GitHub - wyang36/supervised-learning-exp: expirimentation using various supervised learning method for heart disease prediction and posture prediction

wyang36 / supervised-learning-exp Public

Notifications You must be signed in to change notification settings
Fork 0
Star 0

expirimentation using various supervised learning method for heart disease prediction and posture prediction

Notifications

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
dataset		dataset
.gitattributes		.gitattributes
.gitignore		.gitignore
README.txt		README.txt
ann_heart.py		ann_heart.py
ann_mbti.py		ann_mbti.py
boost_heart.py		boost_heart.py
boost_mbti.py		boost_mbti.py
dt_heart.py		dt_heart.py
dt_mbti.py		dt_mbti.py
environment.yml		environment.yml
heart.py		heart.py
knn_heart.py		knn_heart.py
knn_mbti.py		knn_mbti.py
mbti.py		mbti.py
svm_heart.py		svm_heart.py
svm_mbti.py		svm_mbti.py

Repository files navigation

To run the code and reproduce the results for Assignment 1, do the following:
1. Download and unzip the files from: https://drive.google.com/file/d/1kRCITCrIVeIBXEOgEQ4N0cdCqcQuTS1W/view?usp=sharing
2. If not done so already, install miniconda (https://docs.conda.io/en/latest/miniconda.html) on your machine
3. cd into the directory
4. Create and activate the conda environment using:
	conda env create -f environment.yml
	conda activate ml
5. The python files are named as algorithm_dataset.py, example: dt_heart.py, dt_mbti.py. Each file is individually runnable. It will print the GridSearchCV results and final test results and export any plots to ./images folder. The plots used in the analysis are already present but running the code files will overwrite them. To run a code file:
	python dt_heart.py