XAI - mathgrades - regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "044981ec",
   "metadata": {},
   "source": [
    "## Quick note about Jupyter cells\n",
    "\n",
    "When you are editing a cell in Jupyter notebook, you need to re-run the cell by pressing `<Shift> + <Enter>`. This will allow changes you made to be available to other cells.\n",
    "\n",
    "Use `<Enter>` to make new lines inside a cell you are editing.\n",
    "\n",
    "### Code cells\n",
    "Re-running will execute any statements you have written. To edit an existing code cell, click on it.\n",
    "\n",
    "### Markdown cells\n",
    "Re-running will render the markdown text. To edit an existing markdown cell, double-click on it.\n",
    "\n",
    "\n",
    "### Common Jupyter operations\n",
    "\n",
    "**Inserting and removing cells**\n",
    "\n",
    "Use the \"plus sign\" icon to insert a cell below the currently selected cell\n",
    "Use \"Insert\" -> \"Insert Cell Above\" from the menu to insert above\n",
    "\n",
    "**Clear the output of all cells**\n",
    "\n",
    "Use \"Kernel\" -> \"Restart\" from the menu to restart the kernel\n",
    "click on \"clear all outputs & restart\" to have all the output cleared\n",
    "\n",
    "**Show function signature**\n",
    "\n",
    "Start typing function and hit `<Shift> + <Tab>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "occupied-china",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "romantic-peoples",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Technical Degree</td>\n",
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       "<p>1676 rows × 35 columns</p>\n",
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      ],
      "text/plain": [
       "      EmployeeID  Age Attrition     BusinessTravel  DailyRate  Department  \\\n",
       "0        1313919   41        No      Travel_Rarely       1102  Cardiology   \n",
       "1        1200302   49        No  Travel_Frequently        279   Maternity   \n",
       "2        1060315   37       Yes      Travel_Rarely       1373   Maternity   \n",
       "3        1272912   33        No  Travel_Frequently       1392   Maternity   \n",
       "4        1414939   27        No      Travel_Rarely        591   Maternity   \n",
       "...          ...  ...       ...                ...        ...         ...   \n",
       "1671     1117656   26       Yes      Travel_Rarely        471   Neurology   \n",
       "1672     1152327   46        No      Travel_Rarely       1125  Cardiology   \n",
       "1673     1812428   20        No      Travel_Rarely        959   Maternity   \n",
       "1674     1812429   39        No      Travel_Rarely        466   Neurology   \n",
       "1675     1152329   27        No      Travel_Rarely        511  Cardiology   \n",
       "\n",
       "      DistanceFromHome  Education    EducationField  EmployeeCount  ...  \\\n",
       "0                    1          2     Life Sciences              1  ...   \n",
       "1                    8          1     Life Sciences              1  ...   \n",
       "2                    2          2             Other              1  ...   \n",
       "3                    3          4     Life Sciences              1  ...   \n",
       "4                    2          1           Medical              1  ...   \n",
       "...                ...        ...               ...            ...  ...   \n",
       "1671                24          3  Technical Degree              1  ...   \n",
       "1672                10          3         Marketing              1  ...   \n",
       "1673                 1          3     Life Sciences              1  ...   \n",
       "1674                 1          1     Life Sciences              1  ...   \n",
       "1675                 2          2           Medical              1  ...   \n",
       "\n",
       "      RelationshipSatisfaction StandardHours  Shift  TotalWorkingYears  \\\n",
       "0                            1            80      0                  8   \n",
       "1                            4            80      1                 10   \n",
       "2                            2            80      0                  7   \n",
       "3                            3            80      0                  8   \n",
       "4                            4            80      1                  6   \n",
       "...                        ...           ...    ...                ...   \n",
       "1671                         2            80      0                  1   \n",
       "1672                         3            80      1                 15   \n",
       "1673                         4            80      0                  1   \n",
       "1674                         3            80      1                 21   \n",
       "1675                         2            80      0                  9   \n",
       "\n",
       "      TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                         0               1               6   \n",
       "1                         3               3              10   \n",
       "2                         3               3               0   \n",
       "3                         3               3               8   \n",
       "4                         3               3               2   \n",
       "...                     ...             ...             ...   \n",
       "1671                      3               1               1   \n",
       "1672                      3               3               3   \n",
       "1673                      0               4               1   \n",
       "1674                      3               3              21   \n",
       "1675                      5               2               8   \n",
       "\n",
       "     YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                     4                        0                     5  \n",
       "1                     7                        1                     7  \n",
       "2                     0                        0                     0  \n",
       "3                     7                        3                     0  \n",
       "4                     2                        2                     2  \n",
       "...                 ...                      ...                   ...  \n",
       "1671                  0                        0                     0  \n",
       "1672                  2                        1                     2  \n",
