The main goal of this work is to use the low level Tensorflow core API and build a deep learning regression model, to use the competitions dataset that has a lot of features and lots of missing values as well.
As this repository uses Tensorflow core API, it may seems a little too much code to people who is used the Keras or any other high level implementation framework.
- EDA and feature preprocessing. [link]
- Neural network model implementation. [link]
- Neural network methods implementation. [link]
- Model training and prediction. [link]
link for the Kaggle competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
datasets: https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.
- The feature extraction of this work needs an overall improvement.