EmployeeTurnoverAnalytics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c8b7746-5dd1-4098-afa4-5d76fd74e2cf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE \n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "from sklearn.model_selection import cross_val_predict, cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54ddfb60-94e0-4f20-9e75-1d4e0ac95a68",
   "metadata": {
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   "outputs": [
    {
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       "<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>sales</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",
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       "      <th>1</th>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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  sales  \\\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": [
    "df = pd.read_excel('1688640705_hr_comma_sep.xlsx')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "130e0e84-79d2-4296-986c-01ca72216ea6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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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_monthly_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>sales</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",
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       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
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       "      <td>272</td>\n",
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       "      <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",
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       "      <td>low</td>\n",
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       "    <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",
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       "      <td>low</td>\n",
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      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_monthly_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  sales  \\\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": 3,
     "metadata": {},
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   ],
   "source": [
    "# correct typo\n",
    "\n",
    "df.rename({'average_montly_hours':'average_monthly_hours'}, axis=1, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a98f5a8-cc4a-4033-8aaa-3fbfdc03a2db",
   "metadata": {},
   "source": [
    "## 1. Perform data quality checks by checking for missing values if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c3a94d5-2377-4cee-a871-97aff8cdbbd3",
   "metadata": {
    "tags": []
   },
   "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_monthly_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   sales                  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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bbfa08a1-7d5a-4519-91e4-0d8409cef1c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_monthly_hours    0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "sales                    0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd264c7-466c-4022-80ec-88719b262bdb",
   "metadata": {},
   "source": [
    "No missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e281a5ee-1275-4cde-b65d-ff06d22d46c7",
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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_monthly_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",
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      ],
      "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_monthly_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": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9d5ae4-bb6c-4e3d-b807-b5cdaaa7a51c",
   "metadata": {},
   "source": [
    "## 2. Understand what factors contributed most to employee turnover by EDA.\n",
    "\n",
    "### 2.1 Draw a heatmap of the Correlation Matrix between all numerical features/columns in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71e47bf1-27a0-4a4f-bf59-41b79266c871",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,8))\n",
    "sns.heatmap(df.corr(numeric_only = True), cmap = 'coolwarm', annot=True)\n",
    "plt.show()\n",
    "\n",
    "# ... had to downgrade Matplotlib from 3.8 to 3.7. With 3.8, only the frist row gets annotated somehow.\n",
    "# pip install Matplotlib=3.7.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5689e54-47b5-4cd0-8fe8-9bc590c6b68f",
   "metadata": {},
   "source": [
    "### 2.2 Draw the distribution plot of \n",
    "* Employee Satisfaction (use column satisfaction_level)\n",
    "* Employee Evaluation (use column last_evaluation)\n",
    "* Employee Average Monthly Hours (use column average_montly_hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d8e317d-3900-4d32-b84e-ba49f97f3be5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displot_cols = ['satisfaction_level', 'last_evaluation', 'average_monthly_hours']\n",
    "\n",
    "for col in displot_cols:\n",
    "    sns.displot(df, x=col, kde=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa2b11c-c1f8-4c64-b28a-79151765fa3e",
   "metadata": {},
   "source": [
    "## 2.3 Draw the bar plot of the Employee Project Count of both employees who left and stayed in the organization (use column number_project and hue column left), and give your inferences from the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aaf91d41-4155-41cd-8eb2-54cfefad0c6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k5GQVFhZKkgoLC9WxY0c7hiQpLS1NXl5e2rRpk72mX79+dgxJUnp6uoqLi3Xo0KHWvkwAANDOORpEf//73/Xyyy+re/fu+vDDDzV+/Hg99NBDWrJkiSSptLRUkhQVFeXxuKioKHtfaWmpIiMjPfb7+PgoPDzcY83pjnHqOU5VXV0tt9vtcQMAAOcvHydPXl9fr6SkJD377LOSpCuvvFI7duxQbm6uRo0a5dhcOTk5mjFjhmPnBwAAbcvRZ4hiYmLUq1cvj209e/bUnj17JEnR0dGSpLKyMo81ZWVl9r7o6GiVl5d77D9x4oQOHjzoseZ0xzj1HKeaNm2aKisr7dvevXubeokAAOAc4GgQXX/99SouLvbY9vXXX+uiiy6SJCUkJCg6Olr5+fn2frfbrU2bNiklJUWSlJKSooqKChUVFdlrPvroI9XX1ys5Odle8/HHH6u2ttZes2bNGvXo0cPjE20n+fv7KzQ01OMGAADOX44G0cSJE7Vx40Y9++yz+vbbb7Vs2TK9+uqrysjIkCS5XC5NmDBBTz/9tN577z1t375dI0eOVGxsrIYMGSLpp2eUBg0apHHjxmnz5s1av369MjMzNWzYMMXGxkqS7rnnHvn5+Wns2LHauXOn3nrrLb344ovKyspy6tIBAEA74uh7iK6++motX75c06ZN05NPPqmEhATNmzdPw4cPt9dMmTJFVVVVuu+++1RRUaEbbrhBq1atUkBAgL1m6dKlyszM1M033ywvLy8NHTpU8+fPt/eHhYVp9erVysjIUGJiojp37qzs7GyP7yoCAADmcvR7iM4VfA+RefgeIgA4950z30MEAADQHhBEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwXpOCaMCAAaqoqGiw3e12a8CAAc2dCQAAoE01KYgKCgpUU1PTYPvx48f1ySefNHsoAACAtuRzNou//PJL+7937dql0tJS+35dXZ1WrVqlCy+8sOWmAwAAaANnFUR9+/aVy+WSy+U67UtjgYGBeumll1psOAAAgLZwVkFUUlIiy7J08cUXa/PmzYqIiLD3+fn5KTIyUt7e3i0+JAAAQGs6qyC66KKLJEn19fWtMgwAAIATziqITvXNN99o7dq1Ki8vbxBI2dnZzR4MAACgrTQpiF577TWNHz9enTt3VnR0tFwul73P5XIRRAAA4JzSpCB6+umn9cwzz2jq1KktPQ8AAECba9L3EB06dEh33nlnS88CAADgiCYF0Z133qnVq1e39CwAAACOaNJLZt26ddP06dO1ceNG9e7dW76+vh77H3rooRYZDgAAoC00KYheffVVdejQQevWrdO6des89rlcLoIIAACcU5oURCUlJS09BwAAgGOa9B4iAACA80mTniEaM2bMz+5ftGhRk4YBAABwQpOC6NChQx73a2trtWPHDlVUVJz2j74CAAC0Z00KouXLlzfYVl9fr/Hjx+uSSy5p9lAAAABtqcXeQ+Tl5aWsrCzNnTu3pQ4JAADQJlr0TdXfffedTpw40ZKHBAAAaHVNesksKyvL475lWdq/f7/y8vI0atSoFhkMAACgrTQpiD7//HOP+15eXoqIiNCcOXPO+Ak0AACA9qZJQbR27dqWngMAAMAxTQqikw4cOKDi4mJJUo8ePRQREdEiQwEAALSlJr2puqqqSmPGjFFMTIz69eunfv36KTY2VmPHjtXRo0dbekYAAIBW1aQgysrK0rp16/T++++roqJCFRUVevfdd7Vu3TpNmjSppWcEAABoVU16yeydd97RX//6V/Xv39/eduuttyowMFD/+Z//qZdffrml5gMAAGh1TXqG6OjRo4qKimqwPTIykpfMAADAOadJQZSSkqLHH39cx48ft7cdO3ZMM2bMUEpKSosNBwAA0Baa9JLZvHnzNGjQIHXp0kV9+vSRJH3xxRfy9/fX6tWrW3RAAACA1takIOrdu7e++eYbLV26VF999ZUk6e6779bw4cMVGBjYogMCAAC0tiYFUU5OjqKiojRu3DiP7YsWLdKBAwc0derUFhkOAACgLTTpPUSvvPKKLrvssgbbL7/8cuXm5jZ7KAAAgLbUpCAqLS1VTExMg+0RERHav39/s4cCAABoS00Kori4OK1fv77B9vXr1ys2NrbZQwEAALSlJr2HaNy4cZowYYJqa2s1YMAASVJ+fr6mTJnCN1UDAIBzTpOCaPLkyfrxxx/1wAMPqKamRpIUEBCgqVOnatq0aS06IAAAQGtrUhC5XC4999xzmj59unbv3q3AwEB1795d/v7+LT0fAABAq2tSEJ3UoUMHXX311S01CwAAgCOa9KZqAACA8wlBBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIzXboJo5syZcrlcmjBhgr3t+PHjysjIUKdOndShQwcNHTpUZWVlHo/bs2ePBg8erKCgIEVGRmry5Mk6ceKEx5qCggJdddVV8vf3V7du3bR48eI2uCIAAHCuaBdBtGXLFr3yyiu64oorPLZPnDhR77//vv7yl79o3bp12rdvn+644w57f11dnQYPHqyamhpt2LBBS5Ys0eLFi5WdnW2vKSkp0eDBg5Wamqpt27ZpwoQJ+u1vf6sPP/ywza4PAAC0b44H0ZEjRzR8+HC99tpruuCCC+ztlZWV+tOf/qQXXnhBAwYMUGJiol5//XVt2LBBGzdulCStXr1au3bt0n/913+pb9++uuWWW/TUU09p4cKFqqmpkSTl5uYqISFBc+bMUc+ePZWZmalf//rXmjt3riPXCwAA2h/HgygjI0ODBw9WWlqax/aioiLV1tZ6bL/ssssUHx+vwsJCSVJhYaF69+6tqKgoe016errcbrd27txpr/nXY6enp9vHOJ3q6mq53W6PGwAAOH/5OHnyP//5z9q6dau2bNnSYF9paan8/PzUsWNHj+1RUVEqLS2115waQyf3n9z3c2vcbreOHTumwMDABufOycnRjBkzmnxdAADg3OLYM0R79+7V73//ey1dulQBAQFOjXFa06ZNU2VlpX3bu3ev0yMBAIBW5FgQFRUVqby8XFdddZV8fHzk4+OjdevWaf78+fLx8VFUVJRqampUUVHh8biysjJFR0dLkqKjoxt86uzk/TOtCQ0NPe2zQ5Lk7++v0NBQjxsAADh/ORZEN998s7Zv365t27bZt6SkJA0fPtz+b19fX+Xn59uPKS4u1p49e5SSkiJJSklJ0fbt21VeXm6vWbNmjUJDQ9WrVy97zanHOLnm5DEAAAAcew9RSEiIfvGLX3hsCw4OVqdOneztY8eOVVZWlsLDwxUaGqoHH3xQKSkpuvbaayVJAwcOVK9evTRixAjNmjVLpaWleuyxx5SRkSF/f39J0u9+9zstWLBAU6ZM0ZgxY/TRRx/p7bffVl5eXtteMAAAaLccfVP1mcydO1deXl4aOnSoqqurlZ6erj/84Q/2fm9vb61cuVLjx49XSkqKgoODNWrUKD355JP2moSEBOXl5WnixIl68cUX1aVLF/3xj39Uenq6E5cEAADaIZdlWZbTQ7R3brdbYWFhqqysbNX3EyVOfqPVjo2zUzR7pNMjAACa6Wz+/Xb8e4gAAACcRhABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADCej9MDAO3Rnid7Oz0CThGfvd3pEQCc53iGCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMZzNIhycnJ09dVXKyQkRJGRkRoyZIiKi4s91hw/flwZGRnq1KmTOnTooKFDh6qsrMxjzZ49ezR48GAFBQUpMjJSkydP1okTJzzWFBQU6KqrrpK/v7+6deumxYsXt/blAQCAc4SjQbRu3TplZGRo48aNWrNmjWprazVw4EBVVVXZayZOnKj3339ff/nLX7Ru3Trt27dPd9xxh72/rq5OgwcPVk1NjTZs2KAlS5Zo8eLFys7OtteUlJRo8ODBSk1N1bZt2zRhwgT99re/1Ycfftim1wsAANonl2VZltNDnHTgwAFFRkZq3bp16tevnyorKxUREaFly5bp17/+tSTpq6++Us+ePVVYWKhrr71WH3zwgX71q19p3759ioqKkiTl5uZq6tSpOnDggPz8/DR16lTl5eVpx44d9rmGDRumiooKrVq16oxzud1uhYWFqbKyUqGhoa1z8ZISJ7/RasfG2VkeMtvpEXCK+OztTo8A4Bx0Nv9+t6v3EFVWVkqSwsPDJUlFRUWqra1VWlqaveayyy5TfHy8CgsLJUmFhYXq3bu3HUOSlJ6eLrfbrZ07d9prTj3GyTUnjwEAAMzm4/QAJ9XX12vChAm6/vrr9Ytf/EKSVFpaKj8/P3Xs2NFjbVRUlEpLS+01p8bQyf0n9/3cGrfbrWPHjikwMNBjX3V1taqrq+37bre7+RcIAADarXYTRBkZGdqxY4c+/fRTp0dRTk6OZsyY4fQYAM4jvCTefhTNHun0CGiH2sVLZpmZmVq5cqXWrl2rLl262Nujo6NVU1OjiooKj/VlZWWKjo621/zrp85O3j/TmtDQ0AbPDknStGnTVFlZad/27t3b7GsEAADtl6NBZFmWMjMztXz5cn300UdKSEjw2J+YmChfX1/l5+fb24qLi7Vnzx6lpKRIklJSUrR9+3aVl5fba9asWaPQ0FD16tXLXnPqMU6uOXmMf+Xv76/Q0FCPGwAAOH85+pJZRkaGli1bpnfffVchISH2e37CwsIUGBiosLAwjR07VllZWQoPD1doaKgefPBBpaSk6Nprr5UkDRw4UL169dKIESM0a9YslZaW6rHHHlNGRob8/f0lSb/73e+0YMECTZkyRWPGjNFHH32kt99+W3l5eY5dOwAAaD8cfYbo5ZdfVmVlpfr376+YmBj79tZbb9lr5s6dq1/96lcaOnSo+vXrp+joaP3tb3+z93t7e2vlypXy9vZWSkqK7r33Xo0cOVJPPvmkvSYhIUF5eXlas2aN+vTpozlz5uiPf/yj0tPT2/R6AQBA++ToM0SN+QqkgIAALVy4UAsXLvy3ay666CL993//988ep3///vr888/PekYAAHD+axdvqgYAAHASQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIzn4/QAAAC0pT1P9nZ6BJwiPnu70yNI4hkiAAAAs4Jo4cKF6tq1qwICApScnKzNmzc7PRIAAGgHjAmit956S1lZWXr88ce1detW9enTR+np6SovL3d6NAAA4DBjguiFF17QuHHjNHr0aPXq1Uu5ubkKCgrSokWLnB4NAAA4zIggqqmpUVFRkdLS0uxtXl5eSktLU2FhoYOTAQCA9sCIT5n98MMPqqurU1RUlMf2qKgoffXVVw3WV1dXq7q62r5fWVkpSXK73a06Z131sVY9PhrvsG+d0yPgFK39u9cW+P1uP/j9bl9a8/f75LEtyzrjWiOC6Gzl5ORoxowZDbbHxcU5MA2c8AunB4CnnDCnJ8B5hN/vdqYNfr8PHz6ssLCfP48RQdS5c2d5e3urrKzMY3tZWZmio6MbrJ82bZqysrLs+/X19Tp48KA6deokl8vV6vPCWW63W3Fxcdq7d69CQ0OdHgdAC+L32yyWZenw4cOKjY0941ojgsjPz0+JiYnKz8/XkCFDJP0UOfn5+crMzGyw3t/fX/7+/h7bOnbs2AaToj0JDQ3lf5jAeYrfb3Oc6Zmhk4wIIknKysrSqFGjlJSUpGuuuUbz5s1TVVWVRo8e7fRoAADAYcYE0V133aUDBw4oOztbpaWl6tu3r1atWtXgjdYAAMA8xgSRJGVmZp72JTLgVP7+/nr88ccbvGwK4NzH7zf+HZfVmM+iAQAAnMeM+GJGAACAn0MQAQAA4xFEAADAeAQR8C8WLlyorl27KiAgQMnJydq8ebPTIwFoAR9//LFuu+02xcbGyuVyacWKFU6PhHaEIAJO8dZbbykrK0uPP/64tm7dqj59+ig9PV3l5eVOjwagmaqqqtSnTx8tXLjQ6VHQDvEpM+AUycnJuvrqq7VgwQJJP32jeVxcnB588EE98sgjDk8HoKW4XC4tX77c/usFAM8QAf9UU1OjoqIipaWl2du8vLyUlpamwsJCBycDALQ2ggj4px9++EF1dXUNvr08KipKpaWlDk0FAGgLBBEAADAeQQT8U+fOneXt7a2ysjKP7WVlZYqOjnZoKgBAWyCIgH/y8/NTYmKi8vPz7W319fXKz89XSkqKg5MBAFqbUX/cFTiTrKwsjRo1SklJSbrmmms0b948VVVVafTo0U6PBqCZjhw5om+//da+X1JSom3btik8PFzx8fEOTob2gI/dA/9iwYIFmj17tkpLS9W3b1/Nnz9fycnJTo8FoJkKCgqUmpraYPuoUaO0ePHith8I7QpBBAAAjMd7iAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAnBOeeOIJ9e3b1+kxTqsxs/3mN7/RkCFD2mQeAGePIALQZLm5uQoJCdGJEyfsbUeOHJGvr6/69+/vsbagoEAul0vfffddG0/Z+h5++GGPPwrcVtpzJALnGoIIQJOlpqbqyJEj+uyzz+xtn3zyiaKjo7Vp0yYdP37c3r527VrFx8frkksuOatzWJblEVztUYcOHdSpUyenxwDQDAQRgCbr0aOHYmJiVFBQYG8rKCjQ7bffroSEBG3cuNFje2pqqqqrq/XQQw8pMjJSAQEBuuGGG7RlyxaPdS6XSx988IESExPl7++vTz/9tMG5v/vuO1188cXKzMzUmf4k448//qi7775bF154oYKCgtS7d2+9+eabHmvq6+s1a9YsdevWTf7+/oqPj9czzzxj7//HP/6hu+++W+Hh4QoODlZSUpI2bdokqeEzNXV1dcrKylLHjh3VqVMnTZkypcGM9fX1ysnJUUJCggIDA9WnTx/99a9/bfBzyM/PV1JSkoKCgnTdddepuLhYkrR48WLNmDFDX3zxhVwul1wuF3+gFGgGgghAs6Smpmrt2rX2/bVr16p///666aab7O3Hjh3Tpk2blJqaqilTpuidd97RkiVLtHXrVnXr1k3p6ek6ePCgx3EfeeQRzZw5U7t379YVV1zhse/LL7/UDTfcoHvuuUcLFiyQy+X62RmPHz+uxMRE5eXlaceOHbrvvvs0YsQIbd682V4zbdo0zZw5U9OnT9euXbu0bNkyRUVFSfrpZcCbbrpJ33//vd577z198cUXmjJliurr6097vjlz5mjx4sVatGiRPv30Ux08eFDLly/3WJOTk6M33nhDubm52rlzpyZOnKh7771X69at81j36KOPas6cOfrss8/k4+OjMWPGSJLuuusuTZo0SZdffrn279+v/fv366677vrZnwOAn2EBQDO89tprVnBwsFVbW2u53W7Lx8fHKi8vt5YtW2b169fPsizLys/PtyRZ//u//2v5+vpaS5cutR9fU1NjxcbGWrNmzbIsy7LWrl1rSbJWrFjhcZ7HH3/c6tOnj7V+/XrrggsusJ5//vlmzT148GBr0qRJlmVZltvttvz