Build Regression Models in Python for House Price Prediction

Built a Linear Regression model to predict house prices based on key features like area and bedrooms.

Utilized Recursive Feature Elimination (RFE) to select the most important features for accurate predictions.

Scaled numerical features using MinMaxScaler for consistent input data.

Achieved a high R-squared score, indicating strong model performance.

Detected and handled outliers in the data to improve prediction accuracy.

Conducted thorough residual analysis to evaluate and refine the model's performance.

Helps real estate professionals assess property values based on key features, aiding buyers, sellers, and investors.

Assists investors in making data-driven decisions about potential investments by predicting future house prices.

Real estate developers and agents can use predictions to price properties more effectively, ensuring competitive offers.