project_1/index.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objectives\n",
    "<ol>\n",
    "<li>Data Understanding</li>\n",
    "<li>Data cleaning</li>\n",
    "<li>Data preparation</li>\n",
    "<li>Explanatory Data Analysis</li>\n",
    "<li>Modelling</li>\n",
    "<li>Evaluation</li>\n",
    "<li>Conclusion</li>\n",
    "<li>Recommendatons</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sweetviz\n",
    "import model_evaluation_utils as meu\n",
    "\n",
    "# importing the models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns' ,None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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",
       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2</td>\n",
       "      <td>153</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>6</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.77</td>\n",
       "      <td>6</td>\n",
       "      <td>247</td>\n",
       "      <td>4</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>7</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>259</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>8</th>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>224</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>9</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>142</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",
       "5                0.41             0.50               2                   153   \n",
       "6                0.10             0.77               6                   247   \n",
       "7                0.92             0.85               5                   259   \n",
       "8                0.89             1.00               5                   224   \n",
       "9                0.42             0.53               2                   142   \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",
       "5                   3              0     1                      0  sales   \n",
       "6                   4              0     1                      0  sales   \n",
       "7                   5              0     1                      0  sales   \n",
       "8                   5              0     1                      0  sales   \n",
       "9                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  \n",
       "5     low  \n",
       "6     low  \n",
       "7     low  \n",
       "8     low  \n",
       "9     low  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the data\n",
    "main_df = pd.read_csv('HR_comma_sep.csv')\n",
    "main_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <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>14989</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5</td>\n",
       "      <td>228</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6</td>\n",
       "      <td>257</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.83</td>\n",
       "      <td>6</td>\n",
       "      <td>293</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "14989                0.43             0.57               2   \n",
       "14990                0.89             0.88               5   \n",
       "14991                0.09             0.81               6   \n",
       "14992                0.40             0.48               2   \n",
       "14993                0.76             0.83               6   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "14989                   159                   3              1     1   \n",
       "14990                   228                   5              1     1   \n",
       "14991                   257                   4              0     1   \n",
       "14992                   155                   3              0     1   \n",
       "14993                   293                   6              0     1   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years      sales salary  \n",
       "14989                      0  technical    low  \n",
       "14990                      0    support    low  \n",
       "14991                      0    support    low  \n",
       "14992                      0    support    low  \n",
       "14993                      0    support    low  \n",
       "14994                      0    support    low  \n",
       "14995                      0    support    low  \n",
       "14996                      0    support    low  \n",
       "14997                      0    support    low  \n",
       "14998                      0    support    low  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "main_df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "    <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1405</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12638</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.83</td>\n",
       "      <td>4</td>\n",
       "      <td>227</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>accounting</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9566</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.59</td>\n",
       "      <td>5</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4</td>\n",
       "      <td>160</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9470</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>marketing</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7818</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14944</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5</td>\n",
       "      <td>237</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>marketing</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7625</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>product_mng</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13868</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>product_mng</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "1405                 0.39             0.57               2   \n",
       "12638                0.84             0.83               4   \n",
       "9566                 0.93             0.59               5   \n",
       "582                  0.74             0.87               5   \n",
       "4413                 0.60             0.65               4   \n",
       "9470                 0.75             0.59               3   \n",
       "7818                 0.76             0.90               3   \n",
       "14944                0.83             0.85               5   \n",
       "7625                 0.35             0.40               3   \n",
       "13868                0.73             0.57               3   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "1405                    130                   3              0     1   \n",
       "12638                   227                   5              0     1   \n",
       "9566                    158                   3              0     0   \n",
       "582                     242                   5              0     1   \n",
       "4413                    160                   2              0     0   \n",
       "9470                    242                   3              0     0   \n",
