Predict House Pricing Date using Liner Regression and Python

house_prices_prediction_linear_regression.py

"""
This script utilizes machine learning techniques to predict house prices based on various input features.
It performs data preprocessing, splits the data into training and testing sets, trains a linear regression model,
evaluates its performance using  mean squared error (MSE) and R-squared.
"""

import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Load the data
data = pd.read_csv('data/houseprices.csv')

# One-hot encode the location feature
encoder = OneHotEncoder()
location_encoded = encoder.fit_transform(data[['location']]).toarray()

# Combine encoded location with other features
X = np.hstack((data[['area', 'bedrooms', 'total_living_area', 'house_age_in_years']].to_numpy(), location_encoded))
y = data['price'].to_numpy()

# 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)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
predictions = model.predict(X_test)

mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print('Mean Squared Error:', mse)
print('R-squared:', r2)

houseprices.csv