This repository contains a comprehensive analysis and model implementation for predicting diabetes using machine learning techniques. It includes data preprocessing, model training, hyperparameter tuning, and evaluation of various regression metrics.

Introduction

This project aims to identify the best estimators and features for predicting diabetes outcomes based on the provided dataset. The analysis is performed using Python and several data science libraries.

Contents

• Regression Metrics.ipynb: Jupyter Notebook containing the implementation of Random Forest using Python.
• README.md: This file providing an overview of the repository.
• diabetes.csv: This is the working dataset.

Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

• NumPy
• pandas
• scikit-learn
• matplotlib
• seaborn You can install these libraries using pip:

pip install numpy pandas scikit-learn matplotlib seaborn

Usage

1. Clone this repository to your local machine:

git clone https://github.com/BaraSedih11/DiabetesCaseStudy.git

2. Navigate to the repository directory:

cd DiabetesCaseStudy

3. Open and run the Jupyter Notebook jupyter notebook Regression\ Metrics.ipynb.

4. Follow along with the code and comments in the notebook to understand the implementation of regression metrics, model training, and hyperparameter tuning using Python.

Acknowledgements

• scikit-learn: The scikit-learn library for machine learning in Python.
• NumPy: The NumPy library for numerical computing in Python.
• pandas: The pandas library for data manipulation and analysis in Python.
• matplotlib: The matplotlib library for data visualization in Python.
• seaborn: The seaborn library for data visualization in Python.