XGBoosting (Boston Housing Prediction).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9e42a1aa-ea1d-4110-8d84-b2b3d3fa118c",
   "metadata": {},
   "source": [
    "# XGBoost\n",
    "XGBoost is a machine learning library that implements gradient boosting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2cf6ac1-ee36-44d2-b08d-6ea3fc2981cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4c36313-9d5b-4b4b-a2bc-b9bbc9518229",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eedc0c42-5a83-431d-ada2-dffb0e109018",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['PRICE'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d76c00c-b6a3-46e8-a54e-62f6cee334f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea7e787f-b14e-4575-b16f-5151f4839ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = data.iloc[:,:-1], data.iloc[:,-1]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d988c2e2-adc3-4430-b55f-b5be711f793c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "1    21.6\n",
       "2    34.7\n",
       "3    33.4\n",
       "4    36.2\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b81be4c7-33a8-4da9-b55d-a2232e1b645d",
   "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=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2c47bfe6-9bb5-4bf0-9400-ce10bb6575ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "xg_reg = xgb.XGBRegressor(\n",
    "    n_estimators=1000, \n",
    "    max_depth=7, \n",
    "    eta=0.1, \n",
    "    subsample=0.7, \n",
    "    colsample_bytree=0.8\n",
    ")\n",
    "    \n",
    "# Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree. exact tree method requires non-zero value.\n",
    "# L1 regularization term on weights. Increasing this value will make model more conservative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a0bfb6b-4a3b-4d29-b776-94bebb681761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.8, enable_categorical=False,\n",
       "             eta=0.1, gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.100000001,\n",
       "             max_delta_step=0, max_depth=7, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=1000, n_jobs=8,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=0.7,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da547e8c-7630-4c9a-aa03-727160d44a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Param</th>\n",
       "      <th>XGBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAE</td>\n",
       "      <td>2.265729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSE</td>\n",
       "      <td>11.495042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RMSE</td>\n",
       "      <td>3.390434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R2</td>\n",
       "      <td>0.857784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Param    XGBoost\n",
       "0   MAE   2.265729\n",
       "1   MSE  11.495042\n",
       "2  RMSE   3.390434\n",
       "3    R2   0.857784"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = xg_reg.predict(X_test)\n",
    "\n",
    "mae = mean_absolute_error(y_test, preds)\n",
    "mse = mean_squared_error(y_test, preds)\n",
    "rmse = np.sqrt(mse)\n",
    "r2_score = r2_score(y_test, preds)\n",
    "\n",
    "pd.DataFrame({\n",
    "        'Param': ['MAE', 'MSE', 'RMSE', 'R2'], \n",
    "        'XGBoost': [mae, mse, rmse, r2_score]\n",
    "})"
   ]
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   "execution_count": 11,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(preds, y_test)\n",
    "plt.title('XGBoost')\n",
    "plt.xlabel(\"Prediction\")\n",
    "plt.ylabel(\"True\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fe963cf-6743-4934-8fb6-847fc4715aa8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c022cc7-1cb6-4776-9087-4da608cf2ed0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}