Classification/Notebooks/Random_Forest_Tree.ipynb

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    "# Random Forest\n",
    "## Introduction \n",
    "Random Forest is an **ensemble learning technique** that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting. It is used for both **classification** and **regression** tasks.\n",
    "## How Random Forest Works?\n",
    "- It creates multiple Decision Trees on random subsets of data.\n",
    "- Each tree makes a prediction.\n",
    "- The final output is determined by:\n",
    "    -   Majority voting (for classification).\n",
    "    - Averaging predictions (for regression).\n",
    "\n",
    "## Advantages of Random Forest\n",
    "- Handles missing values and noisy data.\n",
    "- Reduces overfitting (compared to a single decision tree).\n",
    "- Works well with both categorical and numerical features.\n",
    "- Can be used for feature importance analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "Predict whether an employee will leave the company based on various attributes like salary, work-life balance, job satisfaction, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>EmployeeNumber</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "      <td>Sales</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
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       "      <td>37</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Other</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
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       "      <td>7</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
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      "text/plain": [
       "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1   49        No  Travel_Frequently        279  Research & Development   \n",
       "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3   33        No  Travel_Frequently       1392  Research & Development   \n",
       "4   27        No      Travel_Rarely        591  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                 1          2  Life Sciences              1               1   \n",
       "1                 8          1  Life Sciences              1               2   \n",
       "2                 2          2          Other              1               4   \n",
       "3                 3          4  Life Sciences              1               5   \n",
       "4                 2          1        Medical              1               7   \n",
       "\n",
       "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
       "0  ...                         1            80                 0   \n",
       "1  ...                         4            80                 1   \n",
       "2  ...                         2            80                 0   \n",
       "3  ...                         3            80                 0   \n",
       "4  ...                         4            80                 1   \n",
       "\n",
       "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
       "0                  8                      0               1               6   \n",
       "1                 10                      3               3              10   \n",
       "2                  7                      3               3               0   \n",
       "3                  8                      3               3               8   \n",
       "4                  6                      3               3               2   \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",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Datasets/HR-Employee-Attrition.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert categorical features to numerical using Label encoding\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label = LabelEncoder()\n",
    "\n",
    "categorical_col = df.select_dtypes(include='object').columns\n",
    "\n",
    "for col in categorical_col:\n",
    "    df[col] = label.fit_transform(df[col])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Attrition',axis= 1)\n",
    "y = df['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# Split dataset \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Perform Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the parameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300], # Number of trees \n",
    "    'max_depth': [4,6,8], # Maximum depth of the tree\n",
    "    'criterion': ['gini', 'entropy'] # criteria for split\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# Initialize the model\n",
    "rf_model = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters:  {'criterion': 'gini', 'max_depth': 8, 'n_estimators': 200}\n"
     ]
    }
   ],
   "source": [
    "# Perform the grid search over the parameters\n",
    "GridSearch = GridSearchCV(rf_model, param_grid, cv=5)\n",
    "GridSearch.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters\n",
    "print(\"Best Parameters: \", GridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Train Random Forest Model with Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  display: flex;\n",
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       "  float: left;\n",
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       "\n",
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       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=8, n_estimators=200, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=8, n_estimators=200, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=8, n_estimators=200, random_state=42)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Train decision Tree Model\n",
    "best_rf = RandomForestClassifier(n_estimators= GridSearch.best_params_['n_estimators'], criterion= GridSearch.best_params_['criterion'] , max_depth= GridSearch.best_params_['max_depth'], \n",
    "                                  random_state=42)\n",
    "best_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Evaluate Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Make prediction\n",
    "y_pred = best_rf.predict(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.88\n",
      "\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      1.00      0.93       255\n",
      "           1       0.80      0.10      0.18        39\n",
      "\n",
      "    accuracy                           0.88       294\n",
      "   macro avg       0.84      0.55      0.56       294\n",
      "weighted avg       0.87      0.88      0.83       294\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "# Accuracy and Classification Report\n",
    "accuracy = best_rf.score(X_test, y_test)\n",
    "print(f\"Model accuracy: {accuracy:.2f}\")\n",
    "print(\"\\nClassification Report: \\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get feature importance\n",
    "feature_importance = pd.DataFrame({'Feature': X.columns, 'Importance': best_rf.feature_importances_})\n",
    "feature_importance = feature_importance.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# Plot feature Importance\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x='Importance', y='Feature',hue='Feature', data=feature_importance,palette='coolwarm', legend=False)\n",
    "plt.title('Feature Importance')\n",
    "plt.savefig('Diagrams/Feature_importance_Random_Forest.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7: Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the trained model and feature order\n",
    "with open('Models/random_forest_HR_data.pkl', 'wb') as file:\n",
    "    pickle.dump((best_rf, list(X.columns)), file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}