house-prices-advanced-regression-techniques

In this project a dataset was given which had description of house as features and price of house as target feature. First data preprocessing was done by filling null values, applying different encoding techniques, and selecting best features. After data preprocessing, I applied different regressor algorithms and neural networks to train my model.

1. The files are notebook files so jupyter notebook IDE or Dataspell IDE is required.
2. You can install the dataset from this link. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data

It was a kaggle compitition project. I took a little help from stackoverflow and chatgpt.

By better data preprocessing and model tuning, you can achieve better accuracy and less loss.