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Diabetes_prediction_improve

In this code I have taken this from a random source and tried to improve the accuracy of the model in order to practice Machine learning

Diabetes Prediction with Naive Bayes

A Python implementation of Naive Bayes classifier for predicting diabetes using the Pima Indians Diabetes Dataset.

Features

• 🩺 Medical prediction model
• 📊 Handles CSV datasets
• 🧮 Implements Gaussian Naive Bayes
• 📈 67/33 train-test split
• 🎯 73.6% accuracy

Installation

1. Clone repository:

git clone https://github.com/yourusername/diabetes-prediction.git
cd diabetes-prediction

2. Install requirements:

pip install -r requirements.txt

Usage

1. Place diabetes.csv in project root
2. Run the classifier:

python MLML.py

Dataset

Download from Kaggle:

• 768 patient records
• 8 medical features
• 1 binary outcome (0 = no diabetes, 1 = diabetes)

CSV Columns:

1. Pregnancies
2. Glucose
3. BloodPressure
4. SkinThickness
5. Insulin
6. BMI
7. DiabetesPedigreeFunction
8. Age
9. Outcome

Example Output

$ python MLML.py
Accuracy: 73.62%

Contributing

Pull requests welcome! For major changes, please open an issue first.

License

MIT

32c2c40 (improved the accuracy)