Upload test.py
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test.py
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# house_price_prediction.py
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import pandas as pd
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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
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import joblib
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# Generate some sample data
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data = {'Size': [1400, 1600, 1700, 1875, 1100, 1550, 2350, 2450, 1425, 1700],
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'Price': [245000, 312000, 279000, 308000, 199000, 219000, 405000, 324000, 319000, 255000]}
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df = pd.DataFrame(data)
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# Split the data into features (X) and target variable (y)
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X = df[['Size']]
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y = df['Price']
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# Split the data into training and testing sets
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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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# Train a linear regression model
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Make predictions on the test set
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y_pred = model.predict(X_test)
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# Evaluate the model
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mse = mean_squared_error(y_test, y_pred)
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print(f'Mean Squared Error: {mse}')
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# Save the model
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joblib.dump(model, 'house_price_model.joblib')
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