SBU-CSE-523-524-Advanced-Project

This repository contains code related to my Masters' advanced project on Missing Data Imputation using GANs.

Installation

This project is implemented in python 3.7 and tensorflow 1.x. Follow these steps to setup your environment:

Download and install Conda
After Anaconda has been installed, open up the terminal (Unix) or Anaconda prompt (Windows). Create a new environment where Tensorflow and related dependencies are installed.
```
conda create --name tf-env
```
Activate the new tf-env environment.
```
conda activate tf-env
```
Run your Python IDE from this environment, for example:
```
jupyter notebook
```
or
```
spyder
```
Open main.ipynb and mention the dataset among the 3 datasets - college, spam, letter for which you want to run the GAIN algorithm. A csv will be generated for the imputed data which we can use for analysis.
For visualizing the imputation accuracy thrugh various plots, run visualisation.ipynb.

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
data		data
README.md		README.md
data_loader.ipynb		data_loader.ipynb
gain.ipynb		gain.ipynb
gain_data.zip		gain_data.zip
hyperparams_tuning.ipynb		hyperparams_tuning.ipynb
main.ipynb		main.ipynb
utils.ipynb		utils.ipynb
visualisation.ipynb		visualisation.ipynb