ATTRITION_ANALYSIS_CODE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c08db5fa-94b6-406d-b856-bfdfacbf03ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "52fca75f-04c4-4145-a906-347162cb05c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv('Attrition data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e41922e6-2b9b-457c-ad3c-6d28a8719d45",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>Gender</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>No</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeID  Age Attrition     BusinessTravel              Department  \\\n",
       "0           1   51        No      Travel_Rarely                   Sales   \n",
       "1           2   31       Yes  Travel_Frequently  Research & Development   \n",
       "2           3   32        No  Travel_Frequently  Research & Development   \n",
       "3           4   38        No         Non-Travel  Research & Development   \n",
       "4           5   32        No      Travel_Rarely  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  Gender  ...  \\\n",
       "0                 6          2  Life Sciences              1  Female  ...   \n",
       "1                10          1  Life Sciences              1  Female  ...   \n",
       "2                17          4          Other              1    Male  ...   \n",
       "3                 2          5  Life Sciences              1    Male  ...   \n",
       "4                10          1        Medical              1    Male  ...   \n",
       "\n",
       "   TotalWorkingYears TrainingTimesLastYear YearsAtCompany  \\\n",
       "0                1.0                     6              1   \n",
       "1                6.0                     3              5   \n",
       "2                5.0                     2              5   \n",
       "3               13.0                     5              8   \n",
       "4                9.0                     2              6   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager EnvironmentSatisfaction  \\\n",
       "0                        0                     0                     3.0   \n",
       "1                        1                     4                     3.0   \n",
       "2                        0                     3                     2.0   \n",
       "3                        7                     5                     4.0   \n",
       "4                        0                     4                     4.0   \n",
       "\n",
       "   JobSatisfaction  WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "0              4.0              2.0               3                  3  \n",
       "1              2.0              4.0               2                  4  \n",
       "2              2.0              1.0               3                  3  \n",
       "3              4.0              3.0               2                  3  \n",
       "4              1.0              3.0               3                  3  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8daf6169-73af-45fc-881d-04804792261c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AGE DISTRIBUTION OF EMPLOYEES\n",
    "plt.hist(data['Age'],edgecolor='white',bins=20)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Range of employees\")\n",
    "plt.title(\"Age Distribution of Employees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "38f5302a-0f1a-40aa-94cd-cc2b714c2898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age\n",
       "35    234\n",
       "34    231\n",
       "31    207\n",
       "36    207\n",
       "29    204\n",
       "32    183\n",
       "30    180\n",
       "33    174\n",
       "38    174\n",
       "40    171\n",
       "37    150\n",
       "27    144\n",
       "28    144\n",
       "42    138\n",
       "39    126\n",
       "45    123\n",
       "41    120\n",
       "26    117\n",
       "46     99\n",
       "44     99\n",
       "43     96\n",
       "50     90\n",
       "25     78\n",
       "24     78\n",
       "47     72\n",
       "49     72\n",
       "55     66\n",
       "51     57\n",
       "48     57\n",
       "53     57\n",
       "54     54\n",
       "52     54\n",
       "22     48\n",
       "58     42\n",
       "23     42\n",
       "56     42\n",
       "21     39\n",
       "20     33\n",
       "59     30\n",
       "19     27\n",
       "18     24\n",
       "60     15\n",
       "57     12\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b2cc02b5-1a6f-422f-beae-197f9231b0bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXPLORE ATTRITION BY AGE\n",
    "plt.scatter(data.Attrition,data.Age)\n",
    "plt.ylabel(\"Age\")\n",
    "plt.xlabel(\"Attrition\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6d016997-efbd-471a-b742-981abac2501b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data['Attrition'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d049816d-f39d-42cc-9bad-c71874cee7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ccfeb3db-df5e-48de-b79a-b4dc93e5e267",
   "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>Attrition</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>3699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition  count\n",
       "0        No   3699\n",