9/a3XXnvttGtfeeUVKyQkxPrxxx9Pu//kbCfFxMTY12NZllVbW2t16dLFuv322y3Lsqzjx49bQUFB1oYNGzyOM3bsWOvuu++2LOv//xz+53/+x96fl5dnSbKOHTt22vMCaDofZ3MMwLmuf//+qqqq0pYtW3To0CFdeumlioiI0E033aTRo0fr+PHjKigo0MUXX6zKykrV1tbq+uuvtx/v6+ura665Rrt37/Y4blJSUoNz7dmzR7/85S/1zDPPaMKECY2esa6uTs8++6zefvttff/996qpqVF1dbWCgoIkSbt371Z1dbVuvvnm0z5+27ZtuvLKKxUeHn7Gc1VWVmr//v1KTk62t/n4+CgpKcl+2ezbb7/V0aNH9ctf/tLjsTU1Nbryyis9tp367FhMTIwkqby8XPHx8Y24cgCNRRABaJZu3bqpS5cuWrt2rQ4dOqSbbrpJkhQbG6u4uDht2LBBa9eu1YABA87quMHBwQ22RUREKDY2Vm+++abGjBmj0NDQRh1r9uzZevHFFzVv3jz17t1bwcHBmjBhgmpqaiRJgYGBP/v4M+0/W0eOHJEk5eXl6cILL/TY5+/v73Hf19fX/u+TLw3+u5fqADQd7yEC0GypqakqKChQQUGBx8ft+/Xrpw8++ECbN29WamqqLrnkEvn5+Wn9+vX2mtraWm3ZskW9evU643kCAwO1cuVKBQQEKD09XYcPH27UfOvXr9ftt9+ue++9V3369NHFF1+sr7/+2t7fvXt3BQYG/tuPzl9xxRXatm1bg/c5nU5YWJhiYmLsN1xL0okTJ1RUVGTf79Wrl/z9/bVnzx5169bN4xYXF9eoa5IkPz8/1dXVNXo9gH+PIALQbKmpqfr000+1bds2+xkiSbrpppv0yiuvqKamRqmpqQoODtb48eM1efJkrVq1Srt27dK4ceN09OhRjR07tlHnCg4OVl5ennx8fHTLLbfYz7b8nO7du2vNmjXasGGDdu/erfvvv19lZWX2/oCAAE2dOlVTpkzRG2+8oe+++04bN27Un/70J0nS3XffrejoaA0ZMkTr16/X3//+d73zzjsqLCw87fl+//vfa+bMmVqxYoW++uorPfDAA6qoqLD3h4SE6OGHH9bEiRO1ZMkSfffdd9q6dateeuklLVmypFE/B0nq2rWrSkpKtG3bNv3www+qrq5u9GMBeCKIADRbamqqjh07pm7dutmfzJJ+CqLDhw/bH8+XpJkzZ2ro0KEaMWKErrrqKn377bf68MMPdcEFFzT6fB06dNAHH3wgy7I0ePBgVVVV/ez6xx57TFdddZXS09PVv39/O25ONX36dE2aNEnZ2dnq2bOn7rrrLpWXl0v66ZmY1atXKzIyUrfeeqt69+6tmTNnytvb+7TnmzRpkkaMGKFRo0YpJSVFISEh+o//+A+PNU899ZSmT5+unJwc9ezZU4MGDVJeXp4SEhIa/XMYOnSoBg0apNTUVEVERDT4KgEAjeeyrDN8gQcAAMB5jmeIAACA8QgiAOe8W265RR06dDjt7dlnn3V6PADnAF4yA3DO+/7773Xs2LHT7gsPD2/U9wcBMBtBBAAAjMdLZgAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADj/T9FGJ4+BfwKfwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drawing more than just the project count to visualize more factors\n",
    "\n",
    "barplot_cols = ['number_project', 'salary', 'time_spend_company', 'satisfaction_level', 'last_evaluation', 'average_monthly_hours', 'Work_accident']\n",
    "\n",
    "for col in barplot_cols:\n",
    "    df2 = df.groupby([col])['left'].value_counts().reset_index(name='count')\n",
    "    df2 = pd.DataFrame(df2)\n",
    "\n",
    "    sns.barplot(df2, x=col, y='count', hue='left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e851b11c-a27b-4779-a43a-bb63a3af07dc",
   "metadata": {},
   "source": [
    "### Inferences\n",
    "* People who are either not busy enough or too busy are more likely to leave. 3 - 5 projects appear to be the sweet spot.\n",
    "* People are more likely to leave after 3 - 6 years\n",
    "* People with lower salary are more likely to leave"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295f381f-bd79-44f3-8d8d-788bc0260769",
   "metadata": {},
   "source": [
    "## 3. Perform clustering of employees who left based on their satisfaction and evaluation\n",
    "\n",
    "### 3.1 Choose columns satisfaction_level, last_evaluation and left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "854dde89-8b6e-4657-9144-e1dad5397420",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left\n",
       "0    11428\n",
       "1     3571\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df[['satisfaction_level', 'last_evaluation', 'left']]\n",
    "df1['left'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c137199-f37c-4a10-b52f-852cb704591e",
   "metadata": {},
   "source": [
    "### 3.2 Do K-Means clustering of employees who left the company into 3 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0dd3c022-55ac-409a-9d71-3bb9c191b7cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df1.loc[df1['left'] == 1]\n",
    "X = df2.drop('left', axis=1).values\n",
    "\n",
    "numClusters = 3\n",
    "\n",
    "model = KMeans(n_clusters = numClusters, n_init=10, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = model.fit_predict(X)\n",
    "\n",
    "colors = ['red', 'blue', 'green']\n",
    "labels = ['c1', 'c2', 'c3']\n",
    "\n",
    "for x in range(numClusters):\n",
    "    plt.scatter(X[y_kmeans == x, 0], X[y_kmeans == x, 1], s = 100, c = colors[x], label = labels[x])\n",
    "\n",
    "plt.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of employees who left the company')\n",
    "plt.xlabel('Satisfaction Level')\n",
    "plt.ylabel('Last Evaluation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62841115-4547-484d-a4bb-4a1e6254d36d",
   "metadata": {},
   "source": [
    "### 3.3 Based on the satisfaction and evaluation factors, give your thoughts on the employee clusters\n",
    "\n",
    "* The blue cluster makes sense. Evaluated well and sastisfied. So why did they leave? Further investigation is needed.\n",
    "* The red cluster also makes sense. Not evaluated well and not satisfied.\n",
    "* The green cluster, I believe, represents those who have outgrown the company. Evaluated highly but they want to do more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4c0bc664-4cae-4e8e-894e-a2ae4211068f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Out of curiosity, attempt to cluster employees who have not left\n",
    "\n",
    "df2 = df1.loc[df1['left'] == 0]\n",
    "X = df2.drop('left', axis=1).values\n",
    "\n",
    "numClusters = 3\n",
    "\n",
    "model = KMeans(n_clusters = numClusters, n_init=10, init = 'k-means++', random_state = 42)\n",
    "y_kmeans = model.fit_predict(X)\n",
    "\n",
    "for x in range(numClusters):\n",
    "    plt.scatter(X[y_kmeans == x, 0], X[y_kmeans == x, 1], s = 100, c = colors[x], label = labels[x])\n",
    "\n",
    "plt.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of employees who have not left the company')\n",
    "plt.xlabel('Satisfaction Level')\n",
    "plt.ylabel('Last Evaluation')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9384a70f-65ff-4f37-9127-bec4974f3821",
   "metadata": {},
   "source": [
    "... Clusters of this data set aren't telling us much..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd7fe31-3faf-4817-ba2e-ea1ca17a3a8b",
   "metadata": {},
   "source": [
    "## 4. Handle the left Class Imbalance using the SMOTE technique\n",
    "\n",
    "### 4.1 Pre-process the data by converting categorical columns to numerical columns by\n",
    "* Separating categorical variables and numeric variables.\n",
    "* Applying get_dummies() to the categorical variables.\n",
    "* Combining categorical variables and numeric variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5b46183d-fb47-47e8-81ab-4168295d61f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_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>sales_RandD</th>\n",
       "      <th>sales_accounting</th>\n",
