main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cement</th>\n",
       "      <th>blast_furnace_slag</th>\n",
       "      <th>fly_ash</th>\n",
       "      <th>water</th>\n",
       "      <th>superplasticizer</th>\n",
       "      <th>coarse_aggregate</th>\n",
       "      <th>fine_aggregate</th>\n",
       "      <th>age</th>\n",
       "      <th>concrete_compressive_strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>79.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>540.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>28</td>\n",
       "      <td>61.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>270</td>\n",
       "      <td>40.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>332.5</td>\n",
       "      <td>142.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>365</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>198.6</td>\n",
       "      <td>132.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.4</td>\n",
       "      <td>825.5</td>\n",
       "      <td>360</td>\n",
       "      <td>44.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cement  blast_furnace_slag  fly_ash  water  superplasticizer  \\\n",
       "0   540.0                 0.0      0.0  162.0               2.5   \n",
       "1   540.0                 0.0      0.0  162.0               2.5   \n",
       "2   332.5               142.5      0.0  228.0               0.0   \n",
       "3   332.5               142.5      0.0  228.0               0.0   \n",
       "4   198.6               132.4      0.0  192.0               0.0   \n",
       "\n",
       "   coarse_aggregate  fine_aggregate   age  concrete_compressive_strength  \n",
       "0            1040.0            676.0   28                          79.99  \n",
       "1            1055.0            676.0   28                          61.89  \n",
       "2             932.0            594.0  270                          40.27  \n",
       "3             932.0            594.0  365                          41.05  \n",
       "4             978.4            825.5  360                          44.30  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('concrete_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = data.drop(columns = ['concrete_compressive_strength'])\n",
    "y_data = data['concrete_compressive_strength']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the training data: (824, 8)\n",
      "Length of the test data: 206\n"
     ]
    }
   ],
   "source": [
    "shape = x_train.shape\n",
    "length = len(y_test)\n",
    "\n",
    "print(f\"Shape of the training data: {shape}\")\n",
    "print(f\"Length of the test data: {length}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for Random Forest: 32.930074369520945\n",
      "R2 Score for Random Forest: 0.8797213840621924\n"
     ]
    }
   ],
   "source": [
    "rf_regressor = RandomForestRegressor(n_estimators = 100, max_depth = 10, random_state = 50)\n",
    "rf_regressor.fit(x_train, y_train)\n",
    "\n",
    "y_predicted_rf = rf_regressor.predict(x_test)\n",
    "mse_rf = mean_squared_error(y_test, y_predicted_rf)\n",
    "r2_rf = r2_score(y_test, y_predicted_rf)\n",
    "\n",
    "print(f\"Mean Squared Error for Random Forest: {mse_rf}\")\n",
    "print(f\"R2 Score for Random Forest: {r2_rf}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error for XGBoost: 28.735790296694898\n",
      "R2 Score for XGBoost: 0.8950412001509298\n"
     ]
    }
   ],
   "source": [
    "xgb_regressor = xgb.XGBRegressor(n_estimators = 100, learning_rate = 0.1, max_depth = 5, random_state = 50)\n",
    "xgb_regressor.fit(x_train, y_train)\n",
    "\n",
    "y_predicted_xgb = xgb_regressor.predict(x_test)\n",
    "mse_xgb = mean_squared_error(y_test, y_predicted_xgb)\n",
    "r2_xgb = r2_score(y_test, y_predicted_xgb)\n",
    "\n",
    "print(f\"Mean Squared Error for XGBoost: {mse_xgb}\")\n",
    "print(f\"R2 Score for XGBoost: {r2_xgb}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_rf = 0.1\n",
    "w_xgb = 0.9\n",
    "\n",
    "y_predicted = (w_rf * y_predicted_rf) + (w_xgb * y_predicted_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 28.68569913709445\n",
      "R2 Score: 0.8952241604224389\n"
     ]
    }
   ],
   "source": [
    "mse_final = mean_squared_error(y_test, y_predicted)\n",
    "r2_final = r2_score(y_test, y_predicted)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse_final}\")\n",
    "print(f\"R2 Score: {r2_final}\")"
   ]
  }
 ],
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