       "1673                  0                        0                     0  \n",
       "1674                  6                       11                     8  \n",
       "1675                  7                        0                     7  \n",
       "\n",
       "[1676 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"data/healthcare_attrition.csv\"\n",
    "df = pd.read_csv(path, encoding='utf-8')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0138a328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1676 entries, 0 to 1675\n",
      "Data columns (total 36 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   EmployeeID                1676 non-null   int64 \n",
      " 1   Age                       1676 non-null   int64 \n",
      " 2   Attrition                 1676 non-null   string\n",
      " 3   BusinessTravel            1676 non-null   object\n",
      " 4   DailyRate                 1676 non-null   int64 \n",
      " 5   Department                1676 non-null   object\n",
      " 6   DistanceFromHome          1676 non-null   int64 \n",
      " 7   Education                 1676 non-null   int64 \n",
      " 8   EducationField            1676 non-null   object\n",
      " 9   EmployeeCount             1676 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   1676 non-null   int64 \n",
      " 11  Gender                    1676 non-null   object\n",
      " 12  HourlyRate                1676 non-null   int64 \n",
      " 13  JobInvolvement            1676 non-null   int64 \n",
      " 14  JobLevel                  1676 non-null   int64 \n",
      " 15  JobRole                   1676 non-null   object\n",
      " 16  JobSatisfaction           1676 non-null   int64 \n",
      " 17  MaritalStatus             1676 non-null   object\n",
      " 18  MonthlyIncome             1676 non-null   int64 \n",
      " 19  MonthlyRate               1676 non-null   int64 \n",
      " 20  NumCompaniesWorked        1676 non-null   int64 \n",
      " 21  Over18                    1676 non-null   object\n",
      " 22  OverTime                  1676 non-null   object\n",
      " 23  PercentSalaryHike         1676 non-null   int64 \n",
      " 24  PerformanceRating         1676 non-null   int64 \n",
      " 25  RelationshipSatisfaction  1676 non-null   int64 \n",
      " 26  StandardHours             1676 non-null   int64 \n",
      " 27  Shift                     1676 non-null   int64 \n",
      " 28  TotalWorkingYears         1676 non-null   int64 \n",
      " 29  TrainingTimesLastYear     1676 non-null   int64 \n",
      " 30  WorkLifeBalance           1676 non-null   int64 \n",
      " 31  YearsAtCompany            1676 non-null   int64 \n",
      " 32  YearsInCurrentRole        1676 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   1676 non-null   int64 \n",
      " 34  YearsWithCurrManager      1676 non-null   int64 \n",
      " 35  Attrition_new             1676 non-null   string\n",
      "dtypes: int64(26), object(8), string(2)\n",
      "memory usage: 471.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "004cc8ff",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.0</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "      <td>1676.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.456796e+06</td>\n",
       "      <td>36.866348</td>\n",
       "      <td>800.557876</td>\n",
       "      <td>9.221957</td>\n",
       "      <td>2.907518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.714797</td>\n",
       "      <td>65.470167</td>\n",
       "      <td>2.724940</td>\n",
       "      <td>2.066826</td>\n",
       "      <td>...</td>\n",
       "      <td>2.718377</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.806086</td>\n",
       "      <td>11.338902</td>\n",
       "      <td>2.805489</td>\n",
       "      <td>2.766110</td>\n",
       "      <td>7.033413</td>\n",
       "      <td>4.264916</td>\n",
       "      <td>2.200477</td>\n",
       "      <td>4.135442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.487486e+05</td>\n",
       "      <td>9.129126</td>\n",
       "      <td>401.594438</td>\n",
       "      <td>8.158118</td>\n",
       "      <td>1.025835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.097534</td>\n",
       "      <td>20.207572</td>\n",
       "      <td>0.714121</td>\n",
       "      <td>1.113423</td>\n",
       "      <td>...</td>\n",
       "      <td>1.078162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.855527</td>\n",
       "      <td>7.834996</td>\n",
       "      <td>1.288431</td>\n",
       "      <td>0.702369</td>\n",
       "      <td>6.098991</td>\n",
       "      <td>3.627456</td>\n",
       "      <td>3.229587</td>\n",
       "      <td>3.559662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.025177e+06</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.235832e+06</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.464606e+06</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>796.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.667992e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.886378e+06</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         EmployeeID          Age    DailyRate  DistanceFromHome    Education  \\\n",
       "count  1.676000e+03  1676.000000  1676.000000       1676.000000  1676.000000   \n",
       "mean   1.456796e+06    36.866348   800.557876          9.221957     2.907518   \n",
       "std    2.487486e+05     9.129126   401.594438          8.158118     1.025835   \n",
       "min    1.025177e+06    18.000000   102.000000          1.000000     1.000000   \n",
       "25%    1.235832e+06    30.000000   465.000000          2.000000     2.000000   \n",
       "50%    1.464606e+06    36.000000   796.500000          7.000000     3.000000   \n",
       "75%    1.667992e+06    43.000000  1157.000000         14.000000     4.000000   \n",
       "max    1.886378e+06    60.000000  1499.000000         29.000000     5.000000   \n",
       "\n",
       "       EmployeeCount  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count         1676.0              1676.000000  1676.000000     1676.000000   \n",
       "mean             1.0                 2.714797    65.470167        2.724940   \n",
       "std              0.0                 1.097534    20.207572        0.714121   \n",
       "min              1.0                 1.000000    30.000000        1.000000   \n",
       "25%              1.0                 2.000000    48.000000        2.000000   \n",
