Diabetes Prediction

Abstract

1. Introduction

In this project, our objective is to predict whether the patient has diabetes or not based on various features like Glucose level, Insulin, Age, BMI. We will perform all the steps from Data gathering to Model deployment. During Model evaluation, we compare various machine learning algorithms on the basis of accuracy_score metric and find the best one. Then we create a web app using Flask which is a python micro framework.

2. Objective

Predict Diabetes using Machine Learning.

This project demonstrates how Jupyter Notebook, Python, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Aditya-Mankar/Diabetes-Prediction.git
cd Diabetes-Prediction

1. Clone this repository and unzip it.
2. After downloading, cd into the flask directory.
3. Begin a new virtual environment with Python 3 and activate it.
4. Install the required packages using
5. Execute the command:
6. Open http://127.0.0.1:5000/ in your browser.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Aditya-Mankar and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.