       "1       Yes    711"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "18b195a1-9e1c-477e-8d65-c1f23a985dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AgKyIGwAgK+IGAMiKuAEAstJm4uaSSy6JioqKmDt3btH9Dz74YFRUVJRpKgCgvWkzcRMR0bVr17j55pvj9ddfL/coAEA71abipq6uLgYMGBBz5sw55D4/+tGP4tRTT43q6uoYOnRo3HLLLUdxQgCgrWtTcVNZWRlf//rXY968ebF58+YDtq9cuTIuuuii+PSnPx3PPfdc3HjjjXHDDTfE/PnzD/mYe/fujYaGhqIbAJCvNhU3ERGf/OQn44wzzohZs2YdsO3WW2+N8ePHxw033BAnnnhiXHLJJXHVVVfFN7/5zUM+3pw5c6JXr16FW21tbWuODwCUWZuLm4iIm2++Of7hH/4h1q5dW3T/2rVr4+yzzy667+yzz44XX3wxGhsbD/pYM2fOjF27dhVuL7/8cqvNDQCUX5uMm3POOScmTJgQM2fOPOLHqq6ujpqamqIbAJCvzuUe4FDmzp0bZ5xxRowcObJw38knnxxLly4t2m/p0qVx4oknRmVl5dEeEQBog9ps3IwePTqmTp0a3/72twv3zZgxIz784Q/H1772tZgyZUosW7Ys7rjjjrjzzjvLOCkA0Ja0yY+l9rvpppuiqamp8Oezzjor7rvvvliwYEGMGjUqvvrVr8ZNN90Ul1xySfmGBADalIqUUir3EEdTQ0PDu1dNffG+6FTdvdzjAEBWXpp7Xqs87v7X7127dr3n+bNt+p0bAIBSiRsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDISudyD1Au9bMnRE1NTbnHAABamHduAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCyIm4AgKyIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCy0rncAxxtKaWIiGhoaCjzJABAc+1/3d7/On44HS5utm/fHhERtbW1ZZ4EACjV7t27o1evXofdp8PFzbHHHhsREZs2bXrPv5wcNTQ0RG1tbbz88stRU1NT7nGOOuu3fuu3/o64/hzWnlKK3bt3x6BBg95z3w4XN506vXuaUa9evdrtAW4JNTU11m/95R6jbKzf+jvq+tv72pv7poQTigGArIgbACArHS5uqqurY9asWVFdXV3uUcrC+q3f+q3f+jve+jva2itSc66pAgBoJzrcOzcAQN7EDQCQFXEDAGRF3AAAWelwcfOd73wnhg4dGl27do2xY8fGz3/+83KPdMRuvPHGqKioKLqddNJJhe1vvfVWXHnllfGBD3wgevToEX/+538er776atFjbNq0Kc4777zo3r179OvXL6655pp45513jvZSmmXJkiUxefLkGDRoUFRUVMSDDz5YtD2lFF/96ldj4MCB0a1bt6irq4sXX3yxaJ8dO3bE1KlTo6amJnr37h2f+9znYs+ePUX7rFmzJj7+8Y9H165do7a2Nr7xjW+09tKa5b3Wf8kllxzwfJg4cWLRPu11/XPmzIkPf/jD0bNnz+jXr19ceOGFsW7duqJ9Wur5vnjx4jjrrLOiuro6RowYEfPnz2/t5b2n5qz/j//4jw84/l/4wheK9mmv6//ud78bp512WuEH0Y0bNy4efvjhwvacj33Ee68/52NfstSBLFiwIFVVVaW///u/T88//3y67LLLUu/evdOrr75a7tGOyKxZs9Kpp56atmzZUrj95je/KWz/whe+kGpra9PChQvTihUr0kc/+tH0sY99rLD9nXfeSaNGjUp1dXVp1apV6Sc/+Unq27dvmjlzZjmW855+8pOfpOuvvz7df//9KSLSAw88ULR97ty5qVevXunBBx9Mzz77bDr//PPTsGHD0ptvvlnYZ+LEien0009PTz/9dHryySfTiBEj0sUXX1zYvmvXrtS/f/80derUVF9fn+69997UrVu3dNdddx2tZR7Se61/2rRpaeLEiUXPhx07dhTt017XP2HChHTPPfek+vr6tHr16vRnf/ZnafDgwWnPnj2FfVri+f6rX/0qde/ePV199dXphRdeSPPmzUuVlZXpkUceOarr/X3NWf8f/dEfpcsuu6zo+O/atauwvT2v/6GHHkr/9V//lX75y1+mdevWpeuuuy516dIl1dfXp5TyPvYpvff6cz72pepQcfORj3wkXXnllYU/NzY2pkGDBqU5c+aUcaojN2vWrHT66acfdNvOnTtTly5d0r//+78X7lu7dm2KiLRs2bKU0rsvlp06dUpbt24t7PPd73431dTUpL1797bq7Efq91/cm5qa0oABA9I3v/nNwn07d+5M1dXV6d57700ppfTCCy+kiEjPPPNMYZ+HH344VVRUpF//+tcppZTuvPPO1KdPn6L1X3vttWnkyJGtvKLSHCpuLrjggkN+T07r37ZtW4qI9MQTT6SUWu75/uUvfzmdeuqpRf+tKVOmpAkTJrT2kkry++tP6d0XuL/5m7855PfktP6UUurTp0/6/ve/3+GO/X77159Sxzv2h9NhPpZ6++23Y+XKlVFXV1e4r1OnTlFXVxfLli0r42Qt48UXX4xBgwbF8OHDY+rUqbFp06aIiFi5cmXs27evaN0nnXRSDB48uLDuZcuWxejRo6N///6FfSZMmBANDQ3x/PPPH92FHKGNGzfG1q1bi9bbq1evGDt2bNF6e/fuHX/wB39Q2Keuri46deoUy5cvL+xzzjnnRFVVVWGfCRMmxLp16+L1118/Sqt5/xYvXhz9+vWLkSNHxhVXXBHbt28vbMtp/bt27YqI//+FuC31fF+2bFnRY+zfp639W/H769/vX/7lX6Jv374xatSomDlzZrzxxhuFbbmsv7GxMRYsWBC//e1vY9y4cR3u2P/++vfrCMe+OTrML8587bXXorGxseigRkT0798//ud//qdMU7WMsWPHxvz582PkyJGxZcuWmD17dnz84x+P+vr62Lp1a1RVVUXv3r2Lvqd///6xdevWiIjYunXrQf9e9m9rT/bPe7D1/O56+/XrV7S9c+fOceyxxxbtM2zYsAMeY/+2Pn36tMr8LWHixInxqU99KoYNGxYbNmyI6667LiZNmhTLli2LysrKbNbf1NQUX/ziF+Pss8+OUaNGRUS02PP9UPs0NDTEm2++Gd26dWuNJZXkYOuPiPjLv/zLGDJkSAwaNCjWrFkT1157baxbty7uv//+iGj/63/uuedi3Lhx8dZbb0WPHj3igQceiFNOOSVWr17dIY79odYfkf+xL0WHiZucTZo0qfD1aaedFmPHjo0hQ4bEfffd126eiLScT3/604WvR48eHaeddlp86EMfisWLF8f48ePLOFnLuvLKK6O+vj6eeuqpco9SFoda/+WXX174evTo0TFw4MAYP358bNiwIT70oQ8d7TFb3MiRI2P16tWxa9eu+OEPfxjTpk2LJ554otxjHTWHWv8pp5yS/bEvRYf5WKpv375RWVl5wJnzr776agwYMKBMU7WO3r17x4knnhjr16+PAQMGxNtvvx07d+4s2ud31z1gwICD/r3s39ae7J/3cMd5wIABsW3btqLt77zzTuzYsSPLv5Phw4dH3759Y/369RGRx/qvuuqq+M///M9YtGhRHH/88YX7W+r5fqh9ampq2sT/MBxq/QczduzYiIii49+e119VVRUjRoyIMWPGxJw5c+L000+P22+/vcMc+0Ot/2ByO/al6DBxU1VVFWPGjImFCxcW7mtqaoqFCxcWfV6Zgz179sSGDRti4MCBMWbMmOjSpUvRutetWxebNm0qrHvcuHHx3HPPFb3gPfbYY1FTU1N4u7O9GDZsWAwYMKBovQ0NDbF8+fKi9e7cuTNWrlxZ2Ofxxx+Ppqamwj8G48aNiyVLlsS+ffsK+zz22GMxcuTINvGRTCk2b94c27dvj4EDB0ZE+15/SimuuuqqeOCBB+Lxxx8/4KOzlnq+jxs3rugx9u9T7n8r3mv9B7N69eqIiKLj317XfzBNTU2xd+/e7I/9oexf/8HkfuwPq9xnNB9NCxYsSNXV1Wn+/PnphRdeSJdffnnq3bt30Znj7dGMGTPS4sWL08aNG9PSpUtTXV1d6tu3b9q2bVtK6d3LIwcPHpwef/zxtGLFijRu3Lg0bty4wvfvvzzw3HPPTatXr06PPPJIOu6449rspeC7d+9Oq1atSqtWrUoRkW699da0atWq9L//+78ppXcvBe/du3f68Y9/nNasWZMuuOCCg14KfuaZZ6bly5enp556Kp1wwglFl0Lv3Lkz9e/fP33mM59J9fX1acGCBal79+5lvxQ6pcOvf/fu3elLX/pSWrZsWdq4cWP62c9+ls4666x0wgknpLfeeqvwGO11/VdccUXq1atXWrx4cdHlrm+88UZhn5Z4vu+/HPaaa65Ja9euTd/5znfaxOWw77X+9evXp5tuuimtWLEibdy4Mf34xz9Ow4cPT+ecc07hMdrz+r/yla+kJ554Im3cuDGtWbMmfeUrX0kVFRXppz/9aUop72Of0uHXn/uxL1WHipuUUpo3b14aPHhwqqqqSh/5yEfS008/Xe6RjtiUKVPSwIEDU1VVVfrgBz+YpkyZktavX1/Y/uabb6a//uu/Tn369Endu3dPn/zkJ9OWLVuKHuOll15KkyZNSt26dUt9+/ZNM2bMSPv27TvaS2mWRYsWpYg44DZt2rSU0ruXg99www2pf//+qbq6Oo0fPz6tW7eu6DG2b9+eLr744tSjR49UU1OTPvvZz6bdu3cX7fPss8+mP/zDP0zV1dXpgx/8YJo7d+7RWuJhHW79b7zxRjr33HPTcccdl7p06ZKGDBmSLrvssgMCvr2u/2Drjoh0zz33FPZpqef7okWL0hlnnJGqqqrS8OHDi/4b5fJe69+0aVM655xz0rHHHpuqq6vTiBEj0jXXXFP0s05Sar/rv/TSS9OQIUNSVVVVOu6449L48eMLYZNS3sc+pcOvP/djX6qKlFI6eu8TAQC0rg5zzg0A0DGIGwAgK+IGAMiKuAEAsiJuAICsiBsAICviBgDIirgBALIibgCArIgbACAr4gYAyIq4AQCy8n+K5jQ9Pcyr6QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using bar to show attrition\n",
    "data['Attrition'].value_counts().plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fc6dbc8e-4ad7-4915-a027-e3d5159661f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(df['Attrition'],df['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9649a210-60c8-4152-b1dc-15c7f6d73481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EducationField\n",
       "Life Sciences       1818\n",
       "Medical             1392\n",
       "Marketing            477\n",
       "Technical Degree     396\n",
       "Other                246\n",
       "Human Resources       81\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of employees by eduction field\n",
    "data['EducationField'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "97756c47-03be-45e8-b7f3-7a8ec38d9cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#representing marital status using bar charty\n",
    "df2=pd.DataFrame(data['MaritalStatus'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "12e0078c-8cad-4d8c-b7e1-584bf3c1b701",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "36d3c3cb-612f-4d78-81b6-6f214d748759",
   "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>MaritalStatus</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Married</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Single</td>\n",
