call_linear_function.py

# call_regression_function.py

from linear_regression import train_polynomial_regression, load_trained_model, predict_price
import pandas as pd

class LinearRegression:

    def predict(area, bed, bath, wm, dtcen, hw, ac, pk, se):
        # Replace 'your_data.csv' with the actual path to your CSV file
        file_path = 'Housing_data_final.csv'

        # Replace with the features you want to use
        num_vars = ['area', 'bedrooms', 'bathrooms', 'washingmachine','dtcenter', 'hotwater', 'ac', 'parking', 'security']

        # Train the model and save it (uncomment the following line if you haven't trained the model yet)
        model, poly = train_polynomial_regression(file_path, num_vars, save_model_path='trained_model.pkl')

        # Load the trained model and polynomial features
        model, poly = load_trained_model(
        'trained_model.pkl', 'trained_model_poly.pkl')
        
        # User input for feature values
        user_input = [area, bed, bath, wm, dtcen, hw, ac, pk, se]
        print(user_input)

        # Create a DataFrame for user input
        user_df = pd.DataFrame([user_input])

        # Predict the price for the user input
        predicted_price = predict_price(model, poly, user_df)

        return(round(predicted_price))