Upload enunch_159.py

enunch_159.py

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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}')