🩺 Diabetes Prediction using Machine Learning

This project uses the Pima Indians Diabetes Dataset to predict whether a person is diabetic using health data and a Machine Learning model.

🚀 Features

• Used Random Forest Classifier
• Scaled inputs using StandardScaler
• Achieved ~81% accuracy
• Accepts real-time user input for prediction

🧠 Tech Stack

• Python
• Jupyter Notebook
• pandas, numpy
• scikit-learn (ML magic)

📁 Dataset Info

• Source: Pima Indians Diabetes Dataset
• Total Rows: 768
• Columns:
  • Pregnancies
  • Glucose
  • Blood Pressure
  • Skin Thickness
  • Insulin
  • BMI
  • Diabetes Pedigree Function
  • Age
  • Outcome (0 = Not Diabetic, 1 = Diabetic)

📊 Example Prediction

# Example input
[5, 160, 70, 32, 100, 35.0, 0.8, 42]

Model Output
"⚠️ Diabetic Detected"

How to Run

• Clone the repo:

git clone https://github.com/siddhikasavant/Diabetes-Prediction.git

• Open main.ipynb in Jupyter
• Run all cells & try testing your own values