https://github.com/rajarohan/diabetes_prediction
https://github.com/rajarohan/diabetes_prediction
Last synced: 13 days ago
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- Host: GitHub
- URL: https://github.com/rajarohan/diabetes_prediction
- Owner: rajarohan
- Created: 2024-04-25T06:19:17.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-25T06:53:14.000Z (about 2 years ago)
- Last Synced: 2025-01-02T00:12:41.945Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.07 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Diabetes Prediction
This project aims to develop a machine learning model to predict the likelihood of diabetes in individuals based on various features such as age, gender, BMI, blood pressure, etc.
## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Installation](#installation)
- [Usage](#usage)
## Introduction
Diabetes is a prevalent chronic disease worldwide, and early prediction can help in proactive management and prevention of complications. Machine learning techniques offer promising approaches to predict diabetes risk based on individual health data.
This project explores the development and evaluation of machine learning models for diabetes prediction using Python and popular libraries such as scikit-learn, pandas, and matplotlib.
## Dataset
The dataset used in this project contains records of individuals with various features including age, gender, BMI, blood pressure, and other relevant medical attributes. The target variable is the presence or absence of diabetes.
Dataset Source: [https://github.com/rajarohan/diabetes_prediction/blob/main/diabetes_prediction_dataset.csv]
## Installation
To run the project locally, follow these steps:
1. Download IPYNB
2. upload the IPYNB to Google Colab
3. Download the dataset
4. upload to Google Colab
5. Run Each cell
## Usage
After installing the dependencies and downloading the dataset, you can proceed with the following steps:
1. Explore the dataset: Use Google Colab or any preferred IDE to open and explore the dataset (`diabetes_prediction_dataset.csv`).
2. Preprocess the data: Preprocess the dataset as needed, including handling missing values, encoding categorical variables, scaling features, etc.
3. Train models: Train machine learning models using various algorithms such as Logistic Regression, Random Forest, Support Vector Machines, etc.
4. Evaluate models: Evaluate the trained models using appropriate metrics such as accuracy, precision, recall, and confusion matrix.
5. Predictions: Use the trained models to make predictions on new data or test set and evaluate their performance.