       "      <td>1410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divorced</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MaritalStatus  count\n",
       "0       Married   2019\n",
       "1        Single   1410\n",
       "2      Divorced    981"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "27264217-5cd1-48e4-8040-e1de4bb92af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df2['MaritalStatus'],df2['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7e1ae03b-a82f-4a13-b22b-431dd211f74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#representing marital status using bar charty\n",
    "df3=pd.DataFrame(data['BusinessTravel'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1545f678-6892-4cf2-8c6b-3eb8f3d09334",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df3.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2f60803f-12b4-45e6-8270-cea3327977fe",
   "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>BusinessTravel</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>3129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      BusinessTravel  count\n",
       "0      Travel_Rarely   3129\n",
       "1  Travel_Frequently    831\n",
       "2         Non-Travel    450"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "10f52fe6-c34b-452b-9bb9-260e63bd30aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(df3['BusinessTravel'],df3['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "374d1383-d33c-487f-8767-e3eeeffb1c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.DataFrame(data['JobRole'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "69180d18-5b16-461f-8794-953eb5998d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=df4.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b4022249-5d8a-4423-878e-2a9eca2720d8",
   "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>JobRole</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manager</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Research Director</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Human Resources</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     JobRole  count\n",
       "0            Sales Executive    978\n",
       "1         Research Scientist    876\n",
       "2      Laboratory Technician    777\n",
       "3     Manufacturing Director    435\n",
       "4  Healthcare Representative    393\n",
       "5                    Manager    306\n",
       "6       Sales Representative    249\n",
       "7          Research Director    240\n",
       "8            Human Resources    156"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "b5a29e42-49a7-4acf-87a8-6b1aebb80041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize attrition by department\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "attrition_by_department = data[data['Attrition'] == 1]['Department'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=attrition_by_department.index, y=attrition_by_department.values, hue=attrition_by_department.index, palette='viridis', dodge=False, legend=False)\n",
    "plt.title('Number of Employees Who Left by Department')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Number of Employees Who Left')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "898bf7ef-5374-4725-84a7-362d6f478c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming 'data' is your DataFrame and it contains 'JobRole' and 'Attrition' columns\n",
    "# Calculate the number of employees who left in each job role\n",
    "attrition_by_job_role = data[data['Attrition'] == 1]['JobRole'].value_counts()\n",
    "\n",
    "# Plot the bar graph\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=attrition_by_job_role.index, y=attrition_by_job_role.values, hue=attrition_by_job_role.index, palette='viridis', dodge=False, legend=False)\n",
    "plt.title('Number of Employees Who Left by Job Role')\n",
    "plt.xlabel('Job Role')\n",
    "plt.ylabel('Number of Employees Who Left')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2d66a319-a96a-40b3-8b37-da4c491b3a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BUILDING A LOGISTIC REGRESSION MODEL TO PREDICT WHICH EMPLOYEES ARE LIKELY FOR ATTRITION\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b34e6a62-69c8-465d-b28f-c264221ef137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4410 entries, 0 to 4409\n",
      "Data columns (total 29 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   EmployeeID               4410 non-null   int64  \n",
      " 1   Age                      4410 non-null   int64  \n",
      " 2   Attrition                4410 non-null   object \n",
      " 3   BusinessTravel           4410 non-null   object \n",
      " 4   Department               4410 non-null   object \n",
      " 5   DistanceFromHome         4410 non-null   int64  \n",
      " 6   Education                4410 non-null   int64  \n",
      " 7   EducationField           4410 non-null   object \n",
      " 8   EmployeeCount            4410 non-null   int64  \n",
      " 9   Gender                   4410 non-null   object \n",
      " 10  JobLevel                 4410 non-null   int64  \n",
      " 11  JobRole                  4410 non-null   object \n",
      " 12  MaritalStatus            4410 non-null   object \n",
      " 13  MonthlyIncome            4410 non-null   int64  \n",
      " 14  NumCompaniesWorked       4391 non-null   float64\n",
      " 15  Over18                   4410 non-null   object \n",
      " 16  PercentSalaryHike        4410 non-null   int64  \n",
      " 17  StandardHours            4410 non-null   int64  \n",
      " 18  StockOptionLevel         4410 non-null   int64  \n",
      " 19  TotalWorkingYears        4401 non-null   float64\n",
      " 20  TrainingTimesLastYear    4410 non-null   int64  \n",
      " 21  YearsAtCompany           4410 non-null   int64  \n",
      " 22  YearsSinceLastPromotion  4410 non-null   int64  \n",
