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Diabetes Risk Prediction using Machine Learning

This project uses four different machine learning algorithms to evaluate a dataset from kaggle. The algorithms used include:

  1. Logistic regression
  2. Naive Bayes
  3. Decision tree
  4. KNN

Steps carried out in the building of this project:

  1. Exploratory data analysis
  2. Datatase splitting (Train test set)
  3. Model(s) building
  4. Model(s) evaluation
  5. Model(s) validation

Paper writeup documentation

Google docs

Contributors

Akasukpe Karo https://www.linkedin.com/in/oghenekaro-akasukpe/

Copyright

Copyright (c) 2024 AkasukpeKaro. All rights reserved

About

This project uses four algorithms (Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors) in predicting diabetes risk and susceptibility. The outcomes showed that the Logistic Regression algorithm had the highest accuracy in predicting diabetes using the diabetes dataset obtained from Kaggle.

Topics

diabetes-predictionhealthcare-machine-learningmachine-learningpandasscikit-learn

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Readme
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