Diabetic-Prediction-Using-Machine-Learning

🩺 Diabetes Prediction Using Machine Learning

This project uses machine learning to predict whether a patient is likely to have diabetes based on diagnostic input parameters. It utilizes a trained model and a standard scaler to provide predictions via a user interface.

📊 Dataset

The dataset used is diabetes.csv, which contains diagnostic measurements for patients. It includes features like:

Pregnancies

Glucose

BloodPressure

SkinThickness

Insulin

BMI

DiabetesPedigreeFunction

Age

Outcome (0 = Non-diabetic, 1 = Diabetic)

🧠 Machine Learning Model

The project includes:

Data preprocessing using scikit-learn’s StandardScaler (saved as scaler.pkl)

A classification model (e.g., Logistic Regression, Random Forest, etc.) trained on diabetes.csv

Prediction pipeline using the trained model and scaler

💻 How to Run

1. Clone the Repository

git clone https://monikasuresha.github.io/Diabetic-Prediction-Using-Machine-Learning

cd Diabetes-Prediction-Using-Machine-Learning

2. Install Requirements

pip install numpy pandas scikit-learn matplotlib streamlit

pip install flask

3. Run the App

python app.py

🧪 Example Input

Feature Example Value:

Pregnancies 2

Glucose 120

BloodPressure 70

SkinThickness 25

Insulin 80

BMI 28.0

DiabetesPedigreeFunction 0.5

Age 30

Output: Diabetic or Not Diabetic

✅ Future Improvements

Add feature selection or PCA

Deploy on a cloud platform (Heroku, AWS, etc.)

Improve UI/UX design

Add user authentication