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# -*- 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}') |