https://github.com/abhipatel35/diabetes_ml_classification

Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.
https://github.com/abhipatel35/diabetes_ml_classification

classification data-analysis data-science data-visualization descision-tree diabetes-prediction jupiter-notebook logistic-regression machine-learning model-evaluation open-source pandas pycharm-ide python random-forest scikit-learn

Last synced: 6 days ago
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Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. Complete lifecycle of ML project included.

• Host: GitHub
• URL: https://github.com/abhipatel35/diabetes_ml_classification
• Owner: abhipatel35
• Created: 2024-02-12T00:56:17.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-02-12T00:59:06.000Z (9 months ago)
• Last Synced: 2024-10-16T22:12:04.605Z (20 days ago)
• Topics: classification, data-analysis, data-science, data-visualization, descision-tree, diabetes-prediction, jupiter-notebook, logistic-regression, machine-learning, model-evaluation, open-source, pandas, pycharm-ide, python, random-forest, scikit-learn
• Language: Python
• Homepage:
• Size: 14.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Classification ML Project: Diabetes Prediction

Welcome to the Classification ML Project repository! In this project, we aim to predict diabetes based on various health indicators using machine learning models.

## Project Overview

This project follows the complete lifecycle of a machine learning project, including data preparation, model selection, training, testing, and evaluation. Here's a brief overview of the steps involved:

1. **Data Preparation**: We start by loading the dataset (`diabetes.csv`) into a Pandas DataFrame. We inspect the data, check for missing values, and split it into independent features (`x`) and the dependent variable (`y`).

2. **Model Selection**: We experiment with three different classification algorithms:

   - Logistic Regression

   - Decision Tree Classifier

   - Random Forest Classifier

3. **Model Training**: We train each model using the training data (`x_train` and `y_train`).

4. **Model Testing**: We evaluate the performance of each model using the testing data (`x_test` and `y_test`). We measure accuracy as our evaluation metric.

## Repository Structure

- **diabetes.csv**: Dataset containing health indicators and diabetes outcomes.

- **main.py**: Python code for the classification ML project.

- **README.md**: You are here! This document provides an overview of the project and instructions for usage.

## Usage

1. Clone this repository to your local machine using `git clone`.

2. Open `main.py` in Jupyter Notebook or any compatible environment.

3. Run the notebook cells to execute the code step by step.

4. Explore the code, experiment with different algorithms, and analyze the results.

## Dependencies

- Python 3.x

- Pandas

- scikit-learn