       "7818                    279                   6              0     0   \n",
       "14944                   237                   5              0     1   \n",
       "7625                    130                   3              0     0   \n",
       "13868                   146                   2              0     0   \n",
       "\n",
       "       promotion_last_5years        sales  salary  \n",
       "1405                       0           hr     low  \n",
       "12638                      0   accounting     low  \n",
       "9566                       0        sales     low  \n",
       "582                        0      support  medium  \n",
       "4413                       0           IT     low  \n",
       "9470                       0    marketing  medium  \n",
       "7818                       0           hr  medium  \n",
       "14944                      0    marketing  medium  \n",
       "7625                       0  product_mng     low  \n",
       "13868                      0  product_mng     low  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "\n",
    "main_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical summary of the data\n",
    "main_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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   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": [
    "# understanding the data\n",
    "main_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "sales                     object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the data types\n",
    "main_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the shape  of the dataset\n",
    "main_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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', 'sales', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking columns\n",
    "main_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HR_comma_sep.csv               analyze.html\n",
      "Human Resource Case Study.docx index.ipynb\n",
      "\u001b[34m__pycache__\u001b[m\u001b[m                    model_evaluation_utils.py\n"
     ]
    }
   ],
   "source": [
    "! ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a EDA report\n",
    "#import sweetviz as sv\n",
    "\n",
    "#analyze_report = sv.analyze(df)\n",
    "#analyze_report.show_html('analyze.html', open_browser=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install pandas-profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pandas_profiling\n",
    "#pandas_profiling.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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', 'sales', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the column name sales with occupation\n",
    "main_df.columns = main_df.columns.str.replace('sales', 'occupation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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', 'occupation', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Checking for completeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "occupation               0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "main_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The dataset is complete as it has no missing values</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Checking for consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3008"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "main_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape before dropping duplicates\n",
    "main_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dealing with duplicates\n",
    "main_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11991, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape after dropping duplicates\n",
    "main_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Checking for Uniformity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11991 entries, 0 to 11999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     11991 non-null  float64\n",
      " 1   last_evaluation        11991 non-null  float64\n",
      " 2   number_project         11991 non-null  int64  \n",
      " 3   average_montly_hours   11991 non-null  int64  \n",
      " 4   time_spend_company     11991 non-null  int64  \n",
      " 5   Work_accident          11991 non-null  int64  \n",
      " 6   left                   11991 non-null  int64  \n",
      " 7   promotion_last_5years  11991 non-null  int64  \n",
      " 8   occupation             11991 non-null  object \n",
      " 9   salary                 11991 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking for uniformity\n",
    "main_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Checking for Validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# checking for outliers\n",
    "# function to detect outliers and remove them\n",
    "def check_for_outlier(df,col):\n",
    "    q1 = df[col].quantile(0.25)\n",
    "    q3 = df[col].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower_bound = q1 - (1.5 * iqr)\n",
    "    upper_bound = q3 + (1.5 * iqr)\n",
    "    df = df[(df[col] > lower_bound) & (df[col] < upper_bound)]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11991, 10)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for outliers in the numerical columns and removing them\n",
    "for col in ['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company']:\n",
    "    check_for_outlier(main_df, col)\n",
    "\n",
    "main_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "main_df.boxplot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Dealing with outliers for the following numeric columns</p>\n",
    "<ol>\n",
    "<li>satisfaction level</li>\n",
    "<li>last evaluation</li>\n",
    "<li>average monthly hours</li>\n",
    "<li>time spend company</li>\n",
    "<li>number of projects</li>\n",
    "</ol>\n",
    "<p>Dealing with outliers for categorical columns is not advisable as they are as a result of class imbalance</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "low       5740\n",
       "medium    5261\n",
       "high       990\n",
       "Name: salary, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.salary.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "occupation                object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataset to be used for visualization and analysis\n",
    "visual_df = main_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing visual_df for clear visualization\n",
    "visual_df.left = visual_df.left.map({0:'false', 1:'true'})\n",
    "visual_df.Work_accident = visual_df.Work_accident.map({0:'false', 1:'true'})\n",
    "visual_df.promotion_last_5years = visual_df.promotion_last_5years.map({0:'false', 1:'true'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>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>occupation</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6113</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.98</td>\n",
       "      <td>4</td>\n",
       "      <td>146</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>marketing</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.51</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
