https://github.com/udacity-machinelearning-internship/diabetescasestudy

Implementing several models with visualizations to putting all togother in Training and Tuning
https://github.com/udacity-machinelearning-internship/diabetescasestudy

Last synced: over 1 year ago
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Implementing several models with visualizations to putting all togother in Training and Tuning

• Host: GitHub
• URL: https://github.com/udacity-machinelearning-internship/diabetescasestudy
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-05-23T03:02:35.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-29T01:44:38.000Z (about 2 years ago)
• Last Synced: 2025-01-21T08:24:10.248Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 1.61 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          


  

  ![Diabetes_Case_Study](https://github.com/BaraSedih11/DiabetesCaseStudy/assets/98843912/90679af3-217d-44c5-a387-9acc43d002d1)


   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/DiabetesCaseStudy) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/DiabetesCaseStudy) [![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/downloads/release/python-380/)

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[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/DiabetesCaseStudy/releases/tag/v1.0.0)

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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:

```bash

pip install numpy pandas scikit-learn matplotlib seaborn

```

## Usage

1. Clone this repository to your local machine:

```bash

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

```

2. Navigate to the repository directory:

```bash

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](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.

- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.

- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.

- [seaborn](https://seaborn.pydata.org/): The seaborn library for data visualization in Python.