House Price Prediction - Regression

The objecive of this project is to predict house price from different features. The dataset includes 1460 instances and 80 features. The following algorithms are applied as on selected features from the data:

Applied Algorithms:

  • Linear Regression

  • Decision Tree

  • SVM

  • Random Forest

  • AdaBoost

  • GradientBoost

  • XGBoost

  • Feature selection is performed using Parson Correlation.

  • Feature imputation, encoding, and scaling is performed.

  • The best performance achieved is R-square = 0.90 with GradientBoost.

This Project’s GitHub Repository