{"id":15136034,"url":"https://github.com/abhipatel35/diabetes_ml_classification","last_synced_at":"2026-01-20T21:06:40.999Z","repository":{"id":222048358,"uuid":"756114991","full_name":"abhipatel35/Diabetes_ML_Classification","owner":"abhipatel35","description":"Predict diabetes using machine learning models. Experiment with logistic regression, decision trees, and random forests to achieve accurate predictions based on health indicators. 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In this project, we aim to predict diabetes based on various health indicators using machine learning models.\n\n## Project Overview\n\nThis 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:\n\n1. **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`).\n\n2. **Model Selection**: We experiment with three different classification algorithms:\n   - Logistic Regression\n   - Decision Tree Classifier\n   - Random Forest Classifier\n\n3. **Model Training**: We train each model using the training data (`x_train` and `y_train`).\n\n4. **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.\n\n## Repository Structure\n\n- **diabetes.csv**: Dataset containing health indicators and diabetes outcomes.\n- **main.py**: Python code for the classification ML project.\n- **README.md**: You are here! This document provides an overview of the project and instructions for usage.\n\n## Usage\n\n1. Clone this repository to your local machine using `git clone`.\n2. Open `main.py` in Jupyter Notebook or any compatible environment.\n3. Run the notebook cells to execute the code step by step.\n4. Explore the code, experiment with different algorithms, and analyze the results.\n\n## Dependencies\n\n- Python 3.x\n- Pandas\n- scikit-learn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhipatel35%2Fdiabetes_ml_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhipatel35%2Fdiabetes_ml_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhipatel35%2Fdiabetes_ml_classification/lists"}