       "      <td>true</td>\n",
       "      <td>false</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11135</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.48</td>\n",
       "      <td>5</td>\n",
       "      <td>192</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>technical</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5963</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.55</td>\n",
       "      <td>3</td>\n",
       "      <td>108</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "      <td>false</td>\n",
       "      <td>false</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>3</td>\n",
       "      <td>true</td>\n",
       "      <td>true</td>\n",
       "      <td>false</td>\n",
       "      <td>product_mng</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "6113                 0.70             0.98               4   \n",
       "1649                 0.38             0.51               2   \n",
       "11135                0.56             0.48               5   \n",
       "5963                 0.62             0.55               3   \n",
       "1442                 0.43             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company Work_accident   left  \\\n",
       "6113                    146                   3         false  false   \n",
       "1649                    138                   3         false   true   \n",
       "11135                   192                   2          true  false   \n",
       "5963                    108                   2          true  false   \n",
       "1442                    154                   3          true   true   \n",
       "\n",
       "      promotion_last_5years   occupation  salary  \n",
       "6113                  false    marketing     low  \n",
       "1649                  false    technical     low  \n",
       "11135                 false    technical  medium  \n",
       "5963                  false        sales  medium  \n",
       "1442                  false  product_mng  medium  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using map to dummy encode the salary column\n",
    "main_df.salary = main_df.salary.map({'low':0, 'medium':1, 'high':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using map to dummy encode the occupation column\n",
    "main_df.occupation = main_df.occupation.map({'sales':0, 'accounting':1, 'hr':2, 'technical':3, 'support':4, \n",
    "'management':5, 'IT':6, 'product_mng':7, 'marketing':8, 'RandD':9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>occupation</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10147</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>5</td>\n",
       "      <td>256</td>\n",
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       "      <th>8255</th>\n",
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       "      <th>9139</th>\n",
       "      <td>0.69</td>\n",
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       "      <td>225</td>\n",
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       "      <th>2586</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.74</td>\n",
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       "      <td>221</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3904</th>\n",
       "      <td>0.83</td>\n",
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       "      <th>6170</th>\n",
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       "      <td>1</td>\n",
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       "      <th>5076</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.71</td>\n",
       "      <td>5</td>\n",
       "      <td>222</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5952</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7836</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.71</td>\n",
       "      <td>3</td>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "10147                0.55             0.55               5   \n",
       "8255                 0.51             0.95               4   \n",
       "9139                 0.69             0.68               4   \n",
       "2586                 0.80             0.74               4   \n",
       "3904                 0.83             0.80               3   \n",
       "6170                 0.70             0.72               4   \n",
       "5076                 0.59             0.71               5   \n",
       "5952                 0.93             0.92               5   \n",
       "590                  0.38             0.55               2   \n",
       "7836                 0.76             0.71               3   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "10147                   256                   3              0     0   \n",
       "8255                    169                   3              1     0   \n",
       "9139                    225                   3              0     0   \n",
       "2586                    221                   4              0     0   \n",
       "3904                    273                   3              0     0   \n",
       "6170                    202                   3              0     0   \n",
       "5076                    222                   3              0     0   \n",
       "5952                    189                   2              0     0   \n",
       "590                     148                   3              0     1   \n",
       "7836                    205                   2              0     0   \n",
       "\n",
       "       promotion_last_5years  occupation  salary  \n",
       "10147                      0           9       1  \n",
       "8255                       0           0       0  \n",
       "9139                       0           3       0  \n",
       "2586                       0           7       0  \n",
       "3904                       0           0       0  \n",
       "6170                       0           6       1  \n",
       "5076                       0           3       0  \n",
       "5952                       0           9       0  \n",
       "590                        0           4       0  \n",
       "7836                       0           4       1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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', 'occupation', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "occupation                 int64\n",
       "salary                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking that all the columns are numerical\n",
    "main_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanatory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>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>promotion_last_5years</th>\n",
       "      <th>occupation</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.667365</td>\n",
       "      <td>0.715667</td>\n",
       "      <td>3.786800</td>\n",
       "      <td>198.94270</td>\n",
       "      <td>3.262000</td>\n",
       "      <td>0.174500</td>\n",
       "      <td>0.019500</td>\n",
