2. housing_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from pandas) (2.2.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from pandas) (2025.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from pandas) (2025.1)\n",
      "Requirement already satisfied: six>=1.5 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: scikit-learn in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (1.6.1)\n",
      "Requirement already satisfied: numpy>=1.19.5 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from scikit-learn) (2.2.2)\n",
      "Requirement already satisfied: scipy>=1.6.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from scikit-learn) (1.15.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from scikit-learn) (3.5.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install -U scikit-learn\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_california_housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>0.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  target  \n",
       "0        -122.23   4.526  \n",
       "1        -122.22   3.585  \n",
       "2        -122.24   3.521  \n",
       "3        -122.25   3.413  \n",
       "4        -122.25   3.422  \n",
       "...          ...     ...  \n",
       "20635    -121.09   0.781  \n",
       "20636    -121.21   0.771  \n",
       "20637    -121.22   0.923  \n",
       "20638    -121.32   0.847  \n",
       "20639    -121.24   0.894  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = fetch_california_housing()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>0.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  target  \n",
       "0        -122.23   4.526  \n",
       "1        -122.22   3.585  \n",
       "2        -122.24   3.521  \n",
       "3        -122.25   3.413  \n",
       "4        -122.25   3.422  \n",
       "...          ...     ...  \n",
       "20635    -121.09   0.781  \n",
       "20636    -121.21   0.771  \n",
       "20637    -121.22   0.923  \n",
       "20638    -121.32   0.847  \n",
       "20639    -121.24   0.894  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=['MedInc', 'AveRooms'])\n",
    "y = df['target']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.477  , 0.458  , 5.00001, ..., 5.00001, 0.723  , 1.515  ],\n",
       "      shape=(4128,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (3.10.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (4.56.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (1.4.8)\n",
      "Requirement already satisfied: numpy>=1.23 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (2.2.2)\n",
      "Requirement already satisfied: packaging>=20.0 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (11.1.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (3.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from matplotlib) (2.9.0.post0)\n",
      "Requirement already satisfied: six>=1.5 in e:\\github\\ml practiacal\\.venv\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pip install matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "metrics = ['MAE', 'MSE', 'RMSE', 'R2']\n",
    "values = [mae, mse, rmse, r2]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(metrics, values, marker='o', linestyle='-', color='b')  # Line plot with markers\n",
    "plt.title('Comparison of Metrics')\n",
    "plt.xlabel('Metrics')\n",
    "plt.ylabel('Values')\n",
    "plt.yscale('log')  # Use logarithmic scale for better visualization of small values\n",
    "plt.grid(True, which=\"both\", ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 8.045620586642819e-16\n",
      "MSE: 5.0089332188901876e-30\n",
      "RMSE: 2.23806461454762e-15\n",
      "R2: 1.0\n"
     ]
    }
   ],
   "source": [
    "print(f'MAE: {mae}')\n",
    "print(f'MSE: {mse}')\n",
    "print(f'RMSE: {rmse}')\n",
    "print(f'R2: {r2}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}