowZADz66KPMnj2bG264AQiCSb7mj9WrV9O/f//k8jvvvENNTQ3HHHMMZkZpaSlLly5l1KhRWUfTtHaETVVVVc7fw44aMWIEV199NRMnTqRHjx6MGTOG0tKgaL700ku55pprMDOuueYavva1r3H77bcDsOeee/L222+3SR5irRGY2clm9pqZ1ZjZ1CzbzcymhdsXm9lh2Y7TFlxTTIjklegjWLFiBfX19Sl9BPkcccQRLF26lPfeey9Z6EPz/1w2uQpgd+e+++5LtomvXLmSESNG5M1zdMz8Pffcw/r16xk2bBhDhw5l+fLlzJo1C4C+ffumXFG///779OvXD4CDDz6YhQsXtniura0RDBgwgNWrVwNB0Npzzz2zHvdLX/oSL774IvPnz6dPnz7JIDlgwABKSkro0qULF110Ec8//3xyn7q6OiorK1vMcyFiCwRmVgJMB04BRgJTzGxkWrJTgOrwdTFwa1z5SfYRFE1jmMiO6dWrF9OmTeOGG26goaGhoH1+8pOfJGsCCcOHD2f58uXU1NQA8Ic//IHjjjuO4cOH89Zbb/HGG28ApDTfnHTSSdx8883JIPLSSy/l/dwRI0Ykj5841ty5c1m+fDnLly9n4cKFyUAwYcIE7rnnHurr6wGYOXNmsh/g3HPP5ZlnnuEvf/lL8lhz585lyZIlKZ+XqBFke40cmV68wemnn84dd9wBwB133JGzvyPRZLRy5Uruv/9+pkyZApAMIhCMyIo2g73++usZzWI7Ks5i8Qigxt3fdPd6YBaQ/ls4A/i9B54FepvZwDgyk5xiQjUCkRYdeuihjBkzJlmItuSUU07J6FytqKjgd7/7HZ/5zGc45JBD6NKlC5dccgkVFRXMmDGDU089lWOOOYZ99903uc8111xDQ0MDo0ePZtSoUVxzzTV5P3f48OFs3LiRTZs2sXz5clauXMlRRx2V3D5s2DB69uzJc889x2mnncaxxx7LuHHjGDt2LE8//XSy47ayspKHH36Ym2++merqakaOHMnMmTNzXsEXaurUqTz22GNUV1fz2GOPMXVq0DDy9ttvJ0cAAZx55pmMHDmSyZMnM336dPbYYw8AvvnNb3LIIYcwevRonnjiCW688cbkPk888QSnnnrqTuUvwfJV33bqwGafBk529wvD5fOBI9398kiah4Hr3f2pcPlx4Gp3X5B2rIsJagwMGTJk3IoVK1qdn4Ur1nP7U2/xnVNHsHfvtqlOibSVZcuW5W0CkdxuvPFGqqqqMu4l2J19+OGHHHfccTz11FPJ/oSobN8nM1vo7uOzHS/OGkG2S+/0qFNIGtx9hruPd/fx0Y6h1hi37x5MP+8wBQGR3cyll15K165dOzob7WrlypVcf/31WYPAjohz1FAtsE9keTCQ3sVdSBoRkZwqKio4//zzOzob7aq6ujrZodwW4qwRvABUm9kwMysHzgFmp6WZDXwuHD10FLDR3VenH0ikGMTVTCvFZUe+R7HVCNy90cwuBx4BSoDb3f0VM7sk3H4bMAeYBNQAW4EvxJUfkV1ZRUUF69at01TUslMSzyOoqKho1X6xdRbHZfz48b5gwYKWE4p0InpCmbSVXE8oy9dZXDR3FovsysrKylr1RCmRtqTbq0REipwCgYhIkVMgEBEpcp2us9jM1gKtv7U40A94rw2z0xnonIuDzrk47Mw57+vuWe/I7XSBYGeY2YJcvea7K51zcdA5F4e4zllNQyIiRU6BQESkyBVbIJjR0RnoADrn4qBzLg6xnHNR9RGIiEimYqsRiIhIGgUCEZEit1sGAjM72cxeM7MaM5uaZbuZ2bRw+2IzO6wj8tmWCjjn88JzXWxmz5jZmI7IZ1tq6Zwj6Q43s+3hU/M6tULO2cwmmNkiM3vFzP7e3nlsawV8t3uZ2UNm9nJ4zp16FmMzu93M3jWzpTm2t3355e671Ytgyus3gP2AcuBlYGRamknAXwmekHYU8FxH57sdzvkjwB7h+1OK4Zwj6f6PYMrzT3d0vtvh79wbeBUYEi7v2dH5bodz/jbw0/B9f+B9oLyj874T5/wx4DBgaY7tbV5+7Y41giOAGnd/093rgVnAGWlpzgB+74Fngd5mNrC9M9qGWjxnd3/G3deHi88SPA2uMyvk7wxwBXAf8G57Zi4mhZzzucD97r4SwN07+3kXcs4OVFnwIIceBIGgsX2z2XbcfT7BOeTS5uXX7hgIBgGrIsu14brWpulMWns+XyK4oujMWjxnMxsEfAq4rR3zFadC/s4HAnuY2TwzW2hmn2u33MWjkHO+BRhB8JjbJcBX3L2pfbLXIdq8/Nodn0eQ7fFO6WNkC0nTmRR8PmZ2PEEgOCbWHMWvkHP+JXC1u2/fTZ76Vcg5lwLjgBOBSuAfZvasu78ed+ZiUsg5nwQsAk4A9gceM7Mn3f2DuDPXQdq8/NodA0EtsE9keTDBlUJr03QmBZ2PmY0GfgOc4u7r2ilvcSnknMcDs8Ig0A+YZGaN7v7n9slimyv0u/2eu28BtpjZfGAM0FkDQSHn/AXgeg8a0GvM7C1gOPB8+2Sx3bV5+bU7Ng29AFSb2TAzKwfOAWanpZkNfC7sfT8K2Ojuq9s7o22oxXM2syHA/cD5nfjqMKrFc3b3Ye4+1N2HAvcCX+7EQQAK+24/CBxrZqVm1g04EljWzvlsS4Wc80qCGhBmNgA4CHizXXPZvtq8/NrtagTu3mhmlwOPEIw4uN3dXzGzS8LttxGMIJkE1ABbCa4oOq0Cz/l7QF/gV+EVcqN34pkbCzzn3Uoh5+zuy8xsLrAYaAJ+4+5ZhyF2BgX+nX8IzDSzJQTNJle7e6edntrM7gYmAP3MrBb4PlAG8ZVfmmJCRKTI7Y5NQyIi0goKBCIiRU6BQESkyCkQiIgUOQUCEZEip0Agu6RwttBFkdfQPGk3t8HnzTSzt8LPetHMjt6BY/zGzEaG77+dtu2Znc1jeJzE72VpOONm7xbSjzWzSW3x2bL70vBR2SWZ2WZ379HWafMcYybwsLvfa2afAG5w99E7cbydzlNLxzWzO4DX3f3HedJfAIx398vbOi+y+1CNQDoFM+thZo+HV+tLzCxjplEzG2hm8yNXzMeG6z9hZv8I9/2TmbVUQM8HDgj3/c/wWEvN7KpwXXcz+0s4//1SMzs7XD/PzMab2fVAZZiPO8Ntm8Of90Sv0MOayJlmVmJmPzOzFyyYY/4/Cvi1/INwsjEzO8KC50y8FP48KLwT9wfA2WFezg7zfnv4OS9l+z1KEeroubf10ivbC9hOMJHYIuABgrvge4bb+hHcVZmo0W4Of34N+E74vgSoCtPOB7qH668Gvpfl82YSPq8A+AzwHMHkbUuA7gTTG78CHAqcCfw6sm+v8Oc8gqvvZJ4iaRJ5/BRwR/i+nGAWyUrgYuC74fquwAJgWJZ8bo6c35+Ak8PlnkBp+P7jwH3h+wuAWyL7Xwd8Nnzfm2AOou4d/ffWq2Nfu90UE7Lb2ObuYxMLZlYGXGdmHyOYOmEQMABYE9nnBeD2MO2f3X2RmR0HjASeDqfWKCe4ks7mZ2b2XWAtwQytJwIPeDCBG2Z2P3AsMBe4wcx+StCc9GQrzuuvwDQz6wqcDMx3921hc9Roa36KWi+gGngrbf9KM1sEDAUWAo9F0t9hZtUEM1GW5fj8TwCnm9nXw+UKYAidez4i2UkKBNJZnEfw9Klx7t5gZssJCrEkd58fBopTgT+Y2c+A9cBj7j6lgM/4hrvfm1gws49nS+Tur5vZOIL5Xn5iZo+6+w8KOQl3rzOzeQRTJ58N3J34OOAKd3+khUNsc/exZtYLeBi4DJhGMN/OE+7+qbBjfV6O/Q04091fKyS/UhzURyCdRS/g3TAIHA/sm57AzPYN0/wa+C3B4/6eBT5qZok2/25mdmCBnzkf+GS4T3eCZp0nzWxvYKu7/y9wQ/g56RrCmkk2swgmCjuWYDI1wp+XJvYxswPDz8zK3TcCVwJfD/fpBfw73HxBJOkmgiayhEeAKyysHpnZobk+Q4qHAoF0FncC481sAUHt4J9Z0kwAFpnZSwTt+De5+1qCgvFuM1tMEBiGF/KB7v4iQd/B8wR9Br9x95eAQ4Dnwyaa7wA/yrL7DGBxorM4zaMEz6X9mwePX4TgORGvAi9a8NDy/6GFGnuYl5cJpmb+b4LaydME/QcJTwAjE53FBDWHsjBvS8NlKXIaPioiUuRUIxARKXIKBCIiRU6BQESkyCkQiIgUOQUCEZEip0AgIlLkFAhERIrc/wfltKOIyiF9FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay,roc_curve,auc\n",
    "\n",
    "fpr, tpr, _ = roc_curve(ytest, model.predict(xtest))\n",
    "\n",
    "# Compute AUC (Area Under the Curve)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Create and plot the ROC curve\n",
    "display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='RFModel')\n",
    "display.plot()\n",
    "\n",
    "# Customize the plot\n",