       "      <td>3.391500</td>\n",
       "      <td>0.637600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440271</td>\n",
       "      <td>0.721783</td>\n",
       "      <td>3.883476</td>\n",
       "      <td>208.16223</td>\n",
       "      <td>3.881467</td>\n",
       "      <td>0.052737</td>\n",
       "      <td>0.004018</td>\n",
       "      <td>3.210447</td>\n",
       "      <td>0.434455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      satisfaction_level  last_evaluation  number_project  \\\n",
       "left                                                        \n",
       "0               0.667365         0.715667        3.786800   \n",
       "1               0.440271         0.721783        3.883476   \n",
       "\n",
       "      average_montly_hours  time_spend_company  Work_accident  \\\n",
       "left                                                            \n",
       "0                198.94270            3.262000       0.174500   \n",
       "1                208.16223            3.881467       0.052737   \n",
       "\n",
       "      promotion_last_5years  occupation    salary  \n",
       "left                                               \n",
       "0                  0.019500    3.391500  0.637600  \n",
       "1                  0.004018    3.210447  0.434455  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The following attributes have impact on whether an employee will leave the comapany or not :</p>\n",
    "<ol>\n",
    "<li>Satisfaction Level</li>\n",
    "<li>Average mothly hours</li>\n",
    "<li>Work Accident</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false    0.833959\n",
       "true     0.166041\n",
       "Name: left, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df['left'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(10,8))\n",
    "sns.countplot(x = visual_df['left'],ax=ax)\n",
    "plt.title('Employees who left the company',fontsize=20)\n",
    "plt.xlabel('Employees who left the company',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "plt.savefig('left.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.478692\n",
       "1    0.438746\n",
       "2    0.082562\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df['salary'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(10,8))\n",
    "sns.countplot(x = visual_df['salary'],ax=ax)\n",
    "plt.title('Salary distribution',fontsize=20)\n",
    "plt.xlabel('Salary',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "#plt.savefig('salary_distribution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Majority of the employees have low salary very few with high salaries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales          3239\n",
       "technical      2244\n",
       "support        1821\n",
       "IT              976\n",
       "RandD           694\n",
       "product_mng     686\n",
       "marketing       673\n",
       "accounting      621\n",
       "hr              601\n",
       "management      436\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df['occupation'].value_counts(normalize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(12,10))\n",
    "sns.countplot(x = visual_df['occupation'], ax=ax)\n",
    "plt.title('Occupation distribution',fontsize=20)\n",
    "plt.xlabel('Occupation',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(rotation=90,fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "#plt.savefig('Occupation distribution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>A majority of people work as sales people </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false    0.983071\n",
       "true     0.016929\n",
       "Name: promotion_last_5years, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df['promotion_last_5years'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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></th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">false</th>\n",
       "      <th>false</th>\n",
       "      <td>9805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true</th>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">true</th>\n",
       "      <th>false</th>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>true</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             promotion_last_5years\n",
       "left  promotion_last_5years                       \n",
       "false false                                   9805\n",
       "      true                                     195\n",
       "true  false                                   1983\n",
       "      true                                       8"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(visual_df['promotion_last_5years'].groupby(visual_df['left']).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(figsize=(10,8))\n",
    "sns.countplot(x = visual_df['promotion_last_5years'],ax=ax)\n",
    "plt.title('Promotion distribution',fontsize=20)\n",
    "plt.xlabel('Promoted',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "#plt.savefig('Promotion distribution.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.307314\n",
       "3    0.293553\n",
       "5    0.186223\n",
       "2    0.131932\n",
       "6    0.068885\n",
       "7    0.012092\n",
       "Name: number_project, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df['number_project'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(10,8))\n",
    "sns.countplot(x = main_df['number_project'],ax=ax)\n",
    "plt.title('Number of projects distribution',fontsize=20)\n",
    "plt.xlabel('Number of projects',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "#plt.savefig('Number of projects distribution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>A majority of employees have worked on four work projects</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false    0.845718\n",
       "true     0.154282\n",
       "Name: Work_accident, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df['Work_accident'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax = plt.subplots(figsize=(10,8))\n",
    "sns.countplot(x = visual_df['Work_accident'],ax=ax)\n",
    "plt.title('Work accident distribution',fontsize=20)\n",
    "plt.xlabel('Work accident',fontsize=15)\n",
    "plt.ylabel('Number of employees',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "#plt.savefig('Work accident distribution.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'satisfaction_level'}>,\n",
       "        <AxesSubplot:title={'center':'last_evaluation'}>,\n",
       "        <AxesSubplot:title={'center':'number_project'}>],\n",
       "       [<AxesSubplot:title={'center':'average_montly_hours'}>,\n",
       "        <AxesSubplot:title={'center':'time_spend_company'}>,\n",
       "        <AxesSubplot:title={'center':'Work_accident'}>],\n",
       "       [<AxesSubplot:title={'center':'left'}>,\n",
       "        <AxesSubplot:title={'center':'promotion_last_5years'}>,\n",
       "        <AxesSubplot:title={'center':'occupation'}>],\n",
       "       [<AxesSubplot:title={'center':'salary'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the distribution of the data\n",
