HR Data Science exercise (employee retention) ML Model.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "39b21b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "18e62eaa",
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   "outputs": [
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      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
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       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
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       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years Department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
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     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/2mris/Downloads/archive/HR_comma_sep.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3987c9e",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  salary\n",
       "0                    0.38                   157                      0     low\n",
       "1                    0.80                   262                      0  medium\n",
       "2                    0.11                   272                      0  medium\n",
       "3                    0.72                   223                      0     low\n",
       "4                    0.37                   159                      0     low\n",
       "...                   ...                   ...                    ...     ...\n",
       "14994                0.40                   151                      0     low\n",
       "14995                0.37                   160                      0     low\n",
       "14996                0.37                   143                      0     low\n",
       "14997                0.11                   280                      0     low\n",
       "14998                0.37                   158                      0     low\n",
       "\n",
       "[14999 rows x 4 columns]"
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "new_df = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "feb3eefa",
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       "       high  low  medium\n",
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   "source": [
    "salarydummies = pd.get_dummies(new_df.salary)\n",
    "salarydummies\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63f21c21",
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       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                    0.38                   157                      0   \n",
       "1                    0.80                   262                      0   \n",
       "2                    0.11                   272                      0   \n",
       "3                    0.72                   223                      0   \n",
       "4                    0.37                   159                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "14994                0.40                   151                      0   \n",
       "14995                0.37                   160                      0   \n",
       "14996                0.37                   143                      0   \n",
       "14997                0.11                   280                      0   \n",
       "14998                0.37                   158                      0   \n",
       "\n",
       "       salary  high  low  medium  \n",
       "0         low     0    1       0  \n",
       "1      medium     0    0       1  \n",
       "2      medium     0    0       1  \n",
       "3         low     0    1       0  \n",
       "4         low     0    1       0  \n",
       "...       ...   ...  ...     ...  \n",
       "14994     low     0    1       0  \n",
       "14995     low     0    1       0  \n",
       "14996     low     0    1       0  \n",
       "14997     low     0    1       0  \n",
       "14998     low     0    1       0  \n",
       "\n",
       "[14999 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_connect = pd.concat([new_df,salarydummies], axis='columns')\n",
    "df_connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c4eef7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  high  \\\n",
       "0                    0.38                   157                      0     0   \n",
       "1                    0.80                   262                      0     0   \n",
       "2                    0.11                   272                      0     0   \n",
       "3                    0.72                   223                      0     0   \n",
       "4                    0.37                   159                      0     0   \n",
       "...                   ...                   ...                    ...   ...   \n",
       "14994                0.40                   151                      0     0   \n",
       "14995                0.37                   160                      0     0   \n",
       "14996                0.37                   143                      0     0   \n",
       "14997                0.11                   280                      0     0   \n",
       "14998                0.37                   158                      0     0   \n",
       "\n",
       "       low  medium  \n",
       "0        1       0  \n",
       "1        0       1  \n",
       "2        0       1  \n",
       "3        1       0  \n",
       "4        1       0  \n",
       "...    ...     ...  \n",
       "14994    1       0  \n",
       "14995    1       0  \n",
       "14996    1       0  \n",
       "14997    1       0  \n",
       "14998    1       0  \n",
       "\n",
       "[14999 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_connect.drop('salary',axis='columns',inplace=True)\n",
    "df_connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be57dafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
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       "      <th>low</th>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  high  \\\n",
       "0                    0.38                   157                      0     0   \n",
       "1                    0.80                   262                      0     0   \n",
       "2                    0.11                   272                      0     0   \n",
       "3                    0.72                   223                      0     0   \n",
       "4                    0.37                   159                      0     0   \n",
       "...                   ...                   ...                    ...   ...   \n",
       "14994                0.40                   151                      0     0   \n",
       "14995                0.37                   160                      0     0   \n",
       "14996                0.37                   143                      0     0   \n",
       "14997                0.11                   280                      0     0   \n",
       "14998                0.37                   158                      0     0   \n",
       "\n",
       "       low  medium  \n",
       "0        1       0  \n",
       "1        0       1  \n",
       "2        0       1  \n",
       "3        1       0  \n",
       "4        1       0  \n",
       "...    ...     ...  \n",
       "14994    1       0  \n",
       "14995    1       0  \n",
       "14996    1       0  \n",
       "14997    1       0  \n",
       "14998    1       0  \n",
       "\n",
       "[14999 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df_connect\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb73926c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df.left\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "07b537bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9effc63a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ccfa5a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea389ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ea817b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7886666666666666"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
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