House Price Prediction - Python, XGBoost, LightGBM

This project is created for Kaggle competition - House Prices: Advanced Regression Techniques. It uses ensembling of XGboost and LightGBM models to make predictions.

Data Sets

• Competition data set in Kaggle.

Frameworks/Libraries

• XGBoost
• LightGBM
• scikit-learn

Getting Started

These instructions will get you a brief idea on setting up the environment and running on your local machine for development and testing purposes.

Prerequisities

• python3.5 or newer
• XGBoost
• LightGBM
• scikit-learn
• numpy
• pandas
• seaborn
• matplotlib
• statsmodels

Setup and running tests

1. Run python -V to check the installation

2. Install all the required libraries.

3. Execute the following commands from terminal to run the tests:

   python main.py

Note: Submission score is 0.12544 LB. Further improvement is definitely possible.