      " 23  YearsWithCurrManager     4410 non-null   int64  \n",
      " 24  EnvironmentSatisfaction  4385 non-null   float64\n",
      " 25  JobSatisfaction          4390 non-null   float64\n",
      " 26  WorkLifeBalance          4372 non-null   float64\n",
      " 27  JobInvolvement           4410 non-null   int64  \n",
      " 28  PerformanceRating        4410 non-null   int64  \n",
      "dtypes: float64(5), int64(16), object(8)\n",
      "memory usage: 999.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "061fa13f-a935-4277-8bbc-71afa7052efd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     No\n",
      "1    Yes\n",
      "2     No\n",
      "3     No\n",
      "4     No\n",
      "Name: Attrition, dtype: object\n",
      "0    0\n",
      "1    1\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "Name: Attrition, dtype: object\n"
     ]
    }
   ],
   "source": [
    "    print(data['Attrition'].head())  # Before replacement\n",
    "    data['Attrition'] = data['Attrition'].replace({'Yes': 1, 'No': 0})\n",
    "    print(data['Attrition'].head())  # After replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "504b8c23-58be-47a5-92e5-4da053686f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    1\n",
      "2    5\n",
      "3    1\n",
      "4    2\n",
      "Name: EducationField, dtype: object\n",
      "0    1\n",
      "1    1\n",
      "2    5\n",
      "3    1\n",
      "4    2\n",
      "Name: EducationField, dtype: object\n"
     ]
    }
   ],
   "source": [
    "    print(data['EducationField'].head())  # Before replacement\n",
    "    data['EducationField'] = data['EducationField'].replace({\n",
    "        'Life Sciences': 1, \n",
    "        'Medical': 2, \n",
    "        'Marketing': 3,\n",
    "        'Technical Degree': 4, \n",
    "        'Other': 5, \n",
    "        'Human Resources': 6\n",
    "    })\n",
    "    print(data['EducationField'].head())  # After replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ddb24024-0436-430b-a87a-20f481dade2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 3]\n"
     ]
    }
   ],
   "source": [
    "data['Department'] = data['Department'].replace({\n",
    "    'Research & Development': 1, \n",
    "    'Sales': 2, \n",
    "    'Human Resources': 3\n",
    "}).infer_objects(copy=False)\n",
    "print(data['Department'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1488869a-6135-48cc-b6f7-7c3bc9e5b3b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       2\n",
      "1       1\n",
      "2       1\n",
      "3       1\n",
      "4       1\n",
      "       ..\n",
      "4405    1\n",
      "4406    1\n",
      "4407    1\n",
      "4408    2\n",
      "4409    1\n",
      "Name: Department, Length: 4410, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data['MaritalStatus'] = data['MaritalStatus'].replace({\n",
    "    'Married': 1, \n",
    "    'Single': 2, \n",
    "    'Divorced': 3\n",
    "}).infer_objects(copy=False)\n",
    "print(data['Department'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "acbaa180-a315-4f82-94d8-df471055ed6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       2\n",
      "2       2\n",
      "3       3\n",
      "4       1\n",
      "       ..\n",
      "4405    1\n",
      "4406    1\n",
      "4407    1\n",
      "4408    1\n",
      "4409    1\n",
      "Name: BusinessTravel, Length: 4410, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Set the option to suppress the warning\n",
    "pd.set_option('future.no_silent_downcasting', True)\n",
    "\n",
    "# Your existing code\n",
    "data['BusinessTravel'] = data['BusinessTravel'].replace({\n",
    "    'Travel_Rarely': 1, \n",
    "    'Travel_Frequently': 2, \n",
    "    'Non-Travel': 3\n",
    "}).infer_objects(copy=False)\n",
    "print(data['BusinessTravel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e1d99f2e-ad75-4746-8b8b-afaeb98f7079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       1\n",
      "2       2\n",
      "3       2\n",
      "4       2\n",
      "       ..\n",
      "4405    1\n",
      "4406    2\n",
      "4407    2\n",
      "4408    2\n",
      "4409    2\n",
      "Name: Gender, Length: 4410, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data['Gender'] = data['Gender'].replace({\n",
    "    'Female': 1, \n",
    "    'Male': 2\n",
    "}).infer_objects(copy=False)\n",
    "print(data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5c0f300b-df72-43e5-86ff-fbfd72fd2431",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Over18'] = data['Over18'].replace({\n",
    "    'Y': 1\n",
    "}).infer_objects(copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e214c616-5ee2-4d2b-8ffd-54d161eafd77",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['JobRole'] = data['JobRole'].replace({\n",
    "    'Sales Executive': 1, \n",
    "    'Research Scientist': 2, \n",
    "    'Laboratory Technician': 3,\n",
    "    'Manufacturing Director': 4, \n",
    "    'Healthcare Representative': 5, \n",
    "    'Manager': 6,\n",
    "    'Sales Representative': 7,\n",
    "    'Research Director': 8,\n",
    "    'Human Resources': 9\n",
    "}).infer_objects(copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "1d87696c-89d3-4c5a-907d-0af9dfe35e5d",
   "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>EmployeeID</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>PerformanceRating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeID  Age  BusinessTravel  Department  DistanceFromHome  Education  \\\n",
       "0           1   51               1           2                 6          2   \n",
       "1           2   31               2           1                10          1   \n",
       "2           3   32               2           1                17          4   \n",
       "3           4   38               3           1                 2          5   \n",
