House price prediction model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec3af8f1-8160-4753-b137-b78675da5707",
   "metadata": {},
   "source": [
    "### Problem Statement :\n",
    "Implement a linear regression model to predict the prices of houses based on their square footage and the number of bedrooms and bathrooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6e27b21-01dc-41dd-b82b-eb39c4e1ee5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=UserWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2475e8f-37cc-41bd-8a85-bee8c37483f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4c43a7a-c629-4f89-bae5-76e59016cf3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfca6c3b-b6e0-4352-9ac6-d0b26732caf3",
   "metadata": {},
   "source": [
    "###  Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e98c014-73da-4809-9c01-30fabe357128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "stories             0\n",
       "mainroad            0\n",
       "guestroom           0\n",
       "basement            0\n",
       "hotwaterheating     0\n",
       "airconditioning     0\n",
       "parking             0\n",
       "prefarea            0\n",
       "furnishingstatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdf6f661-94bc-4fb7-8229-84fdc99c489f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa2fc59e-300f-41e0-953e-20c919878f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9994b3c7-b265-4082-952c-e1049ceee16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e65cfdb-e899-450e-9245-737d985b516c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['area', 'bedrooms', 'bathrooms']]\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df84924c-0d71-4f61-8b8a-841814e8ebb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb1afef-35de-46a3-9b4e-4ce341ef3210",
   "metadata": {},
   "source": [
    "### Linear Regression Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f40972c-31f2-4ca4-ab3b-995f34b6ed30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\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: 1ex;\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>LinearRegression()</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 fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7628f146-def2-4a9c-91d8-017ac423b487",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75471827-a4b2-43fe-aa61-15af2afccfad",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "944b176a-b372-4811-a441-289ba9b95d8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2750040479309.0513\n",
      "R^2 Score: 0.45592991188724474\n"
     ]
    }
   ],
   "source": [
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'R^2 Score: {r2}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6626a6bb-b48b-4f6f-b80c-0d018b39f5be",
   "metadata": {},
   "source": [
    "### Prediction Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "866e9430-cd90-422a-84fc-de6e38e9a6ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Features: {'area': 5900, 'bedrooms': 4, 'bathrooms': 2}\n",
      "Actual Price: 4060000\n",
      "Predicted Price: 6383168.06\n"
     ]
    }
   ],
   "source": [
    "sample = X_test.iloc[0]\n",
    "actual_price = y_test.iloc[0]\n",
    "predicted_price = model.predict([sample])[0]\n",
    "\n",
    "# Print the actual and predicted prices\n",
    "print('Sample Features:', sample.to_dict())\n",
    "print('Actual Price:', actual_price)\n",
    "print(f'Predicted Price: {predicted_price:.2f}') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02913443-b7b8-464d-90cb-80910fd93244",
   "metadata": {},
   "source": [
    "### Result Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b75fc437-dbfb-4f51-8242-8f013f4a57ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the actual vs predicted prices\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.scatterplot(x=y_test, y=y_pred)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs Predicted Prices')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b412d275-08ab-47e5-8a2f-62ac6deffa1c",
   "metadata": {},
   "source": [
    "The model shows moderate performance with an R-squared value of about 0.46. \n",
    "This example shows that the model's prediction is in the same ballpark as the actual price, but there's still room for improvement. The prediction is off by about 54%, which aligns with our earlier R-squared value of 0.46. Adding more features or adopting more complex models could enhance the accuracy of predictions, particularly for houses at the higher end of the market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15caa3f6-e9ee-4323-93c7-0225eeccf3e4",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}