Project information

• Tech Stack: NumPy, Matplotlib, Seaborn, Skit-Learn, XGBoost, Random Forest, Gradient Boosting, Extra Tree Regressor

Trained model based on the given data to predict the house price accurately and is evaluated by the Kaggle leader board.

Performed Exploratory Data Analysis, Feature Preprocessing, Benchmark Modelling and Model Improvement.

Random Forest regression and default parameter in Scikit-learn package are selected and used for benchmark.

Model metrics are tuned to improve performance and Stacking generalization technique is used for final output.