       "      <th>sales_hr</th>\n",
       "      <th>sales_management</th>\n",
       "      <th>sales_marketing</th>\n",
       "      <th>sales_product_mng</th>\n",
       "      <th>sales_sales</th>\n",
       "      <th>sales_support</th>\n",
       "      <th>sales_technical</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
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       "      <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",
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       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_monthly_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  \\\n",
       "0                   3              0     1                      0   \n",
       "1                   6              0     1                      0   \n",
       "2                   4              0     1                      0   \n",
       "3                   5              0     1                      0   \n",
       "4                   3              0     1                      0   \n",
       "\n",
       "   sales_RandD  sales_accounting  sales_hr  sales_management  sales_marketing  \\\n",
       "0            0                 0         0                 0                0   \n",
       "1            0                 0         0                 0                0   \n",
       "2            0                 0         0                 0                0   \n",
       "3            0                 0         0                 0                0   \n",
       "4            0                 0         0                 0                0   \n",
       "\n",
       "   sales_product_mng  sales_sales  sales_support  sales_technical  salary_low  \\\n",
       "0                  0            1              0                0           1   \n",
       "1                  0            1              0                0           0   \n",
       "2                  0            1              0                0           0   \n",
       "3                  0            1              0                0           1   \n",
       "4                  0            1              0                0           1   \n",
       "\n",
       "   salary_medium  \n",
       "0              0  \n",
       "1              1  \n",
       "2              1  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_df = df.select_dtypes(include=[np.number])\n",
    "categorical_df = df.select_dtypes(exclude=[np.number])\n",
    "\n",
    "encoded_df = pd.get_dummies(categorical_df, columns=['sales', 'salary'], drop_first=True, dtype=int)\n",
    "\n",
    "result_df = pd.concat([numeric_df, encoded_df], axis=1)\n",
    "\n",
    "result_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86acc3c4-0da7-4acf-b766-74453356c8c0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 4.2 Do the stratified split of the dataset to train and test in the ratio 80:20 with random_state=123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bda9f27e-3979-4e50-bb9f-a456b27d6fec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   satisfaction_level  last_evaluation  number_project  average_monthly_hours  \\\n",
      "0                0.38             0.53             0.0               0.285047   \n",
      "1                0.80             0.86             0.6               0.775701   \n",
      "2                0.11             0.88             1.0               0.822430   \n",
      "3                0.72             0.87             0.6               0.593458   \n",
      "4                0.37             0.52             0.0               0.294393   \n",
      "\n",
      "   time_spend_company  Work_accident  promotion_last_5years  sales_RandD  \\\n",
      "0               0.125              0                      0            0   \n",
      "1               0.500              0                      0            0   \n",
      "2               0.250              0                      0            0   \n",
      "3               0.375              0                      0            0   \n",
      "4               0.125              0                      0            0   \n",
      "\n",
      "   sales_accounting  sales_hr  sales_management  sales_marketing  \\\n",
      "0                 0         0                 0                0   \n",
      "1                 0         0                 0                0   \n",
      "2                 0         0                 0                0   \n",
      "3                 0         0                 0                0   \n",
      "4                 0         0                 0                0   \n",
      "\n",
      "   sales_product_mng  sales_sales  sales_support  sales_technical  salary_low  \\\n",
      "0                  0            1              0                0           1   \n",
      "1                  0            1              0                0           0   \n",
      "2                  0            1              0                0           0   \n",
      "3                  0            1              0                0           1   \n",
      "4                  0            1              0                0           1   \n",
      "\n",
      "   salary_medium  \n",
      "0              0  \n",
      "1              1  \n",
      "2              1  \n",
      "3              0  \n",
      "4              0  \n",
      "\n",
      "left\n",
      "0    11428\n",
      "1     3571\n",
      "Name: count, dtype: int64\n",
      "left\n",
      "0    9142\n",
      "1    2857\n",
      "Name: count, dtype: int64\n",
      "left\n",
      "0    2286\n",
      "1     714\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "X = result_df.drop(['left'], axis=1)\n",
    "y = result_df['left']\n",
    "\n",
    "# scale columns in X before splitting\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# The nature of satisfaction_level and last_evaluation indicates that they are already \"scaled\" between 0 and 1. Scale other 3 remaining integer features.\n",
    "cols = ['average_monthly_hours', 'time_spend_company', 'number_project']\n",
    "\n",
    "for col in cols:\n",
    "    X[col] = scaler.fit_transform(X[col].values.reshape(-1,1))\n",
    "\n",
    "print(X.head())\n",
    "print()\n",
    "    \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=123) \n",
    "\n",
    "print(y.value_counts())\n",
    "print(y_train.value_counts())\n",
    "print(y_test.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "491b551a-9d5a-442e-a893-5b5a64a39591",
   "metadata": {},
   "source": [
