# -*- coding: utf-8 -*-
"""enunch.159

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1hjXMe2PUvW0yL5RwWXem47vZgBENHq-6
"""

import warnings
warnings.filterwarnings('ignore')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

file_path = '/content/House Price India.csv'
df = pd.read_csv(file_path)

df.head()

df.isnull().sum()

df['Date'] = pd.to_datetime(df['Date'], origin='1899-12-30', unit='D')

df.describe()

numeric_df = df.select_dtypes(include=[np.number])
plt.figure(figsize=(15, 10))
sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

X = numeric_df.drop(columns=['Price'])
y = numeric_df['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
print(f'R-squared: {r2}')