HR Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "214ac120-02fc-4c9d-8bf0-2c44b892dc50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5941239b-c80d-443b-a460-07d5424d3186",
   "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>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"HR_comma_sep.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "11d45a91-e84a-4709-97b5-6385760a0f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Visualization & Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7bf9d18-fbec-4f6b-aeac-18d621d42f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = df[df.left==1]\n",
    "left.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "046f885a-4887-4093-bbab-dd7bdb835d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retained = df[df.left==0]\n",
    "retained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1a5ad5-6811-449d-852b-8ff9fcac32ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average Number for all columns\n",
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4f274a14-3911-4983-ab13-e157985d99f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Impact of salary on the retention of employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e21f2f4-4470-47df-8d0c-13b1e2e00c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11f86998-d570-46a3-86a5-75136a02bd99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Department wise employee retention rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c90760e-6d59-42f9-b357-6f7a5ceec549",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Department'>"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "id": "450adb71-2878-471c-9e8a-182994f92a14",
   "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>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  salary\n",
       "0                0.38                   157                      0     low\n",
       "1                0.80                   262                      0  medium\n",
       "2                0.11                   272                      0  medium\n",
       "3                0.72                   223                      0     low\n",
       "4                0.37                   159                      0     low"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will create a sub-dataframe with the following independent variables \n",
    "subdf = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\n",
    "subdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd7f8887-e3be-4ddd-a129-2ca5d4a77f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_dummies = pd.get_dummies(subdf.salary, prefix = \"salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ddfa676a-8fd3-47ca-bea6-e41010962eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummies = pd.concat([subdf, salary_dummies], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ae70aef-752c-4d76-8b5e-685cc5e3c423",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummies.drop('salary', axis='columns', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7beb47d2-853d-484a-81c7-be6b64cf9bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
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       "    </tr>\n",
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       "      <td>0.80</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38                   157                      0   \n",
       "1                0.80                   262                      0   \n",
       "2                0.11                   272                      0   \n",
       "3                0.72                   223                      0   \n",
       "4                0.37                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0        False        True          False  \n",
       "1        False       False           True  \n",
       "2        False       False           True  \n",
       "3        False        True          False  \n",
       "4        False        True          False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b104cfa4-9ea7-4593-afb9-2dbb1e25dd9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38                   157                      0   \n",
       "1                0.80                   262                      0   \n",
       "2                0.11                   272                      0   \n",
       "3                0.72                   223                      0   \n",
       "4                0.37                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0        False        True          False  \n",
       "1        False       False           True  \n",
       "2        False       False           True  \n",
       "3        False        True          False  \n",
       "4        False        True          False  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_with_dummies\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f0161bb7-1a3e-4e11-b70d-481e94849eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.left\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c3f4fbf2-ae93-4c6c-a70f-58acefae67b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "40f5d31a-7345-4f94-8886-e9d3349f0579",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dd5c9456-dbaf-4363-97cd-0fbeced861f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39459c7c-14bc-4f7d-a101-4dbcfe5631b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "45b02b3d-86c9-4493-ab6c-de9e745a4e09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91fcb691-f0fe-435c-a67c-0b6f01980827",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93a4cf5e-ad21-4e8d-9153-88152ccf3181",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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