Chronic Kidney Disease (CKD) is a significant health condition requiring early diagnosis and risk management to prevent severe complications. This project focuses on employing and comparing 5 different machine learning (ML) models to classify patients as having CKD or not: XGBoosting, randomforest, SVM, Logistic Regression, KNN. Anx after applying five different ML algorithms, a prediction accuracy of 99.5% was achieved by XGBoosting.
- Install conda: https://www.anaconda.com/products/individual
- Create and activate environment:
conda env create -n data1030 -f environment.yaml
conda activate data1030
- In the Desktop directory, run the following command. A browser window will open displaying the contents of the directory, choose data1030.
jupyter notebook
Bowei Sun: [email protected]\
This project is licensed under the Apache 2.0 License - see the LICENSE file for details