House_Prices_Predictions

Description:-

My project for the Machine learning course - DV2542. The main idea is to compare various ordinal encoding techniques for the ordinal categorical variables of the house prices dataset in terms of accuracies.

The dataset is collected from the Kaggle House Prices competition. Link:- https://www.kaggle.com/c/house-prices-advanced-regression-techniques/

The project code contains 3 more additional files:-

1. house_prices_nominal_features.txt - Lists the nominal categorical features of the above dataset.
2. house_prices_ordinal_features.txt - Lists the ordinal categorical features of the above dataset.
3. house_prices.py - The main file.

Pre-requisites:-

Numpy(1.13.3), Pandas(0.21.0), SciKit-learn(0.19.0), Matplotlib(2.1.2) and Category-encoders(1.3.0)

Installation:-

To install a pre-requisite, use:- pip install <package_name>.

License:-

This project is licensed under the MIT License - see the LICENSE.md file for details.