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