    "main_df.hist(figsize=(15,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "pd.crosstab(visual_df.salary,visual_df.left).plot(kind='bar',ax=ax).legend(['Stayed','Left'])\n",
    "plt.title('Resignation Frequency for Salary Level',fontsize=20)\n",
    "plt.xlabel('Salary',fontsize=15)\n",
    "plt.ylabel('Frequency of resignation',fontsize=15)\n",
    "plt.xticks(rotation=0,fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.savefig('Resignation_frequency_salary.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th>salary</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">false</th>\n",
       "      <th>low</th>\n",
       "      <td>4566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>4492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">true</th>\n",
       "      <th>low</th>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              salary\n",
       "left  salary        \n",
       "false low       4566\n",
       "      medium    4492\n",
       "      high       942\n",
       "true  low       1174\n",
       "      medium     769\n",
       "      high        48"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(visual_df['salary'].groupby(visual_df['left']).value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Employees with high salary are the least likely to quit while those with low salary are the most likely to quit</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the data\n",
    "fig, ax = plt.subplots(figsize=(12, 10))\n",
    "pd.crosstab(visual_df.occupation,visual_df.left).plot(kind='bar',ax=ax).legend(['Stayed','Left'])\n",
    "plt.title('Resignation Frequency for Occupation Level',fontsize=20)\n",
    "plt.xlabel('Occupation',fontsize=16)\n",
    "plt.ylabel('Frequency of resignation',fontsize=16)\n",
    "plt.xticks(rotation=85,fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.savefig('Resignation_Frequency_for_Occupation_Level.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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></th>\n",
       "      <th>occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">false</th>\n",
       "      <th>sales</th>\n",
       "      <td>2689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>1854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>1509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">true</th>\n",
       "      <th>sales</th>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   occupation\n",
       "left  occupation             \n",
       "false sales              2689\n",
       "      technical          1854\n",
       "      support            1509\n",
       "      IT                  818\n",
       "      RandD               609\n",
       "      product_mng         576\n",
       "      marketing           561\n",
       "      accounting          512\n",
       "      hr                  488\n",
       "      management          384\n",
       "true  sales               550\n",
       "      technical           390\n",
       "      support             312\n",
       "      IT                  158\n",
       "      hr                  113\n",
       "      marketing           112\n",
       "      product_mng         110\n",
       "      accounting          109\n",
       "      RandD                85\n",
       "      management           52"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(visual_df['occupation'].groupby(visual_df['left']).value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>coming soon</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "pd.crosstab(visual_df.number_project,visual_df.left).plot(kind='bar',ax=ax).legend(['Stayed','Left'])\n",
    "plt.title('Resignation Frequency per Number of projects',fontsize=20)\n",
    "plt.xlabel('Number of projects',fontsize=15)\n",
    "plt.ylabel('Frequency of resignation',fontsize=15)\n",
    "plt.xticks(rotation=0,fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.savefig('Resignation_frequency_per_number_of_projects.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "pd.crosstab(visual_df.time_spend_company,visual_df.left).plot(kind='bar',ax=ax).legend(['Stayed','Left'])\n",
    "plt.title('Resignation Frequency per Time spend in the company',fontsize=20)\n",
    "plt.xlabel('Time spent in the company',fontsize=15)\n",
    "plt.ylabel('Frequency of resignation',fontsize=15)\n",
    "plt.xticks(rotation=0,fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.savefig('Resignation_frequency_per_time_spent_in_the_company.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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></th>\n",
       "      <th>satisfaction_level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">false</th>\n",
       "      <th>0.50</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.66</th>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.74</th>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.59</th>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.63</th>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">true</th>\n",
       "      <th>0.58</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.64</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.67</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.68</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          satisfaction_level\n",
       "left  satisfaction_level                    \n",
       "false 0.50                               198\n",
       "      0.66                               186\n",
       "      0.74                               186\n",
       "      0.59                               181\n",
       "      0.63                               180\n",
       "...                                      ...\n",
       "true  0.58                                 1\n",
       "      0.64                                 1\n",
       "      0.65                                 1\n",
       "      0.67                                 1\n",
       "      0.68                                 1\n",
       "\n",
       "[170 rows x 1 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(visual_df['satisfaction_level'].groupby(visual_df['left']).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig ,ax = plt.subplots(figsize=(10,8))\n",
    "sns.barplot(x = 'left', y = 'average_montly_hours', data = visual_df,ax=ax)\n",
    "plt.title('Average monthly hours distribution',fontsize=20)\n",
    "plt.xlabel('Left',fontsize=15)\n",
    "plt.ylabel('Average monthly hours',fontsize=15)\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.show()\n",
    "plt.savefig('resignation_per_the_Average_monthly_hours_distribution.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>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>occupation</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>satisfaction_level</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095186</td>\n",
       "      <td>-0.133246</td>\n",
       "      <td>-0.006252</td>\n",
       "      <td>-0.152915</td>\n",
       "      <td>0.039940</td>\n",
