Employee_Retention_Model.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
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       "      <td>medium</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
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       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  average_montly_hours  time_spend_company  Work_accident  left  promotion_last_5years Department  salary\n",
       "0                    0.38             0.53               2                   157                   3              0     1                      0      sales     low\n",
       "1                    0.80             0.86               5                   262                   6              0     1                      0      sales  medium\n",
       "2                    0.11             0.88               7                   272                   4              0     1                      0      sales  medium\n",
       "3                    0.72             0.87               5                   223                   5              0     1                      0      sales     low\n",
       "4                    0.37             0.52               2                   159                   3              0     1                      0      sales     low\n",
       "...                   ...              ...             ...                   ...                 ...            ...   ...                    ...        ...     ...\n",
       "14994                0.40             0.57               2                   151                   3              0     1                      0    support     low\n",
       "14995                0.37             0.48               2                   160                   3              0     1                      0    support     low\n",
       "14996                0.37             0.53               2                   143                   3              0     1                      0    support     low\n",
       "14997                0.11             0.96               6                   280                   4              0     1                      0    support     low\n",
       "14998                0.37             0.52               2                   158                   3              0     1                      0    support     low\n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "df=pd.read_csv('HR.csv') \n",
    "df \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0adab633-cb41-40ff-8453-47379a6e80ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'Department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d72adba1-975c-4ea1-8396-0fb98471df8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e58c3038-5023-4ee8-87fb-7df15a5a96bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features and target\n",
    "X = df.iloc[:, [0, 1, 2, 3, 4, 5, 7, 8, 9]]  # Select columns by index\n",
    "y = df['left']  # Select the target column\n",
    "\n",
    "# Split the data\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e073162a-2bab-4bbe-881c-ee5f7c384210",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
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       "      <th>7913</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.98</td>\n",
       "      <td>4</td>\n",
       "      <td>134</td>\n",
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       "      <th>2369</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.69</td>\n",
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       "      <td>245</td>\n",
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       "      <th>3093</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.79</td>\n",
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       "      <td>3</td>\n",
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       "      <td>low</td>\n",
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       "      <th>210</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.46</td>\n",
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       "      <td>156</td>\n",
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       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
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       "      <th>1639</th>\n",
       "      <td>0.42</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
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       "      <th>1379</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>154</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>management</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>9782</th>\n",
       "      <td>0.51</td>\n",
       "      <td>0.79</td>\n",
       "      <td>3</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11495</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.71</td>\n",
       "      <td>4</td>\n",
       "      <td>173</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.89</td>\n",
       "      <td>6</td>\n",
       "      <td>309</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>medium</td>\n",
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       "  </tbody>\n",
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       "<p>11999 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  average_montly_hours  time_spend_company  Work_accident  promotion_last_5years  Department  salary\n",
       "7913                 0.84             0.98               4                   134                   5              1                      0     support  medium\n",
       "2369                 0.48             0.69               4                   245                   3              0                      0  management  medium\n",
       "3093                 0.81             0.79               5                   135                   3              0                      0   technical     low\n",
       "210                  0.43             0.46               2                   156                   3              0                      0   technical     low\n",
       "1639                 0.42             0.46               2                   135                   3              0                      0   technical     low\n",
       "...                   ...              ...             ...                   ...                 ...            ...                    ...         ...     ...\n",
       "1379                 0.37             0.48               2                   154                   3              0                      0  management     low\n",
       "9782                 0.51             0.79               3                   137                   3              0                      0       sales  medium\n",
       "11495                0.77             0.75               3                   170                   7              0                      0       sales     low\n",
       "4935                 0.67             0.71               4                   173                   3              0                      0     support    high\n",
       "1040                 0.11             0.89               6                   309                   4              0                      0     support  medium\n",
       "\n",
       "[11999 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0159b808-9e57-49aa-a2da-7b542fb6a238",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
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       "  <tbody>\n",
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       "      <th>13226</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.56</td>\n",
       "      <td>3</td>\n",
       "      <td>257</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>marketing</td>\n",
       "      <td>medium</td>\n",
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       "    <tr>\n",
       "      <th>4233</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>high</td>\n",
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       "    <tr>\n",
       "      <th>6857</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.64</td>\n",
       "      <td>3</td>\n",
       "      <td>142</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11311</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.63</td>\n",
       "      <td>4</td>\n",
       "      <td>203</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>technical</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>5084</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4</td>\n",
       "      <td>141</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>medium</td>\n",
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       "      <th>13425</th>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5</td>\n",
       "      <td>264</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>accounting</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14016</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.65</td>\n",
       "      <td>3</td>\n",
       "      <td>175</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14396</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.46</td>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>hr</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.59</td>\n",
       "      <td>4</td>\n",