       "50%              1.0                 3.000000    65.500000        3.000000   \n",
       "75%              1.0                 4.000000    83.000000        3.000000   \n",
       "max              1.0                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours        Shift  \\\n",
       "count  1676.000000  ...               1676.000000         1676.0  1676.000000   \n",
       "mean      2.066826  ...                  2.718377           80.0     0.806086   \n",
       "std       1.113423  ...                  1.078162            0.0     0.855527   \n",
       "min       1.000000  ...                  1.000000           80.0     0.000000   \n",
       "25%       1.000000  ...                  2.000000           80.0     0.000000   \n",
       "50%       2.000000  ...                  3.000000           80.0     1.000000   \n",
       "75%       3.000000  ...                  4.000000           80.0     1.000000   \n",
       "max       5.000000  ...                  4.000000           80.0     3.000000   \n",
       "\n",
       "       TotalWorkingYears  TrainingTimesLastYear  WorkLifeBalance  \\\n",
       "count        1676.000000            1676.000000      1676.000000   \n",
       "mean           11.338902               2.805489         2.766110   \n",
       "std             7.834996               1.288431         0.702369   \n",
       "min             0.000000               0.000000         1.000000   \n",
       "25%             6.000000               2.000000         2.000000   \n",
       "50%            10.000000               3.000000         3.000000   \n",
       "75%            15.000000               3.000000         3.000000   \n",
       "max            40.000000               6.000000         4.000000   \n",
       "\n",
       "       YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "count     1676.000000         1676.000000              1676.000000   \n",
       "mean         7.033413            4.264916                 2.200477   \n",
       "std          6.098991            3.627456                 3.229587   \n",
       "min          0.000000            0.000000                 0.000000   \n",
       "25%          3.000000            2.000000                 0.000000   \n",
       "50%          5.000000            3.000000                 1.000000   \n",
       "75%         10.000000            7.000000                 3.000000   \n",
       "max         40.000000           18.000000                15.000000   \n",
       "\n",
       "       YearsWithCurrManager  \n",
       "count           1676.000000  \n",
       "mean               4.135442  \n",
       "std                3.559662  \n",
       "min                0.000000  \n",
       "25%                2.000000  \n",
       "50%                3.000000  \n",
       "75%                7.000000  \n",
       "max               17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8faf906",
   "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>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>...</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>Shift</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Attrition_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1313919</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200302</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1060315</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1272912</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1414939</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>1117656</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>471</td>\n",
       "      <td>Neurology</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>1152327</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1125</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>1812428</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>959</td>\n",
       "      <td>Maternity</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>1812429</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>466</td>\n",
       "      <td>Neurology</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>1152329</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>511</td>\n",
       "      <td>Cardiology</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1676 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeID  Age  Attrition     BusinessTravel  DailyRate  Department  \\\n",
       "0        1313919   41          0      Travel_Rarely       1102  Cardiology   \n",
       "1        1200302   49          0  Travel_Frequently        279   Maternity   \n",
       "2        1060315   37          1      Travel_Rarely       1373   Maternity   \n",
       "3        1272912   33          0  Travel_Frequently       1392   Maternity   \n",
       "4        1414939   27          0      Travel_Rarely        591   Maternity   \n",
       "...          ...  ...        ...                ...        ...         ...   \n",
       "1671     1117656   26          1      Travel_Rarely        471   Neurology   \n",
       "1672     1152327   46          0      Travel_Rarely       1125  Cardiology   \n",
       "1673     1812428   20          0      Travel_Rarely        959   Maternity   \n",
       "1674     1812429   39          0      Travel_Rarely        466   Neurology   \n",
       "1675     1152329   27          0      Travel_Rarely        511  Cardiology   \n",
       "\n",
       "      DistanceFromHome  Education    EducationField  EmployeeCount  ...  \\\n",
       "0                    1          2     Life Sciences              1  ...   \n",
       "1                    8          1     Life Sciences              1  ...   \n",
       "2                    2          2             Other              1  ...   \n",
       "3                    3          4     Life Sciences              1  ...   \n",
       "4                    2          1           Medical              1  ...   \n",
       "...                ...        ...               ...            ...  ...   \n",
       "1671                24          3  Technical Degree              1  ...   \n",
       "1672                10          3         Marketing              1  ...   \n",
       "1673                 1          3     Life Sciences              1  ...   \n",
       "1674                 1          1     Life Sciences              1  ...   \n",
       "1675                 2          2           Medical              1  ...   \n",
       "\n",