    "### 4.3 Upsample the train dataset using the SMOTE technique from the imblearn module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b0a97f64-b13a-4c39-a0d1-4fbf1526a994",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "left\n",
       "0    9142\n",
       "1    9142\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm = SMOTE(random_state = 123) \n",
    "X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c5dedc-d9d8-4087-8025-12341dee50ac",
   "metadata": {},
   "source": [
    "## 5. Perform 5-fold cross-validation model training and evaluate performance\n",
    "### 5.1 Train a Logistic Regression model, apply a 5-fold CV, and plot the classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7ad6a48d-db31-46e1-866c-4321c61be79e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, make_scorer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True)\n",
    "\n",
    "def classification_report_with_accuracy_score(y_true, y_pred):\n",
    "    print (classification_report(y_true, y_pred)) # print classification report for each fold\n",
    "    return accuracy_score(y_true, y_pred) # return accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "90d02e50-bba4-4f48-93ce-677c50561408",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.92      0.87      2285\n",
      "           1       0.60      0.37      0.46       715\n",
      "\n",
      "    accuracy                           0.79      3000\n",
      "   macro avg       0.71      0.65      0.66      3000\n",
      "weighted avg       0.77      0.79      0.77      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.93      0.87      2286\n",
      "           1       0.59      0.32      0.41       714\n",
      "\n",
      "    accuracy                           0.79      3000\n",
      "   macro avg       0.70      0.63      0.64      3000\n",
      "weighted avg       0.76      0.79      0.76      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.93      0.87      2286\n",
      "           1       0.61      0.34      0.44       714\n",
      "\n",
      "    accuracy                           0.79      3000\n",
      "   macro avg       0.71      0.64      0.66      3000\n",
      "weighted avg       0.77      0.79      0.77      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.93      0.87      2286\n",
      "           1       0.59      0.34      0.44       714\n",
      "\n",
      "    accuracy                           0.79      3000\n",
      "   macro avg       0.71      0.64      0.65      3000\n",
      "weighted avg       0.76      0.79      0.77      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.93      0.87      2285\n",
      "           1       0.62      0.34      0.44       714\n",
      "\n",
      "    accuracy                           0.79      2999\n",
      "   macro avg       0.72      0.64      0.65      2999\n",
      "weighted avg       0.77      0.79      0.77      2999\n",
      "\n",
      "Mean score of 0.79 with a standard deviation of 0.00\n",
      "0.7909193946263084\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "                        \n",
    "log_reg_scores = cross_val_score(log_reg, X, y, cv=kfold, scoring=make_scorer(classification_report_with_accuracy_score))\n",
    "\n",
    "print(\"Mean score of %0.2f with a standard deviation of %0.2f\" % (log_reg_scores.mean(), log_reg_scores.std()))\n",
    "\n",
    "log_reg_y_pred = cross_val_predict(log_reg, X, y, cv=kfold)\n",
    "log_reg_accuracy = accuracy_score(log_reg_y_pred.astype(int), y.astype(int))\n",
    "print(log_reg_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "905c69d2-553d-4824-bcb4-8f3723956417",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7917189729909969\n",
      "{1: array([[0.76166667, 0.76166667, 0.77133333, 0.78366667, 0.79366667,\n",
      "        0.79633333, 0.79633333, 0.79633333, 0.79633333, 0.79633333],\n",
      "       [0.762     , 0.762     , 0.77033333, 0.77566667, 0.78966667,\n",
      "        0.792     , 0.792     , 0.792     , 0.792     , 0.792     ],\n",
      "       [0.762     , 0.762     , 0.77166667, 0.785     , 0.79766667,\n",
      "        0.798     , 0.799     , 0.799     , 0.799     , 0.799     ],\n",
      "       [0.762     , 0.762     , 0.776     , 0.77333333, 0.782     ,\n",
      "        0.787     , 0.78666667, 0.78666667, 0.78666667, 0.78666667],\n",
      "       [0.76192064, 0.76192064, 0.77425809, 0.77625875, 0.7795932 ,\n",
      "        0.78426142, 0.78459486, 0.78459486, 0.78459486, 0.78459486]])}\n"
     ]
    }
   ],
   "source": [
    "# for good measure, try with LogicticRegressionCV. Should result in similar scores\n",
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "lrcv = LogisticRegressionCV(cv=kfold, scoring='accuracy', random_state=42)\n",
    "lrcv.fit(X, y)\n",
    "print(lrcv.scores_[1].mean(axis=0).max()) \n",
    "print(lrcv.scores_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02dc7b77-cb61-4537-b6fb-2420330df506",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 5.2 Train a Random Forest Classifier model, apply the 5-fold CV, and plot the classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "51d28f70-017e-4e33-94c6-a256380ccc1c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      2285\n",
      "           1       0.99      0.96      0.98       715\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.99      0.98      0.98      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00      2286\n",
      "           1       0.99      0.98      0.99       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.99      0.99      0.99      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      2286\n",
      "           1       1.00      0.96      0.98       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.99      0.98      0.99      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      2286\n",
      "           1       0.99      0.97      0.98       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.99      0.99      0.99      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      2285\n",
      "           1       0.99      0.97      0.98       714\n",
      "\n",
      "    accuracy                           0.99      2999\n",
      "   macro avg       0.99      0.98      0.99      2999\n",
      "weighted avg       0.99      0.99      0.99      2999\n",
      "\n",
      "Mean score of 0.99 with a standard deviation of 0.00\n",
      "0.9910660710714048\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rand_for = RandomForestClassifier(random_state = 42)\n",