       "4           5   32               1           1                10          1   \n",
       "\n",
       "  EducationField  EmployeeCount  Gender  JobLevel  ...  TotalWorkingYears  \\\n",
       "0              1              1       1         1  ...                1.0   \n",
       "1              1              1       1         1  ...                6.0   \n",
       "2              5              1       2         4  ...                5.0   \n",
       "3              1              1       2         3  ...               13.0   \n",
       "4              2              1       2         1  ...                9.0   \n",
       "\n",
       "   TrainingTimesLastYear  YearsAtCompany  YearsSinceLastPromotion  \\\n",
       "0                      6               1                        0   \n",
       "1                      3               5                        1   \n",
       "2                      2               5                        0   \n",
       "3                      5               8                        7   \n",
       "4                      2               6                        0   \n",
       "\n",
       "   YearsWithCurrManager  EnvironmentSatisfaction  JobSatisfaction  \\\n",
       "0                     0                      3.0              4.0   \n",
       "1                     4                      3.0              2.0   \n",
       "2                     3                      2.0              2.0   \n",
       "3                     5                      4.0              4.0   \n",
       "4                     4                      4.0              1.0   \n",
       "\n",
       "   WorkLifeBalance  JobInvolvement  PerformanceRating  \n",
       "0              2.0               3                  3  \n",
       "1              4.0               2                  4  \n",
       "2              1.0               3                  3  \n",
       "3              3.0               2                  3  \n",
       "4              3.0               3                  3  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop(['Attrition'],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "167e193d-e36a-4d88-9635-9302b60cb1ad",
   "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>Attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Attrition\n",
       "0         0\n",
       "1         1\n",
       "2         0\n",
       "3         0\n",
       "4         0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data[['Attrition']]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f2ae5969-c5b6-422f-8310-cf392ff21233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3307, 28) (1103, 28) (3307, 1) (1103, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y, random_state=5)\n",
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1b9f1cfa-e51c-45ff-a1ad-61284b09d4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Create an imputer to replace NaN with the mean (or other strategy)\n",
    "imputer = SimpleImputer(strategy='mean')  # You can also use 'median' or 'most_frequent'\n",
    "\n",
    "# Fit the imputer on X_train and transform X_train\n",
    "X_train = imputer.fit_transform(X_train)\n",
    "\n",
    "# Apply the same transformation to X_test if needed\n",
    "X_test = imputer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "0a48b89a-e622-4d32-bf20-5309a4892d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Ensure 'Attrition' column is correctly encoded\n",
    "data['Attrition'] = data['Attrition'].replace({'Yes': 1, 'No': 0})\n",
    "\n",
    "# Split the data into features and target\n",
    "X = data.drop(['Attrition'], axis=1)\n",
    "y = data['Attrition']\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)\n",
    "\n",
    "# Handle missing values in X_train\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "X_train = imputer.fit_transform(X_train)\n",
    "X_test = imputer.transform(X_test)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Ensure y_train is a 1D array of integers\n",
    "y_train = y_train.values.ravel().astype(int)\n",
    "\n",
    "# Fit the Logistic Regression model with increased max_iter\n",
    "lr = LogisticRegression(max_iter=200)  # Increase max_iter as needed\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "138b2c06-39c1-4191-9a2e-8820f41dfccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.82930514 0.83383686 0.83509834 0.83509834 0.83509834]\n",
      "Mean CV score: 0.8336874003044003\n",
      "Best parameters found by GridSearchCV: {'C': 0.1, 'max_iter': 100}\n",
      "Cross-validation scores: [0.82930514 0.83383686 0.83509834 0.83509834 0.83509834]\n",
      "Mean CV score: 0.8336874003044003\n",
      "Best parameters found by GridSearchCV: {'C': 0.1, 'max_iter': 100}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.1)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# ... existing code ...\n",
    "\n",
    "# Fit the Logistic Regression model\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# Cross-validation\n",
    "cv_scores = cross_val_score(lr, X_train, y_train, cv=5)\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(\"Mean CV score:\", cv_scores.mean())\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV\n",
    "param_grid = {'C': [0.1, 1, 10, 100], 'max_iter': [100, 200, 300]}\n",
    "grid_search = GridSearchCV(LogisticRegression(), param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters found by GridSearchCV\n",
    "print(\"Best parameters found by GridSearchCV:\", grid_search.best_params_)\n",
    "\n",
    "# Optionally, you can refit the model with the best parameters\n",
    "best_lr = grid_search.best_estimator_\n",
    "best_lr.fit(X_train, y_train)\n",
    "\n",
    "# ... continue with predictions and further analysis ...\n",
    "# Cross-validation\n",
    "cv_scores = cross_val_score(lr, X_train, y_train, cv=5)\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(\"Mean CV score:\", cv_scores.mean())\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV\n",
