Diabetes Prediction - Streamlit App

Overview

This project is a Diabetes Prediction Tool built using Streamlit. It allows users to input health-related parameters and predicts the likelihood of diabetes using a pre-trained machine learning model.

Features

User-friendly interface to input health metrics.

Predicts diabetes likelihood based on user input.

Data preprocessing and feature scaling.

Model trained using Scikit-learn.

Interactive visualizations for data insights.

Tech Stack

Python (3.9.6)

Streamlit (Web App Framework)

Scikit-learn (1.3.0) – for ML model

Joblib (1.4.2) – for model serialization

Numpy (1.26.4)

Pandas, Matplotlib, Seaborn (for data analysis & visualization) Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/your-repo.git

cd your-repo

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run Locally streamlit run src/app.py

Deployment on Streamlit Cloud

Push your code to GitHub.

Go to Streamlit Cloud and connect your repo.

Ensure requirements.txt and runtime.txt are present.

Deploy and monitor logs for any errors.

Troubleshooting

If the app runs locally but fails on Streamlit Cloud:

Ensure dependencies are correctly listed in requirements.txt.

Check for OS compatibility issues (Windows vs. Mac).

Add runtime.txt with the correct Python version (python-3.9.6).

Review logs for missing packages or path errors.