       "      <td>-0.350558</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>0.029241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_evaluation</th>\n",
       "      <td>0.095186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.270256</td>\n",
       "      <td>0.264678</td>\n",
       "      <td>0.096829</td>\n",
       "      <td>-0.005695</td>\n",
       "      <td>0.013520</td>\n",
       "      <td>-0.007206</td>\n",
       "      <td>0.009266</td>\n",
       "      <td>-0.015775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_project</th>\n",
       "      <td>-0.133246</td>\n",
       "      <td>0.270256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.331516</td>\n",
       "      <td>0.188837</td>\n",
       "      <td>-0.005612</td>\n",
       "      <td>0.030928</td>\n",
       "      <td>-0.000544</td>\n",
       "      <td>0.005870</td>\n",
       "      <td>-0.000502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_montly_hours</th>\n",
       "      <td>-0.006252</td>\n",
       "      <td>0.264678</td>\n",
       "      <td>0.331516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102875</td>\n",
       "      <td>-0.012860</td>\n",
       "      <td>0.070409</td>\n",
       "      <td>-0.004964</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <td>-0.152915</td>\n",
       "      <td>0.096829</td>\n",
       "      <td>0.188837</td>\n",
       "      <td>0.102875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.173295</td>\n",
       "      <td>0.056828</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>0.038164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <td>0.039940</td>\n",
       "      <td>-0.005695</td>\n",
       "      <td>-0.005612</td>\n",
       "      <td>-0.012860</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.125436</td>\n",
       "      <td>0.029852</td>\n",
       "      <td>0.010891</td>\n",
       "      <td>-0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>-0.350558</td>\n",
       "      <td>0.013520</td>\n",
       "      <td>0.030928</td>\n",
       "      <td>0.070409</td>\n",
       "      <td>0.173295</td>\n",
       "      <td>-0.125436</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044657</td>\n",
       "      <td>-0.023835</td>\n",
       "      <td>-0.118882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <td>0.019789</td>\n",
       "      <td>-0.007206</td>\n",
       "      <td>-0.000544</td>\n",
       "      <td>-0.004964</td>\n",
       "      <td>0.056828</td>\n",
       "      <td>0.029852</td>\n",
       "      <td>-0.044657</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017525</td>\n",
       "      <td>0.087851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <td>0.005716</td>\n",
       "      <td>0.009266</td>\n",
       "      <td>0.005870</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>0.003698</td>\n",
       "      <td>0.010891</td>\n",
       "      <td>-0.023835</td>\n",
       "      <td>0.017525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>0.029241</td>\n",
       "      <td>-0.015775</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>0.038164</td>\n",
       "      <td>-0.000058</td>\n",
       "      <td>-0.118882</td>\n",
       "      <td>0.087851</td>\n",
       "      <td>0.016557</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       satisfaction_level  last_evaluation  number_project  \\\n",
       "satisfaction_level               1.000000         0.095186       -0.133246   \n",
       "last_evaluation                  0.095186         1.000000        0.270256   \n",
       "number_project                  -0.133246         0.270256        1.000000   \n",
       "average_montly_hours            -0.006252         0.264678        0.331516   \n",
       "time_spend_company              -0.152915         0.096829        0.188837   \n",
       "Work_accident                    0.039940        -0.005695       -0.005612   \n",
       "left                            -0.350558         0.013520        0.030928   \n",
       "promotion_last_5years            0.019789        -0.007206       -0.000544   \n",
       "occupation                       0.005716         0.009266        0.005870   \n",
       "salary                           0.029241        -0.015775       -0.000502   \n",
       "\n",
       "                       average_montly_hours  time_spend_company  \\\n",
       "satisfaction_level                -0.006252           -0.152915   \n",
       "last_evaluation                    0.264678            0.096829   \n",
       "number_project                     0.331516            0.188837   \n",
       "average_montly_hours               1.000000            0.102875   \n",
       "time_spend_company                 0.102875            1.000000   \n",
       "Work_accident                     -0.012860            0.000003   \n",
       "left                               0.070409            0.173295   \n",
       "promotion_last_5years             -0.004964            0.056828   \n",
       "occupation                        -0.000095            0.003698   \n",
       "salary                            -0.000551            0.038164   \n",
       "\n",
       "                       Work_accident      left  promotion_last_5years  \\\n",
       "satisfaction_level          0.039940 -0.350558               0.019789   \n",
       "last_evaluation            -0.005695  0.013520              -0.007206   \n",
       "number_project             -0.005612  0.030928              -0.000544   \n",
       "average_montly_hours       -0.012860  0.070409              -0.004964   \n",
       "time_spend_company          0.000003  0.173295               0.056828   \n",
       "Work_accident               1.000000 -0.125436               0.029852   \n",
       "left                       -0.125436  1.000000              -0.044657   \n",
       "promotion_last_5years       0.029852 -0.044657               1.000000   \n",
       "occupation                  0.010891 -0.023835               0.017525   \n",
       "salary                     -0.000058 -0.118882               0.087851   \n",
       "\n",
       "                       occupation    salary  \n",
       "satisfaction_level       0.005716  0.029241  \n",
       "last_evaluation          0.009266 -0.015775  \n",
       "number_project           0.005870 -0.000502  \n",
       "average_montly_hours    -0.000095 -0.000551  \n",
       "time_spend_company       0.003698  0.038164  \n",
       "Work_accident            0.010891 -0.000058  \n",
       "left                    -0.023835 -0.118882  \n",
       "promotion_last_5years    0.017525  0.087851  \n",
       "occupation               1.000000  0.016557  \n",
       "salary                   0.016557  1.000000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "\n",
    "corr = main_df.corr()\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "vf, ax = plt.subplots(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr, mask=mask, vmax=.3, center=0,annot=True,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.show()\n",
    "plt.savefig('Correlation.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This correlation matrix tells us that the higher someone is satisfied in their job the less likely they are to leave.\n",