    "param_grid = {'C': [0.1, 1, 10, 100], 'max_iter': [100, 200, 300]}\n",
    "grid_search = GridSearchCV(LogisticRegression(), param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters found by GridSearchCV\n",
    "print(\"Best parameters found by GridSearchCV:\", grid_search.best_params_)\n",
    "\n",
    "# Optionally, you can refit the model with the best parameters\n",
    "best_lr = grid_search.best_estimator_\n",
    "best_lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "970ff803-9196-4a8d-ad64-3fed0de59e36",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "bfb9c2e9-89c5-466d-a086-a62a3547cffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "22fc97d4-94ed-42b9-957a-8f67fbb45944",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_prob = lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "28b70e40-23f2-41d2-850c-fd08251e7c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.76471734 0.23528266]\n",
      " [0.90855235 0.09144765]\n",
      " [0.83602706 0.16397294]\n",
      " ...\n",
      " [0.83667294 0.16332706]\n",
      " [0.92072985 0.07927015]\n",
      " [0.96560642 0.03439358]]\n"
     ]
    }
   ],
   "source": [
    "print(y_predicted_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7c919ddc-d6eb-4fea-bded-efc4111e16bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary library for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# Feature importance\n",
    "# Assuming X is your DataFrame with feature names\n",
    "feature_importance = np.abs(lr.coef_[0])\n",
    "feature_names = X.columns\n",
    "\n",
    "# Plot feature importance\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x=feature_importance, y=feature_names)\n",
    "plt.title('Feature Importance from Logistic Regression Coefficients')\n",
    "plt.xlabel('Coefficient Magnitude')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f99fe0c2-f38b-4a16-b6ad-41ac35d98252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot ROC curve to evaluate model's ability to predict employee attrition\n",
    "fpr, tpr, _ = roc_curve(y_test, y_predicted_prob[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate (Employees Predicted to Stay)')\n",
    "plt.ylabel('True Positive Rate (Employees Predicted to Leave)')\n",
    "plt.title('ROC Curve for Employee Attrition Prediction')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "# Plot confusion matrix to visualize prediction accuracy for employee attrition\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted Stay', 'Predicted Leave'], yticklabels=['Actual Stay', 'Actual Leave'])\n",
    "plt.ylabel('Actual Employee Status')\n",
    "plt.xlabel('Predicted Employee Status')\n",
    "plt.title('Confusion Matrix for Employee Attrition Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "eb7bd4bc-7e7f-4055-bba9-bf13eb60a49f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in y_test: [0 1]\n",
      "Unique values in y_predicted: [0 1]\n",
      "0.14505893019038985\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "# Convert to numpy arrays and ensure they are 1D\n",
    "y_test = np.array(y_test).ravel()\n",
    "y_predicted = np.array(y_predicted).ravel()\n",
    "\n",
    "# Check the unique values in both arrays\n",
    "print(\"Unique values in y_test:\", np.unique(y_test))\n",
    "print(\"Unique values in y_predicted:\", np.unique(y_predicted))\n",
    "\n",
    "# Convert to integer type if necessary\n",
    "y_test = y_test.astype(int)\n",
    "y_predicted = y_predicted.astype(int)\n",
    "\n",
    "# Ensure both arrays have the same length\n",
    "assert len(y_test) == len(y_predicted), \"y_test and y_predicted must have the same length\"\n",
    "\n",
    "# Now calculate the accuracy score\n",
    "print(metrics.accuracy_score(y_test, y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "b5c9387a-6a96-4f02-895b-cd6fb10517ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2855593651783163\n"
     ]
    }
   ],
   "source": [
    "print (metrics.roc_auc_score(y_test, y_predicted_prob[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "432ba282-54e9-4f40-a4d2-26d0f7815a62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.14      0.96      0.25       164\n",
      "           1       0.30      0.00      0.01       939\n",
      "\n",
      "    accuracy                           0.15      1103\n",
      "   macro avg       0.22      0.48      0.13      1103\n",
      "weighted avg       0.28      0.15      0.04      1103\n",
      "\n",
      "[[157   7]\n",
      " [936   3]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, precision_score, confusion_matrix\n",
    "\n",
    "# Assuming y_true = y_test and y_pred = y_predicted\n",
    "y_true = y_test\n",
    "y_pred = y_predicted\n",
    "\n",
    "# Set zero_division to 1 or 0 to handle undefined precision\n",
    "print(classification_report(y_true, y_pred, zero_division=1))\n",
    "print(confusion_matrix(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "5aa97a68-b898-4579-8e13-55605c647c1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          1.4519238  -0.33796202 ...  2.06484698  1.06398949\n",
      "   2.16136363]\n",
      " [ 0.         -0.68874138 -0.33796202 ... -0.03389707  1.06398949\n",
      "   1.33255687]\n",
      " [ 0.         -0.68874138  2.95891234 ...  2.43521357  1.06398949\n",
      "  -0.82234072]\n",
      " ...\n",
      " [ 0.         -0.68874138 -0.33796202 ...  0.58338059  0.08984407\n",
      "  -0.49081801]\n",
      " [ 0.         -0.68874138 -0.33796202 ... -0.5277192   0.08984407\n",
      "  -0.32505666]\n",
      " [ 0.         -0.68874138 -0.33796202 ...  0.21301399 -0.88430134\n",
      "   0.00646605]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "ee56ed80-3c8e-4d2d-b8f6-bdca73eca483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.01621327 0.98378673]]\n"
     ]
