Diabetes Prediction from Health Data

What you'll build

This project provides a comprehensive, hands-on introduction to a real-world machine learning classification problem. You will build a model to predict the likelihood of a patient having diabetes based on key health indicators from the PIMA Indians Diabetes Database. The project goes beyond simple model training; it's designed to be a portfolio-worthy piece that demonstrates end-to-end machine learning skills. You will start with data exploration and cleaning, train multiple algorithms, rigorously evaluate them, and interpret their decisions. As an enhancement to make this project stand out and showcase practical application, the roadmap includes steps to build a prediction pipeline, deploy the final model as a simple REST API, and even create a basic web interface to interact with it. This transforms a data science exercise into a tangible product, simulating a full project lifecycle.

What you'll learn

Data preprocessing for tabular data.
Implementation of various classical classification algorithms.
Model evaluation for health-related classification tasks.
Feature importance and model interpretability.

Roadmap

12 steps · 98 tasks

Diabetes Prediction from Health Data

Setup & Data Acquisition

Exploratory Data Analysis

Data Cleaning & Preprocessing

Baseline Model Training

Baseline Model Evaluation

Advanced Model Training

Hyperparameter Tuning

Model Interpretation

Build a Prediction Pipeline

Build Prediction API

Create Basic UI

Documentation & Wrap-up