house_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
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       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>8450</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>60</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
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       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1710</td>\n",
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       "    <tr>\n",
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       "      <td>181500</td>\n",
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       "      <td>223500</td>\n",
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       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1647</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2073</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2340</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1078</td>\n",
       "      <td>2</td>\n",
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       "      <td>142125</td>\n",
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       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1256</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>147500</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      GrLivArea  BedroomAbvGr  FullBath  SalePrice\n",
       "0          1710             3         2     208500\n",
       "1          1262             3         2     181500\n",
       "2          1786             3         2     223500\n",
       "3          1717             3         1     140000\n",
       "4          2198             4         2     250000\n",
       "...         ...           ...       ...        ...\n",
       "1455       1647             3         2     175000\n",
       "1456       2073             3         2     210000\n",
       "1457       2340             4         2     266500\n",
       "1458       1078             2         1     142125\n",
       "1459       1256             3         1     147500\n",
       "\n",
       "[1460 rows x 4 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select relevant columns\n",
    "df = data[['GrLivArea', 'BedroomAbvGr', 'FullBath', 'SalePrice']]\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<bound method NDFrame.head of       GrLivArea  BedroomAbvGr  FullBath\n",
       " 0          1710             3         2\n",
       " 1          1262             3         2\n",
       " 2          1786             3         2\n",
       " 3          1717             3         1\n",
       " 4          2198             4         2\n",
       " ...         ...           ...       ...\n",
       " 1455       1647             3         2\n",
       " 1456       2073             3         2\n",
       " 1457       2340             4         2\n",
       " 1458       1078             2         1\n",
       " 1459       1256             3         1\n",
       " \n",
       " [1460 rows x 3 columns]>,\n",
       " <bound method NDFrame.head of 0       208500\n",
       " 1       181500\n",
       " 2       223500\n",
       " 3       140000\n",
       " 4       250000\n",
       "          ...  \n",
       " 1455    175000\n",
       " 1456    210000\n",
       " 1457    266500\n",
       " 1458    142125\n",
       " 1459    147500\n",
       " Name: SalePrice, Length: 1460, dtype: int64>)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define X and y variables\n",
    "X = df[['GrLivArea', 'BedroomAbvGr', 'FullBath']]  # Features\n",
    "y = df['SalePrice']  # Target variable\n",
    "X.head, y.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\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": 62,
   "metadata": {},
   "outputs": [
    {
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       "  align-self: flex-start;\n",
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       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
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       "/* Serial-specific style estimator block */\n",
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       "*/\n",
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       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2806426667.25\n",
      "R-squared: 0.63\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean squared error and R-squared value\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f'Mean Squared Error: {mse:.2f}')\n",
    "print(f'R-squared: {r2:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot the results\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs Predicted Prices')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}