    "\n",
    "rand_for_scores = cross_val_score(rand_for, X, y, cv=kfold, scoring=make_scorer(classification_report_with_accuracy_score))\n",
    "\n",
    "print(\"Mean score of %0.2f with a standard deviation of %0.2f\" % (rand_for_scores.mean(), rand_for_scores.std()))\n",
    "\n",
    "rand_for_y_pred = cross_val_predict(rand_for, X, y, cv=kfold)\n",
    "rand_for_accuracy = accuracy_score(rand_for_y_pred.astype(int), y.astype(int))\n",
    "\n",
    "print(rand_for_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebab974-c60e-4022-a666-be4d5e406a18",
   "metadata": {},
   "source": [
    "### 5.3 Train a Gradient Boosting Classifier model, apply the 5-fold CV, and plot the classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5a184a8-d41f-436d-8da0-ddf083b163a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      2285\n",
      "           1       0.97      0.92      0.95       715\n",
      "\n",
      "    accuracy                           0.97      3000\n",
      "   macro avg       0.97      0.96      0.96      3000\n",
      "weighted avg       0.97      0.97      0.97      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      2286\n",
      "           1       0.97      0.93      0.95       714\n",
      "\n",
      "    accuracy                           0.97      3000\n",
      "   macro avg       0.97      0.96      0.96      3000\n",
      "weighted avg       0.97      0.97      0.97      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      2286\n",
      "           1       0.97      0.93      0.95       714\n",
      "\n",
      "    accuracy                           0.98      3000\n",
      "   macro avg       0.97      0.96      0.97      3000\n",
      "weighted avg       0.98      0.98      0.98      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99      2286\n",
      "           1       0.98      0.94      0.96       714\n",
      "\n",
      "    accuracy                           0.98      3000\n",
      "   macro avg       0.98      0.97      0.97      3000\n",
      "weighted avg       0.98      0.98      0.98      3000\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98      2285\n",
      "           1       0.96      0.93      0.95       714\n",
      "\n",
      "    accuracy                           0.98      2999\n",
      "   macro avg       0.97      0.96      0.97      2999\n",
      "weighted avg       0.98      0.98      0.98      2999\n",
      "\n",
      "Mean score of 0.98 with a standard deviation of 0.00\n",
      "0.9761984132275485\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "grad_boost = GradientBoostingClassifier(random_state = 42)\n",
    "\n",
    "grad_boost_scores = cross_val_score(grad_boost, X, y, cv=kfold, scoring=make_scorer(classification_report_with_accuracy_score))\n",
    "\n",
    "print(\"Mean score of %0.2f with a standard deviation of %0.2f\" % (grad_boost_scores.mean(), grad_boost_scores.std()))\n",
    "\n",
    "grad_boost_y_pred = cross_val_predict(grad_boost, X, y, cv=kfold)\n",
    "grad_boost_accuracy = accuracy_score(grad_boost_y_pred.astype(int), y.astype(int))\n",
    "\n",
    "print(grad_boost_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73927966-e17b-4bf0-a092-a536fa37e96f",
   "metadata": {},
   "source": [
    "## 6. Identify the best model and justify the evaluation metrics used\n",
    "### 6.1 Find the ROC/AUC for each model and plot the ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07b2157c-d157-429e-9340-af487b40337b",
   "metadata": {},
   "source": [
    "#### 6.1.1 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ea16e14-8a48-4a80-a143-030b16d9b566",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "def plot_roc(model, model_name, X_train, y_train, X_test, y_test):\n",
    "    y_pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "    auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "    plt.plot(fpr,tpr)\n",
    "    plt.title(model_name + ' ROC')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()\n",
    "\n",
    "    print('AUC: ', auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2b728dcb-3056-42c8-96e7-cae1728482c9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.827264239028945\n"
     ]
    }
   ],
   "source": [
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "plot_roc(log_reg, 'Logic Regression', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0a7dd0-b870-42db-bcb7-6b4c95daa672",
   "metadata": {},
   "source": [
    "#### 6.1.2 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e6bcbc70-bd8f-41d4-8580-4e3bcd172aed",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.9945965700365886\n"
     ]
    }
   ],
   "source": [
    "rand_for.fit(X_train, y_train)\n",
    "\n",
    "plot_roc(rand_for, 'Random Forest', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b52a84-459d-4b75-a9aa-63eb8efb0aad",
   "metadata": {},
   "source": [
    "#### 6.1.3 Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "842d95e6-91e4-4c93-97c6-bfd2cfecff47",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC:  0.9859055608245049\n"
     ]
    }
   ],
   "source": [
    "grad_boost.fit(X_train, y_train)\n",
    "\n",
    "plot_roc(grad_boost, 'Gradient Boosting', X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "267173b0-5d02-4696-9353-2c651a9bd4f6",
   "metadata": {},
   "source": [
    "### 6.2 Find the confusion matrix for each of the models\n",
    "\n",
    "#### 6.2.1 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27e64b20-5671-4162-ac31-b915cbe9288e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1718,  568],\n",
       "       [ 135,  579]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_y_pred = log_reg.predict(X_test)\n",
    "\n",
    "lrcm = confusion_matrix(y_test, lr_y_pred)\n",
    "lrcm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5f5634af-25c9-472a-9988-51682bc67cf0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.75      0.83      2286\n",
      "           1       0.50      0.81      0.62       714\n",
      "\n",
      "    accuracy                           0.77      3000\n",
      "   macro avg       0.72      0.78      0.73      3000\n",
      "weighted avg       0.83      0.77      0.78      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, lr_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ead119f4-fe5e-452b-bd74-f22180cb64d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(lrcm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "674cc7b2-3e46-49cd-916f-efe48d97b13c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 6.2.2 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "16889197-7bf9-4f06-8fab-a78fe5feacf8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2275,   11],\n",
