Logistic Regression for Diabetes Prediction

Description:

This Python code implements a Logistic Regression model from scratch to predict diabetes diagnoses based on various patient characteristics. It uses the Healthcare-Diabetes.csv dataset (assumed to be located in the same directory).

Features:

• Data pre-processing
• Logistic Regression model implementation
• Model training and prediction

Requirements:

• Python 3.x
• pandas
• numpy
• matplotlib (optional, for visualization)

Usage:

1. Download the Healthcare-Diabetes.csv dataset: Ensure it's in the same directory as this code.
2. Run the script: Execute the Python script (e.g., python diabetes_prediction.py).

Customization:

• Modify the LogisticRegression class for hyperparameter tuning.
• Explore feature engineering techniques.
• Add functionalities like model saving and visualization.

Contributions:

We welcome contributions in the form of bug fixes, code improvements, or new features. Please create a pull request to share your changes.