Diabetes Prediction Model

Overview

This project is a Diabetes Prediction Model that utilizes various classification algorithms for prediction, with the final selection based on Support Vector Machine (SVM). The model has been optimized using SMOTE for resampling and GridSearchCV for hyperparameter tuning.

Features

• Dataset Included: The project comes with a preprocessed dataset for training and testing.
• All Models Included: Multiple classification models are tested for performance.
• Hyperparameter Tuning: GridSearchCV is used to find the best parameters.
• Easy-to-Use UI: A user-friendly web interface built with Flask.

Models Tested

The following classification models were tested:

• K-Nearest Neighbors (KNN)
• Decision Tree Classifier
• Random Forest Classifier
• Gradient Boosting Classifier
• XGBoost Classifier
• Support Vector Classifier (SVC) (Final Selection)

Directory Structure

DiabetesPrediction/
│── Notebooks/
│   ├── Analysis.ipynb         # Jupyter notebook for data analysis
│
│── Resources/                 # Dataset and trained models
│   ├── diabetes.csv
│   ├── Imputed_final.csv
│   ├── model.pkl              # Trained classification model
│   ├── scaler.pkl             # Scaler for preprocessing
│
│── static/                    # Static files (CSS, images, JS)
│   ├── style.css              # CSS for styling the UI
│
│── templates/                 # HTML templates for Flask app
│   ├── form.html              # Main UI page for input & results
│
│── app.py                     # Flask app entry point
│── README.md                  # Project documentation
│── requirements.txt            # Dependencies list

Installation & Running the Project

Clone the repository and set up the environment:

git clone https://github.com/mShubham18/DiabetesPrediction.git
cd DiabetesPrediction
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r requirements.txt

Running the Application

Run the Flask application:

python app.py

Then, open your browser and go to:

http://127.0.0.1:5000/

Now you can enter the required inputs, and the model will predict whether the person is diabetic or not.

Feel free to contribute on this project 🙌🏻

Happy Coding ;)