model_for_HR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>low</td>\n",
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       "      <td>0.37</td>\n",
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       "      <td>2</td>\n",
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       "      <td>low</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "      <td>0.37</td>\n",
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       "      <td>2</td>\n",
       "      <td>158</td>\n",
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       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "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",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "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 Department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \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  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"HR_analytics.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retained=df[df.left==0]\n",
    "retained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leave=df[df.left==1]\n",
    "leave.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2380825388359224"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.left.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\utkarsh gupta\\AppData\\Local\\Temp\\ipykernel_6896\\588011459.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df.groupby('left').mean()\n"
     ]
    },
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       "  <thead>\n",
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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",
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       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
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       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.005321</td>\n",
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      "text/plain": [
       "      satisfaction_level  last_evaluation  number_project  \\\n",
       "left                                                        \n",
       "0               0.666810         0.715473        3.786664   \n",
       "1               0.440098         0.718113        3.855503   \n",
       "\n",
       "      average_montly_hours  time_spend_company  Work_accident  \\\n",
       "left                                                            \n",
       "0               199.060203            3.380032       0.175009   \n",
       "1               207.419210            3.876505       0.047326   \n",
       "\n",
       "      promotion_last_5years  \n",
       "left                         \n",
       "0                  0.026251  \n",
       "1                  0.005321  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>1155</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>5144</td>\n",
       "      <td>2172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>5129</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "left       0     1\n",
       "salary            \n",
       "high    1155    82\n",
       "low     5144  2172\n",
       "medium  5129  1317"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.salary,df.left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='salary'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>left</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>954</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>666</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>563</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>524</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>539</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>655</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>704</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>3126</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>1674</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>2023</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "left            0     1\n",
       "Department             \n",
       "IT            954   273\n",
       "RandD         666   121\n",
       "accounting    563   204\n",
       "hr            524   215\n",
       "management    539    91\n",
       "marketing     655   203\n",
       "product_mng   704   198\n",
       "sales        3126  1014\n",
       "support      1674   555\n",
       "technical    2023   697"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.Department,df.left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Department'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Department,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}