    }
   ],
   "source": [
    "# Assuming these are the features used in training\n",
    "# Adjust this list to match the features actually used in your model\n",
    "feature_names = [\n",
    "    'Age', 'BusinessTravel', 'Department', 'DistanceFromHome',\n",
    "    'Education', 'EducationField', 'Gender', 'HourlyRate', 'JobLevel',\n",
    "    'JobRole', 'MaritalStatus'  # Add any missing features here\n",
    "]\n",
    "\n",
    "# Create a new input with the correct number of features\n",
    "# Adjust the values to match the order and type of your training features\n",
    "kk = [[40, 1, 2, 10, 3, 5, 1, 30, 2, 3, 1]]  # Example values, ensure it has 11 features\n",
    "\n",
    "# Apply the same preprocessing to kk\n",
    "kk_preprocessed = scaler.transform(kk)\n",
    "\n",
    "# Predict probabilities\n",
    "print(lr.predict_proba(kk_preprocessed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "635d8823-b27c-48e4-ae1e-959b1571fc7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from patsy import dmatrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "0958fbcb-260c-479a-9e4c-9a51f7773e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "y,X = dmatrices('Attrition ~ Age + Department + \\\n",
    "                  DistanceFromHome + Education + EducationField + YearsAtCompany',\n",
    "                  data, return_type=\"dataframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "465cd004-74c7-4357-9126-390342bda361",
   "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",
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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>Attrition[0]</th>\n",
       "      <th>Attrition[1]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <th>4406</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>4407</th>\n",
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       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4410 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Attrition[0]  Attrition[1]\n",
       "0              1.0           0.0\n",
       "1              0.0           1.0\n",
       "2              1.0           0.0\n",
       "3              1.0           0.0\n",
       "4              1.0           0.0\n",
       "...            ...           ...\n",
       "4405           1.0           0.0\n",
       "4406           1.0           0.0\n",
       "4407           1.0           0.0\n",
       "4408           1.0           0.0\n",
       "4409           1.0           0.0\n",
       "\n",
       "[4410 rows x 2 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "f014a1a7-45b4-47e7-a521-ac380ed0113a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Intercept</th>\n",
       "      <th>EducationField[T.2]</th>\n",
       "      <th>EducationField[T.3]</th>\n",
       "      <th>EducationField[T.4]</th>\n",
       "      <th>EducationField[T.5]</th>\n",
       "      <th>EducationField[T.6]</th>\n",
       "      <th>Age</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4405</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>4407</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4408</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>4409</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4410 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Intercept  EducationField[T.2]  EducationField[T.3]  \\\n",
       "0           1.0                  0.0                  0.0   \n",
       "1           1.0                  0.0                  0.0   \n",
       "2           1.0                  0.0                  0.0   \n",
       "3           1.0                  0.0                  0.0   \n",
       "4           1.0                  1.0                  0.0   \n",
       "...         ...                  ...                  ...   \n",
       "4405        1.0                  1.0                  0.0   \n",
       "4406        1.0                  1.0                  0.0   \n",
       "4407        1.0                  0.0                  0.0   \n",
       "4408        1.0                  1.0                  0.0   \n",
       "4409        1.0                  1.0                  0.0   \n",
       "\n",
       "      EducationField[T.4]  EducationField[T.5]  EducationField[T.6]   Age  \\\n",
       "0                     0.0                  0.0                  0.0  51.0   \n",
       "1                     0.0                  0.0                  0.0  31.0   \n",
       "2                     0.0                  1.0                  0.0  32.0   \n",
       "3                     0.0                  0.0                  0.0  38.0   \n",
       "4                     0.0                  0.0                  0.0  32.0   \n",
       "...                   ...                  ...                  ...   ...   \n",
       "4405                  0.0                  0.0                  0.0  42.0   \n",
       "4406                  0.0                  0.0                  0.0  29.0   \n",
       "4407                  0.0                  0.0                  0.0  25.0   \n",
       "4408                  0.0                  0.0                  0.0  42.0   \n",
       "4409                  0.0                  0.0                  0.0  40.0   \n",
       "\n",
       "      Department  DistanceFromHome  Education  YearsAtCompany  \n",
       "0            2.0               6.0        2.0             1.0  \n",
       "1            1.0              10.0        1.0             5.0  \n",
       "2            1.0              17.0        4.0             5.0  \n",
       "3            1.0               2.0        5.0             8.0  \n",
       "4            1.0              10.0        1.0             6.0  \n",
       "...          ...               ...        ...             ...  \n",
       "4405         1.0               5.0        4.0             3.0  \n",
       "4406         1.0               2.0        4.0             3.0  \n",