       "      StandardHours Shift  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                80     0                  8                      0   \n",
       "1                80     1                 10                      3   \n",
       "2                80     0                  7                      3   \n",
       "3                80     0                  8                      3   \n",
       "4                80     1                  6                      3   \n",
       "...             ...   ...                ...                    ...   \n",
       "1671             80     0                  1                      3   \n",
       "1672             80     1                 15                      3   \n",
       "1673             80     0                  1                      0   \n",
       "1674             80     1                 21                      3   \n",
       "1675             80     0                  9                      5   \n",
       "\n",
       "      WorkLifeBalance YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                   1              6                   4   \n",
       "1                   3             10                   7   \n",
       "2                   3              0                   0   \n",
       "3                   3              8                   7   \n",
       "4                   3              2                   2   \n",
       "...               ...            ...                 ...   \n",
       "1671                1              1                   0   \n",
       "1672                3              3                   2   \n",
       "1673                4              1                   0   \n",
       "1674                3             21                   6   \n",
       "1675                2              8                   7   \n",
       "\n",
       "     YearsSinceLastPromotion  YearsWithCurrManager  Attrition_new  \n",
       "0                          0                     5             No  \n",
       "1                          1                     7             No  \n",
       "2                          0                     0            Yes  \n",
       "3                          3                     0             No  \n",
       "4                          2                     2             No  \n",
       "...                      ...                   ...            ...  \n",
       "1671                       0                     0            Yes  \n",
       "1672                       1                     2             No  \n",
       "1673                       0                     0             No  \n",
       "1674                      11                     8             No  \n",
       "1675                       0                     7             No  \n",
       "\n",
       "[1676 rows x 36 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attrition'] = df['Attrition'].map({'Yes': 1, 'No':0})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "55c38e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_feature = df.Attrition\n",
    "features = df.drop('Attrition',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2537302f",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Travel_Rarely'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-d13cd0104969>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdtc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdtc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_feature_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mdtc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    896\u001b[0m         \"\"\"\n\u001b[0;32m    897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 898\u001b[1;33m         super().fit(\n\u001b[0m\u001b[0;32m    899\u001b[0m             \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    900\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[0mcheck_X_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"csc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m             \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_2d\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m             X, y = self._validate_data(X, y,\n\u001b[0m\u001b[0;32m    157\u001b[0m                                        validate_separately=(check_X_params,\n\u001b[0;32m    158\u001b[0m                                                             check_y_params))\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    428\u001b[0m                 \u001b[1;31m# :(\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m                 \u001b[0mcheck_X_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_y_params\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalidate_separately\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m                 \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_X_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    614\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    615\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 616\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    617\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    618\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \"\"\"\n\u001b[1;32m---> 83\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1898\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1899\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1900\u001b[0m     def __array_wrap__(\n",
      "\u001b[1;32mc:\\Users\\luise.prielinger\\Miniconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m     \"\"\"\n\u001b[1;32m---> 83\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Travel_Rarely'"
     ]
    }
   ],
   "source": [
    "features_train, features_test, target_feature_train , target_feature_test, = train_test_split(features, target_feature,  test_size=0.33, random_state=42)\n",
    "\n",
    "\n",
    "dtc = DecisionTreeClassifier(random_state=42)\n",
    "dtc.fit(features_train, target_feature_train)\n",
    "dtc.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "14efb79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "727     0\n",
       "1491    0\n",
       "243     0\n",
       "1233    0\n",
       "312     0\n",
       "       ..\n",
       "1130    0\n",
       "1294    0\n",
       "860     0\n",
       "1459    0\n",
       "1126    0\n",
       "Name: Attrition, Length: 1122, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6528e507",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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