UCSD ECE Group 19 Project: Prediction and Analysis of Heart Failure
Requires:
- conda
- python 3.9.5
- pandas
- numpy
- jupyter notebook
- sklearn
- seaborn
- plotly
Clone the repository using
git clone https://github.com/rskpdev/ECE-143-Project.git
Create a conda environment from the environment.yml file. The first line of the .yml file sets the new environment's name
conda env create -f environment.yml
Activate the conda environment
conda activate 143_env
Deactivate when done making changes
conda deactivate
The data we used is stored in data folder, and the Machine Learning model scripts are stored in model folder.
EDA of the features from dataset are stored in the notebook in notebooks folder.
View notebook here jupyter nbviewer
Machine Learning of the features from dataset are stored in the notebook in model folder.
View notebook here jupyter nbviewer
- PCA.py
This file is used for performing PCA to extract the top 2 features for visualization. - model.py
This file consists of all the models used in prediction, plotting confusion matrix and calculating the metric scores. - encoder.py
this file encodes the categorical features in dataset. - split_dataset.py
This file splits the dataset into testing and training sets