    "plt.title(f'ROC curve (AUC = {roc_auc:.2f}), RFModel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Above plot represents the chance of model predicting a random positive higher than a random negative model which is 95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "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": [
    "feat_importances = pd.Series(model.best_estimator_.feature_importances_, index=x.columns)\n",
    "feat_importances.nlargest(5).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above plot represents the top 5 important features which influenced Y Dependent variable  \n",
    "- `Satisfaction Level` at 0.35 as the most important.\n",
    "- `Number Of Projects` at 0.20 as the second most important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GXrsxT498Z7h"
   },
   "source": [
    "### Summary of model results\n",
    "We have trained 3 models:  \n",
    "- Logistic Regression Model\n",
    "- GaussianNB Classifier Model\n",
    "- Random Forest Classifier Model  \n",
    "\n",
    "`Random Forest Classifier Model` managed to outperformed all the other models by a huge margin, outputting high accuracy, precision, f1 score by ~240% of `Logistic Regression Model`. \n",
    "\n",
    "Metrics for Random Forest Regression Model:  \n",
    "- Accuracy:0.97 \n",
    "- Precision:0.98  \n",
    "- F1:0.93  \n",
    "- Recall:0.89  \n",
    "- ROC-AUC:0.95  \n",
    "\n",
    "Important Features:\n",
    "- `Satisfaction Level`: 0.35\n",
    "- `Number of projects`:  0.19\n",
    "- `Years in company`:0.18  \n",
    "- `Average monthly hours`:0.16  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9MOMqelNLn2v"
   },
   "source": [
    "### Conclusion, Recommendations, Next Steps\n",
    "\n",
    "- Model performed signficantly well on all 3 data sets\n",
    "- Verify model metrics on unseen data set\n",
    "- Use the model for exploration, as well as business decisions after verification  \n",
    "- Re-explore the data to derive more insights from known correlations in the model\n",
    "- Contact the team to know about unicode errors in `department` and `salary` columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations!** You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged."
   ]
  }
 ],
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