       "      <td>199</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RandD</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6205</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.96</td>\n",
       "      <td>4</td>\n",
       "      <td>267</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  average_montly_hours  time_spend_company  Work_accident  promotion_last_5years  Department  salary\n",
       "13226                0.64             0.56               3                   257                   6              0                      0   marketing  medium\n",
       "4233                 0.88             0.65               4                   224                   2              0                      0          hr    high\n",
       "6857                 0.49             0.64               3                   142                   4              0                      0          IT     low\n",
       "11311                0.24             0.63               4                   203                   5              0                      0   technical     low\n",
       "5084                 0.62             0.70               4                   141                   4              0                      0     support  medium\n",
       "...                   ...              ...             ...                   ...                 ...            ...                    ...         ...     ...\n",
       "13425                0.50             1.00               5                   264                   8              0                      1  accounting    high\n",
       "14016                0.44             0.65               3                   175                   2              0                      0          IT     low\n",
       "14396                0.40             0.46               2                   127                   3              0                      0          hr  medium\n",
       "2436                 0.75             0.59               4                   199                   2              0                      0       RandD     low\n",
       "6205                 0.55             0.96               4                   267                   3              0                      0       sales  medium\n",
       "\n",
       "[3000 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38b32a1a-857b-4257-963c-212dcce9fa32",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9bf2118d-07cf-4201-8167-8be043019e30",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "687c27bc-4cb7-414d-9d63-e2a1e3296be1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "satisfaction_level        float64\n",
      "last_evaluation           float64\n",
      "number_project              int64\n",
      "average_montly_hours        int64\n",
      "time_spend_company          int64\n",
      "Work_accident               int64\n",
      "promotion_last_5years       int64\n",
      "Department_RandD             bool\n",
      "Department_accounting        bool\n",
      "Department_hr                bool\n",
      "Department_management        bool\n",
      "Department_marketing         bool\n",
      "Department_product_mng       bool\n",
      "Department_sales             bool\n",
      "Department_support           bool\n",
      "Department_technical         bool\n",
      "salary_low                   bool\n",
      "salary_medium                bool\n",
      "dtype: object\n",
      "[0 0 0 ... 0 0 0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "categorical_columns = ['Department', 'salary']\n",
    "\n",
    "# One-hot encode categorical columns\n",
    "df = pd.get_dummies(df, columns=categorical_columns, drop_first=True)\n",
    "\n",
    "# Separate features and target\n",
    "X = df.drop('left', axis=1)  # Features\n",
    "y = df['left']  # Target\n",
    "\n",
    "# Split data into train and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Ensure all features are numeric\n",
    "print(x_train.dtypes)\n",
    "\n",
    "# Initialize and fit the logistic regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Predict on test data\n",
    "predictions = model.predict(x_test)\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa39b6df-f2f9-4864-9a88-cc15fa81e508",
   "metadata": {},
   "outputs": [
    {
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       "<p>3000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  average_montly_hours  ...  Department_support  Department_technical  salary_low  salary_medium\n",
       "6723                 0.65             0.96               5                   226  ...               False                 False       False           True\n",
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       "4679                 0.69             0.98               3                   214  ...               False                 False        True          False\n",
       "862                  0.41             0.47               2                   154  ...               False                 False        True          False\n",
       "7286                 0.87             0.76               5                   254  ...               False                 False        True          False\n",
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       "14346                0.31             0.84               7                   133  ...               False                  True        True          False\n",
       "\n",
       "[3000 rows x 18 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7cc176ce-d9b9-48d6-8a10-7ff1d1a2b2e2",
   "metadata": {},
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6872</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.86</td>\n",
       "      <td>4</td>\n",
       "      <td>112</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.93</td>\n",
       "      <td>7</td>\n",
       "      <td>284</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.96</td>\n",
       "      <td>4</td>\n",
       "      <td>246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13418</th>\n",
       "      <td>0.49</td>\n",
       "      <td>0.65</td>\n",
       "      <td>4</td>\n",
       "      <td>233</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5</td>\n",
       "      <td>249</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.79</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "      <td>218</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2</td>\n",
       "      <td>219</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11999 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  average_montly_hours  ...  Department_support  Department_technical  salary_low  salary_medium\n",
       "9838                 1.00             0.61               3                   188  ...               False                 False        True          False\n",
       "7689                 0.16             0.78               4                   196  ...               False                  True       False          False\n",
       "6557                 0.80             0.80               3                   175  ...               False                 False       False           True\n",
       "6872                 0.66             0.86               4                   112  ...               False                 False       False           True\n",
       "820                  0.11             0.93               7                   284  ...               False                  True        True          False\n",
       "...                   ...              ...             ...                   ...  ...                 ...                   ...         ...            ...\n",
       "5191                 0.52             0.96               4                   246  ...                True                 False        True          False\n",
       "13418                0.49             0.65               4                   233  ...               False                 False       False           True\n",
       "5390                 0.66             0.73               5                   249  ...                True                 False       False           True\n",
       "860                  0.79             1.00               4                   218  ...               False                 False        True          False\n",
       "7270                 0.98             0.86               2                   219  ...               False                 False        True          False\n",
       "\n",
       "[11999 rows x 18 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "704681a9-5541-41a2-a473-369e568b99db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.797"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5665215c-078e-40b5-a78b-e948bb242651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "78f4281a-60a4-421c-a788-2aaa5c7519f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6723     0\n",
       "6473     0\n",
       "4679     0\n",
       "862      1\n",
       "7286     0\n",
       "        ..\n",
       "3297     0\n",
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       "14346    1\n",
       "Name: left, Length: 3000, dtype: int64"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_test"
   ]
  }
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