       "       [  13,  701]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_y_pred = rand_for.predict(X_test)\n",
    "\n",
    "rdcm = confusion_matrix(y_test, rf_y_pred)\n",
    "rdcm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5e55ab02-5ec0-4592-887e-7c21390b9b7d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      2286\n",
      "           1       0.98      0.98      0.98       714\n",
      "\n",
      "    accuracy                           0.99      3000\n",
      "   macro avg       0.99      0.99      0.99      3000\n",
      "weighted avg       0.99      0.99      0.99      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rf_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b415e59e-475a-4205-adb9-d74375fcd54c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(rdcm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ca9c37-416c-4526-aa5f-085b2373e2c9",
   "metadata": {},
   "source": [
    "#### 6.2.3 Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "09ea312b-2410-4657-859a-88c2d990353f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2232,   54],\n",
       "       [  45,  669]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_y_pred = grad_boost.predict(X_test)\n",
    "\n",
    "gbcm = confusion_matrix(y_test, gb_y_pred)\n",
    "gbcm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f260175d-ac5e-496c-8e32-df9835a78dd0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      2286\n",
      "           1       0.93      0.94      0.93       714\n",
      "\n",
      "    accuracy                           0.97      3000\n",
      "   macro avg       0.95      0.96      0.95      3000\n",
      "weighted avg       0.97      0.97      0.97      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, gb_y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "14995fc5-7ca6-4604-bb05-a980510efabc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(gbcm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d01cae3-f296-4fc1-b3c6-854e2298dab9",
   "metadata": {},
   "source": [
    "### 6.3 Explain which metric needs to be used from the confusion matrix - Recall or Precision?\n",
    "\n",
    "For the objective of this research, we are trying to avoid any surprise departure by an employee. We want false negative to be low. Therefore, we should focus more on Recall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3bb2f26-a57f-42e9-ad7a-f4b35ab66ec5",
   "metadata": {},
   "source": [
    "### Overall, Random Forest scored the highest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445898da-4c64-4e57-9c86-01e48e7610a2",
   "metadata": {},
   "source": [
    "## 7. Suggest various retention strategies for targeted employees\n",
    "### 7.1 Using the best model, predict the probability of employee turnover in the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0b19ee-7db4-420d-88d8-38d56c1bbdfb",
   "metadata": {},
   "source": [
    "Based on scores above, we conclude that Random Forest is the best model for our purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "290cada4-25f9-4505-aacf-e95bf5a30e64",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 0.  ],\n",
       "       [0.66, 0.34],\n",
       "       [0.97, 0.03],\n",
       "       ...,\n",
       "       [0.98, 0.02],\n",
       "       [0.97, 0.03],\n",
       "       [0.  , 1.  ]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rand_for.predict_proba(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "df236ff8-b91b-4d3d-838b-bfce0cde97cb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.34, 0.03, ..., 0.02, 0.03, 1.  ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151d9c61-a659-42ef-b570-29fa929ef8db",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 7.2 a. Based on the probability score range below, categorize the employees into four zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9b9e88ed-2ced-41b4-b06f-ecff19592fa3",
   "metadata": {
    "tags": []
   },
   "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_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_monthly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales_RandD</th>\n",
       "      <th>sales_accounting</th>\n",
       "      <th>sales_hr</th>\n",
       "      <th>...</th>\n",
       "      <th>sales_marketing</th>\n",
       "      <th>sales_product_mng</th>\n",
       "      <th>sales_sales</th>\n",
       "      <th>sales_support</th>\n",
       "      <th>sales_technical</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "      <th>Probability</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Colors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10627</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.443925</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Safe Zone</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2703</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.817757</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>Low Risk Zone</td>\n",
       "      <td>Yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6059</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.415888</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Safe Zone</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.593458</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>Safe Zone</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4565</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.224299</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Safe Zone</td>\n",
       "      <td>Green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "10627                0.98             0.77             0.2   \n",
       "2703                 0.76             0.93             0.2   \n",
       "6059                 0.70             0.93             0.2   \n",
       "3258                 0.48             0.61             0.2   \n",
       "4565                 0.57             0.90             0.2   \n",
       "\n",
       "       average_monthly_hours  time_spend_company  Work_accident  \\\n",
       "10627               0.443925               0.000              0   \n",
       "2703                0.817757               0.375              0   \n",
       "6059                0.415888               0.250              0   \n",
       "3258                0.593458               0.125              1   \n",
       "4565                0.224299               0.000              0   \n",
       "\n",