       "4407         1.0              25.0        2.0             4.0  \n",
       "4408         2.0              18.0        2.0             9.0  \n",
       "4409         1.0              28.0        3.0            21.0  \n",
       "\n",
       "[4410 rows x 11 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "c8de81a7-39a1-486a-841a-31ae0a6dce8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.18372452 0.81627548]\n",
      " [0.17811303 0.82188697]\n",
      " [0.12622937 0.87377063]\n",
      " ...\n",
      " [0.18063301 0.81936699]\n",
      " [0.12405806 0.87594194]\n",
      " [0.13748308 0.86251692]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Check if y is a DataFrame or Series\n",
    "if isinstance(y, pd.DataFrame):\n",
    "    y = y.iloc[:, 0]  # Select the first column if y is a DataFrame\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Fit the Logistic Regression model with increased max_iter\n",
    "model2 = LogisticRegression(max_iter=500, solver='lbfgs')  # Increase max_iter as needed\n",
    "model2.fit(X_train, y_train)\n",
    "\n",
    "# Predict probabilities\n",
    "probs = model2.predict_proba(X_test)\n",
    "print(probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "c56dcf75-1864-4e65-8ca1-92216b5690d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 1. 1. ... 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# Ensure y is a 1D array\n",
    "# y = y.to_numpy()  # This line is not needed if y is already a numpy array\n",
    "\n",
    "# Fit the model\n",
    "model2 = LogisticRegression()\n",
    "model2.fit(X_train, y_train)\n",
    "\n",
    "# Predict using the fitted model\n",
    "predicted = model2.predict(X_test)\n",
    "print(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "4917642d-4b02-40f2-b220-2a75f959eed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.18372452 0.81627548]\n",
      " [0.17811303 0.82188697]\n",
      " [0.12622937 0.87377063]\n",
      " ...\n",
      " [0.18063301 0.81936699]\n",
      " [0.12405806 0.87594194]\n",
      " [0.13748308 0.86251692]]\n"
     ]
    }
   ],
   "source": [
    "probs = model2.predict_proba(X_test)\n",
    "print (probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "8f953f98-6c4a-4379-9ad9-d3df04c0f973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8522212148685403\n",
      "0.648218135535988\n"
     ]
    }
   ],
   "source": [
    "print (metrics.accuracy_score(y_test, predicted))\n",
    "print (metrics.roc_auc_score(y_test, probs[:, 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "7d1b6580-d38e-4d9b-863a-a5b24fad6cae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.4519238 , -0.33796202, ...,  2.06484698,\n",
       "         1.06398949,  2.16136363],\n",
       "       [ 0.        , -0.68874138, -0.33796202, ..., -0.03389707,\n",
       "         1.06398949,  1.33255687],\n",
       "       [ 0.        , -0.68874138,  2.95891234, ...,  2.43521357,\n",
       "         1.06398949, -0.82234072],\n",
       "       ...,\n",
       "       [ 0.        , -0.68874138, -0.33796202, ...,  0.58338059,\n",
       "         0.08984407, -0.49081801],\n",
       "       [ 0.        , -0.68874138, -0.33796202, ..., -0.5277192 ,\n",
       "         0.08984407, -0.32505666],\n",
       "       [ 0.        , -0.68874138, -0.33796202, ...,  0.21301399,\n",
       "        -0.88430134,  0.00646605]])"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "4cf9b7c3-649a-49a1-ad56-880775499b25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.22044605e-16 1.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Assuming you have a list of feature names in the same order as your training data\n",
    "feature_names = [\n",
    "    'Intercept', 'EducationField[T.2]', 'EducationField[T.3]', \n",
    "    'EducationField[T.4]', 'EducationField[T.5]', 'EducationField[T.6]', \n",
    "    'Age', 'Department', 'DistanceFromHome', 'Education', 'YearsAtCompany'\n",
    "]\n",
    "\n",
    "# Add random values to kk according to the parameters mentioned above\n",
    "kk = [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 23.0, 1.0, 500.0, 3.0, 24.0]]\n",
    "\n",
    "# Ensure kk is a numpy array and has the correct shape\n",
    "kk = np.array(kk)\n",
    "\n",
    "# Predict probabilities\n",
    "print(model2.predict_proba(kk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "8b647cfb-62cb-4ac7-8e03-0a4d74e2d865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, confusion_matrix\n",
    "\n",
    "# Assuming you have already defined y_pred and y_pred_prob\n",
    "y_pred = model2.predict(X_test)\n",
    "y_pred_prob = model2.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Confusion Matrix Heatmap\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted Stay', 'Predicted Leave'], yticklabels=['Actual Stay', 'Actual Leave'])\n",
    "plt.ylabel('Actual Employee Status')\n",
    "plt.xlabel('Predicted Employee Status')\n",
    "plt.title('Confusion Matrix for Employee Attrition Prediction')\n",
    "plt.show()\n",
    "\n",
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_score(y_test, y_pred_prob))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate (Predicted Stay)')\n",
    "plt.ylabel('True Positive Rate (Predicted Leave)')\n",
    "plt.title('ROC Curve for Employee Attrition Prediction')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "# Precision-Recall Curve\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred_prob)\n",
    "plt.figure()\n",
    "plt.plot(recall, precision, marker='.', label='Precision-Recall curve')\n",
    "plt.xlabel('Recall (True Positive Rate)')\n",
    "plt.ylabel('Precision (Positive Predictive Value)')\n",
    "plt.title('Precision-Recall Curve for Employee Attrition Prediction')\n",
    "plt.legend(loc=\"lower left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9da11b87-0257-4555-8b93-7913c189f60c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}