Upload test.py

test.py

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+# house_price_prediction.py
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import joblib
+
+# Generate some sample data
+data = {'Size': [1400, 1600, 1700, 1875, 1100, 1550, 2350, 2450, 1425, 1700],
+        'Price': [245000, 312000, 279000, 308000, 199000, 219000, 405000, 324000, 319000, 255000]}
+
+df = pd.DataFrame(data)
+
+# Split the data into features (X) and target variable (y)
+X = df[['Size']]
+y = df['Price']
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a linear regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = model.predict(X_test)
+
+# Evaluate the model
+mse = mean_squared_error(y_test, y_pred)
+print(f'Mean Squared Error: {mse}')
+
+# Save the model
+joblib.dump(model, 'house_price_model.joblib')