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"""enunch.159 |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1hjXMe2PUvW0yL5RwWXem47vZgBENHq-6 |
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""" |
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import warnings |
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warnings.filterwarnings('ignore') |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn.model_selection import train_test_split |
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from sklearn.linear_model import LinearRegression |
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from sklearn.metrics import mean_squared_error, r2_score |
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file_path = '/content/House Price India.csv' |
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df = pd.read_csv(file_path) |
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df.head() |
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df.isnull().sum() |
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df['Date'] = pd.to_datetime(df['Date'], origin='1899-12-30', unit='D') |
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df.describe() |
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numeric_df = df.select_dtypes(include=[np.number]) |
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plt.figure(figsize=(15, 10)) |
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sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm') |
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plt.title('Correlation Heatmap') |
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plt.show() |
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X = numeric_df.drop(columns=['Price']) |
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y = numeric_df['Price'] |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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model = LinearRegression() |
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model.fit(X_train, y_train) |
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y_pred = model.predict(X_test) |
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mse = mean_squared_error(y_test, y_pred) |
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r2 = r2_score(y_test, y_pred) |
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print(f'Mean Squared Error: {mse}') |
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print(f'R-squared: {r2}') |