    "<ul><li>Time spent with the company is positively correlated with leaving, \n",
    "suggesting the longer someone works for this organization the more likely they are too leave.</li>\n",
    "<li>Another relationship that makes sense is that a higher salary appears to be negatively associated with leaving.</li>\n",
    "\n",
    "<li>Having had a work accident is negatively related to leaving the company, which doesn't really make intuitive sense. \n",
    "On the surface this doesn't make a whole lot of sense \n",
    "because you would assume that an employee felt unsafe at an organization that they might want to leave.</li>\n",
    "</ul></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11991 entries, 0 to 11999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     11991 non-null  float64\n",
      " 1   last_evaluation        11991 non-null  float64\n",
      " 2   number_project         11991 non-null  int64  \n",
      " 3   average_montly_hours   11991 non-null  int64  \n",
      " 4   time_spend_company     11991 non-null  int64  \n",
      " 5   Work_accident          11991 non-null  int64  \n",
      " 6   left                   11991 non-null  int64  \n",
      " 7   promotion_last_5years  11991 non-null  int64  \n",
      " 8   occupation             11991 non-null  int64  \n",
      " 9   salary                 11991 non-null  int64  \n",
      "dtypes: float64(2), int64(8)\n",
      "memory usage: 1.3 MB\n"
     ]
    }
   ],
   "source": [
    "main_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 7) (20000,)\n"
     ]
    }
   ],
   "source": [
    "# feature scaling\n",
    "scaler = StandardScaler()\n",
    "main_df[['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company','occupation']] = scaler.fit_transform(main_df[['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company','occupation']])\n",
    "main_df.sample(5)\n",
    "\n",
    "# dealing with class imbalance with SMOTE\n",
    "# implementing the SMOTE algorithm to handle unbalanced data\n",
    "sm = SMOTE(random_state = 1, sampling_strategy='minority')\n",
    "X = main_df.drop(['left','Work_accident','promotion_last_5years'],axis=1)\n",
    "y = main_df['left']\n",
    "X,y = sm.fit_resample(X,y)\n",
    "print(X.shape,y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data into train and test\n",
    "X = main_df.drop('left', axis=1)\n",
    "y = main_df['left']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a function to fit and evaluate the models\n",
    "def fit_and_evaluate(model):\n",
    "    # fit the model\n",
    "    model.fit(X_train, y_train)\n",
    "    # predict the values\n",
    "    y_pred = model.predict(X_test)\n",
    "    # evaluate the model\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    confusion = confusion_matrix(y_test, y_pred)\n",
    "    classification = classification_report(y_test, y_pred)\n",
    "    # return the values\n",
    "    return print('Accuracy: ',accuracy,'\\n\\n','Confusion Matrix: \\n [[TP , FP] \\n [FN , TN]]\\n\\n',confusion,'\\n\\n','Classification Report:',classification)\n",
    "    \n",
    "    \n",
    "\n",
    "# creating a function to cross validate the models\n",
    "def cross_validate(model):\n",
    "    # cross validate the model\n",
    "    score = cross_val_score(model, X_train, y_train, cv=5)\n",
    "    # return the values\n",
    "    return score\n",
    "\n",
    "# creating a function to tune the hyperparameters\n",
    "def hyperparameter_tuning(model, params):\n",
    "    # tune the hyperparameters\n",
    "    grid = GridSearchCV(model, params, cv=5)\n",
    "    grid.fit(X_train, y_train)\n",
    "    # return the values\n",
    "    return grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8322214809873248 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2415   86]\n",
      " [ 417   80]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.97      0.91      2501\n",
      "           1       0.48      0.16      0.24       497\n",
      "\n",
      "    accuracy                           0.83      2998\n",
      "   macro avg       0.67      0.56      0.57      2998\n",
      "weighted avg       0.79      0.83      0.80      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a LogisticRegression with random_state=42\n",
    "baseline_model = LogisticRegression(random_state = 42)\n",
    "fit_and_evaluate(baseline_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82490272, 0.82712618, 0.82879377, 0.82647386, 0.83926585])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(baseline_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_model_params = {\n",
    "    \"solver\": ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "    \"penalty\": ['l1', 'l2', 'elasticnet', 'none'],\n",
    "    \"C\": np.logspace(-4, 4, 20),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.0001, 'penalty': 'l2', 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparameter_tuning(baseline_model, params=baseline_model_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8462308205470314 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2476   25]\n",
      " [ 436   61]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.99      0.91      2501\n",
      "           1       0.71      0.12      0.21       497\n",
      "\n",
      "    accuracy                           0.85      2998\n",
      "   macro avg       0.78      0.56      0.56      2998\n",
      "weighted avg       0.83      0.85      0.80      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_tuned_model = LogisticRegression(C=0.0001, penalty='l2', solver='liblinear')\n",
    "fit_and_evaluate(log_tuned_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.84602557, 0.84991662, 0.84991662, 0.84760845, 0.85205784])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(log_tuned_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> After tuning the baseline model the overall accuracy increased from 83% to 85%, however ,the model still does not perform to satisfactory level on the minority class </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9653102068045364 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2441   60]\n",
      " [  44  453]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98      2501\n",
      "           1       0.88      0.91      0.90       497\n",
      "\n",
      "    accuracy                           0.97      2998\n",
      "   macro avg       0.93      0.94      0.94      2998\n",
      "weighted avg       0.97      0.97      0.97      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate a decision tree model\n",
    "tree_model = DecisionTreeClassifier(random_state = 42)\n",
    "fit_and_evaluate(tree_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96942746, 0.96831573, 0.96775987, 0.96829811, 0.96941046])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(tree_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model_params = {\n",
    "    \"criterion\": ['gini', 'entropy'],\n",
    "    \"splitter\": ['best', 'random'],\n",