       "       promotion_last_5years  sales_RandD  sales_accounting  sales_hr  ...  \\\n",
       "10627                      0            0                 0         0  ...   \n",
       "2703                       0            0                 0         1  ...   \n",
       "6059                       0            0                 1         0  ...   \n",
       "3258                       0            0                 0         0  ...   \n",
       "4565                       0            0                 0         0  ...   \n",
       "\n",
       "       sales_marketing  sales_product_mng  sales_sales  sales_support  \\\n",
       "10627                0                  0            1              0   \n",
       "2703                 0                  0            0              0   \n",
       "6059                 0                  0            0              0   \n",
       "3258                 0                  0            0              1   \n",
       "4565                 0                  0            0              0   \n",
       "\n",
       "       sales_technical  salary_low  salary_medium  Probability           Zone  \\\n",
       "10627                0           0              1         0.00      Safe Zone   \n",
       "2703                 0           1              0         0.34  Low Risk Zone   \n",
       "6059                 0           0              1         0.03      Safe Zone   \n",
       "3258                 0           0              1         0.02      Safe Zone   \n",
       "4565                 0           0              1         0.00      Safe Zone   \n",
       "\n",
       "       Colors  \n",
       "10627   Green  \n",
       "2703   Yellow  \n",
       "6059    Green  \n",
       "3258    Green  \n",
       "4565    Green  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = X_test # preserve X_test\n",
    "\n",
    "test_data.loc[:, \"Probability\"] = y_pred[:,1]\n",
    "\n",
    "categories = ['Safe Zone', 'Low Risk Zone', 'Medium Risk Zone', 'High Risk Zone']\n",
    "colors = ['Green', 'Yellow', 'Orange', 'Red']\n",
    "bins = [-1, 0.2, 0.6, 0.9, 1.0]\n",
    "\n",
    "test_data['Zone'] = pd.cut(test_data['Probability'], bins=bins, labels=categories)\n",
    "test_data['Colors'] = pd.cut(test_data['Probability'], bins=bins, labels=colors)\n",
    "\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2cc79fc8-8907-4b5b-ba2a-bbf1f5a0e386",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=test_data, x='Zone', palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efa3d63-5f49-44b1-8bea-2e281fbc31b7",
   "metadata": {},
   "source": [
    "### 7.2 b. Suggest your thoughts on the retention strategies for each zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "68b2b4c3-271d-48fa-951e-7d3772176f17",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level - 26.61%\n",
      "time_spend_company - 24.86%\n",
      "number_project - 17.20%\n",
      "average_monthly_hours - 15.43%\n",
      "last_evaluation - 11.45%\n",
      "Work_accident - 1.36%\n",
      "salary_low - 0.69%\n",
      "sales_technical - 0.44%\n",
      "salary_medium - 0.36%\n",
      "sales_sales - 0.32%\n",
      "sales_support - 0.28%\n",
      "promotion_last_5years - 0.24%\n",
      "sales_accounting - 0.17%\n",
      "sales_hr - 0.16%\n",
      "sales_management - 0.13%\n",
      "sales_RandD - 0.12%\n",
      "sales_marketing - 0.11%\n",
      "sales_product_mng - 0.09%\n"
     ]
    }
   ],
   "source": [
    "# Identify important features\n",
    "\n",
    "feat_labels = X_train.columns\n",
    "importance = rand_for.feature_importances_\n",
    "feature_indexes_by_importance = importance.argsort()[::-1] # descending order\n",
    "\n",
    "for index in feature_indexes_by_importance:\n",
    "    print('{} - {:.2f}%'.format(feat_labels[index], (importance[index] *100.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cede48-6980-4791-af9c-3f27fcde45dc",
   "metadata": {},
   "source": [
    "We need to focus on these factors?\n",
    "* satisfaction_level - 26.61%\n",
    "* time_spend_company - 24.86%\n",
    "* number_project - 17.20%\n",
    "* average_monthly_hours - 15.43%\n",
    "* last_evaluation - 11.45%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164f7471-556e-41d4-b3c2-69fa60955ca2",
   "metadata": {},
   "source": [
    "* Safe Zone (Green) (Score < 20%)\n",
    "1. Satisfaction level is the most important factor. Regardless of the turnover probability, we must continously evaluate what satisfies employees and how we can improve. Continous improvement on the company culture should always be part of the improvement.\n",
    "1. Evaluate those with high satisfaction level. Find out what makes them satisfied.\n",
    "1. Evaluate the length of tenure for each employee in this zone. Newer employees may move to a higher risk zone in a year or two. Pay special attention to their satisfaction level, last evaluation and work load (i.e. average monthly hours and number of projects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fbfa08-6aa2-45dd-acae-7846c8ceb588",
   "metadata": {},
   "source": [
    "* Low Risk Zone (Yellow) (20% < Score < 60%)\n",
    "1. Same as Safe Zone recommendations. At this level, we just need to continue to improve attractiveness as an employer.\n",
    "1. Council those who scored low on their last evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c239b2f8-f3f0-4462-ab2d-844cad64085a",
   "metadata": {},
   "source": [
    "* Medium Risk Zone (Orange) (60% < Score < 90%)\n",
    "1. Continue improving the company culture\n",
    "1. Evaluate their work load (i.e. average monthly hours and number of projects). Are they overworked or not engaged enough?\n",
    "1. Council those who scored low on their last evaluation.\n",
    "1. Interview those with low satisfaction. Find out causes and ways to improve."
   ]
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    "* High Risk Zone (Red) (Score > 90%)\n",
    "1. Continue improving the company culture\n",
    "1. Evaluate their work load (i.e. average monthly hours and number of projects). Are they overworked or not engaged enough?\n",
    "1. Identify those who scored high on their last evaluation. At this risk level, we need to focus on retaining competent employees.\n",
    "1. Identify those with high satisfaction level. At this risk level, we need to focus on retaining those who are otherwise satisfied.\n",
    "1. ... with that said, try to identify causes for low satisfaction"
   ]
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