https://github.com/kaoutarmi/diabetes-prediction-system

Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied
https://github.com/kaoutarmi/diabetes-prediction-system

accuracy detection diabetes diabetes-detection diabetes-prediction health-data knn-classification logistic-regression machine-learning machine-learning-algorithms naive-bayes-classifier random-forest-classifier support-vector-machines

Last synced: 7 months ago
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Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied

• Host: GitHub
• URL: https://github.com/kaoutarmi/diabetes-prediction-system
• Owner: kaoutarmi
• Created: 2024-11-20T11:51:41.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-02-26T13:05:09.000Z (8 months ago)
• Last Synced: 2025-02-26T14:22:33.188Z (8 months ago)
• Topics: accuracy, detection, diabetes, diabetes-detection, diabetes-prediction, health-data, knn-classification, logistic-regression, machine-learning, machine-learning-algorithms, naive-bayes-classifier, random-forest-classifier, support-vector-machines
• Language: Jupyter Notebook
• Homepage:
• Size: 3.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🩺 Diabetes Prediction Using Machine Learning

Welcome to the **Diabetes Prediction** project! 🎉 The goal of this project is to predict whether a person has diabetes using various **machine learning algorithms**. We focus on applying data cleaning, visualization, and modeling techniques to build accurate prediction models.

## 🎯 Objective

- 🧹 **Clean the data** to ensure high-quality inputs.

- 📊 **Visualize the data** to better understand patterns and correlations.

- 🤖 **Train machine learning models** to predict diabetes.

- 💡 **Evaluate model performance** using several evaluation metrics.

## 🛠️ Techniques Used

1. **Data Cleaning** 🧼: Removing missing values, handling outliers, and preparing the data for modeling.

2. **Data Visualization** 📊: Analyzing and visualizing the data to understand patterns and trends.

3. **Machine Learning Modeling** 🤖: Training multiple machine learning models to predict diabetes.

## 🧠 Algorithms Used

1. **Logistic Regression** 🧑‍💼

2. **Support Vector Machine (SVM)** 🔲

3. **K-Nearest Neighbors (KNN)** 🔍

4. **Random Forest Classifier** 🌳

5. **Naive Bayes** 🧑‍🔬

6. **Gradient Boosting** 🔥

## 📈 Model Evaluation Methods Used

1. **Accuracy Score** ✅: Measures how often the model makes correct predictions.

2. **ROC AUC Curve** 📉: Evaluates the trade-off between true positive rate and false positive rate.

3. **Cross-Validation** 🔄: Splitting the data into different subsets to ensure the model performs well on unseen data.

4. **Confusion Matrix** 📊: Provides a breakdown of prediction errors, including false positives, false negatives, true positives, and true negatives.

## 📦 Dependencies

To run this project, you will need the following libraries:

- `pandas` 📑

- `numpy` 🔢

- `matplotlib` 📊

- `seaborn` 🎨

- `scikit-learn` 🧑‍💻

You can install the dependencies by running:

```bash

pip install pandas numpy matplotlib seaborn scikit-learn