    "    \"max_depth\": [int(x) for x in np.linspace(10, 110, num = 11)],\n",
    "    \"min_samples_split\": [2, 5, 10],\n",
    "    \"min_samples_leaf\": [1, 2, 4],\n",
    "    \"max_features\": ['auto', 'sqrt', 'log2']\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 5,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparameter_tuning(tree_model, params=tree_model_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9799866577718479 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2484   17]\n",
      " [  43  454]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99      2501\n",
      "           1       0.96      0.91      0.94       497\n",
      "\n",
      "    accuracy                           0.98      2998\n",
      "   macro avg       0.97      0.95      0.96      2998\n",
      "weighted avg       0.98      0.98      0.98      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tree_tuned_model = DecisionTreeClassifier(criterion='entropy', max_depth=10, max_features='auto', min_samples_leaf=1, min_samples_split=5, splitter='best')\n",
    "fit_and_evaluate(tree_tuned_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97554197, 0.97387437, 0.97720956, 0.97719689, 0.98275862])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(tree_tuned_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> The Accuracy metrics increased from 97% on the base tree model to 98% on the tuned tree model</p>\n",
    "<p> The decision tree classifier performs relatively well on its  base model and performs even better after Hyperparameter tuning with both the minority and majority class with precision, recall metrics all above 0.9.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9829886591060707 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2494    7]\n",
      " [  44  453]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      2501\n",
      "           1       0.98      0.91      0.95       497\n",
      "\n",
      "    accuracy                           0.98      2998\n",
      "   macro avg       0.98      0.95      0.97      2998\n",
      "weighted avg       0.98      0.98      0.98      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# instantiate a Random Forest model\n",
    "forest_model = RandomForestClassifier(random_state = 42)\n",
    "fit_and_evaluate(forest_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98554753, 0.98332407, 0.98499166, 0.98665184, 0.98720801])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(forest_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_params = {\n",
    "    \"n_estimators\": [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000],\n",
    "    \"max_features\": ['auto', 'sqrt'],\n",
    "    \"max_depth\": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],\n",
    "    \"min_samples_split\": [2, 5, 10],\n",
    "    \"min_samples_leaf\": [1, 2, 4],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyperparameter_tuning(forest_model, params=forest_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9829886591060707 \n",
      "\n",
      " Confusion Matrix: \n",
      " [[TP , FP] \n",
      " [FN , TN]]\n",
      "\n",
      " [[2494    7]\n",
      " [  44  453]] \n",
      "\n",
      " Classification Report:               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99      2501\n",
      "           1       0.98      0.91      0.95       497\n",
      "\n",
      "    accuracy                           0.98      2998\n",
      "   macro avg       0.98      0.95      0.97      2998\n",
      "weighted avg       0.98      0.98      0.98      2998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "forest_tuned_model = RandomForestClassifier(n_estimators=1000, min_samples_split=5, min_samples_leaf=1, max_features='auto', max_depth=70, bootstrap=True)\n",
    "fit_and_evaluate(forest_tuned_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98499166, 0.98332407, 0.98554753, 0.98553949, 0.98720801])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(forest_tuned_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>  The Accuracy metrics increased from 98% on the base forest model to 99% on the tuned forest model</p>\n",
    "<p></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions on unseen data\n",
    "def Make_prediction():\n",
    "    print('Enter the following details to predict the employee retention')\n",
    "    satisfaction_level = float(input('Enter the satisfaction level: range from 0 to 1: '))\n",
    "    last_evaluation = float(input('Enter how the employee feels about the last evaluation: rate between 0 and 1: '))\n",
    "    number_project = int(input('Enter the number of projects employee has worked on: '))\n",
    "    average_montly_hours = int(input('Enter the employees average monthly hours: '))\n",
    "    time_spend_company = int(input('Enter the employees time spend in the company: '))\n",
    "    Work_accident = int(input('Enter the work accident: 0 for No and 1 for Yes: '))\n",
    "    promotion_last_5years = int(input('Enter if employee has had a promotion in the last 5 years: 0: No, 1: Yes '))\n",
    "    salary = input('Enter the salary as high/medium/low: ')\n",
    "    occupation = int(input(\"Enter the occupation 'sales':0, 'accounting':1, 'hr':2, 'technical':3, 'support':4, 'management':5, 'IT':6, 'product_mng':7, 'marketing':8, 'RandD':9\"))\n",
    "    # creating a dictionary\n",
    "    data = {\n",
    "        'satisfaction_level': satisfaction_level,\n",
    "        'last_evaluation': last_evaluation,\n",
    "        'number_project': number_project,\n",
    "        'average_montly_hours': average_montly_hours,\n",
    "        'time_spend_company': time_spend_company,\n",
    "        'Work_accident': Work_accident,\n",
    "        'promotion_last_5years': promotion_last_5years,\n",
    "        'salary': salary,\n",
    "        'occupation': occupation\n",
    "    }\n",
    "    # creating a dataframe\n",
    "    df = pd.DataFrame(data, index=[0])\n",
    "    # encoding the salary column\n",
    "    df['salary'] = df['salary'].map({'low':0, 'medium':1, 'high':2})\n",
    "    # feature scaling\n",
    "    df[['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company','occupation']] = scaler.transform(df[['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company','occupation']])\n",
    "    # predicting the values\n",
    "    prediction = forest_tuned_model.predict(df)\n",
    "    # return the values\n",
    "    return print('The employee will leave the company: (0 for False and 1 for True):  ',prediction[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the following details to predict the employee retention\n",
      "The employee will leave the company: (0 for False and 1 for True):   1\n"
     ]
    }
   ],
   "source": [
    "Make_prediction()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Giving the model an employees input the model was able to predict that the employee would quit the organisation by returning 0 which represents true on an employee leaving the organisation</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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