🩺 Diabetes Prediction Web App using Machine Learning

📌 Project Overview

This project is an end-to-end Machine Learning application that predicts whether a person is diabetic or not based on key medical attributes.

The solution integrates:

• A trained Machine Learning model
• A user-friendly web interface built using Streamlit
• Real-time prediction system

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🎯 Objective

To develop a predictive system that:

• Analyzes patient health data
• Predicts diabetes risk
• Provides an easy-to-use interface for users

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🧠 How It Works

1. User enters medical details in the web app

2. Input data is converted into NumPy array

3. Data is reshaped for model compatibility

4. Pre-trained ML model makes prediction

5. Result is displayed as:

   • ✅ Not Diabetic
   • ⚠️ Diabetic

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📊 Input Features

The model uses the following medical parameters:

• Number of Pregnancies
• Glucose Level
• Blood Pressure
• Skin Thickness
• Insulin Level
• Body Mass Index (BMI)
• Diabetes Pedigree Function
• Age

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⚙️ Tech Stack

💻 Programming

• Python

📚 Libraries Used

• NumPy
• Pandas
• Scikit-learn
• Pickle

🌐 Web Framework

• Streamlit

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🔄 Project Workflow

1. Data Collection (Diabetes dataset)
2. Data Preprocessing
3. Feature Selection
4. Model Training
5. Model Evaluation
6. Model Saving using Pickle
7. Web App Development using Streamlit

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🤖 Machine Learning Model

A classification model is trained to predict diabetes outcome based on input features.

The trained model is saved as:

training model.sav

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🖥️ Application Interface

The Streamlit app provides:

• Input fields for medical data
• Prediction button
• Instant result display

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🚀 How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/thejasdev/Diabites_ML_Project.git
cd Diabites_ML_Project

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Run the Application

streamlit run frontend.py

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📁 Project Structure

Diabetes-Prediction-ML-Project/
│
├── app/
│   └── frontend.py              # Streamlit app
│
├── model/
│   └── training_model.sav       # Trained ML model
│
├── data/
│   └── diabetes.csv             # Dataset
│
├── notebooks/
│   └── ml.ipynb                 # Training notebook
│
├── requirements.txt
├── README.md
└── .gitignore

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📈 Expected Output

• "The Person is Not Diabetic"
• "The Person is Diabetic"

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💡 Future Improvements

• Improve UI/UX design
• Add data validation
• Deploy on cloud (AWS / Render / Streamlit Cloud)
• Add multiple disease prediction
• Improve model accuracy with advanced algorithms

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📚 Learning Outcomes

• End-to-end Machine Learning pipeline
• Model deployment using Streamlit
• Handling real-world medical dataset
• Building interactive web applications

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👨‍💻 Author

Thejas K S

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub!