https://github.com/halyusa16/diabetes-prediction-using-machine-learning

This project focuses on predicting diabetes using machine learning techniques. Various preprocessing steps such as handling imbalanced data (SMOTE, RandomUnderSampler), feature selection (RFE), and outlier removal (Z-score) were applied to improve model performance.
https://github.com/halyusa16/diabetes-prediction-using-machine-learning

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This project focuses on predicting diabetes using machine learning techniques. Various preprocessing steps such as handling imbalanced data (SMOTE, RandomUnderSampler), feature selection (RFE), and outlier removal (Z-score) were applied to improve model performance.

• Host: GitHub
• URL: https://github.com/halyusa16/diabetes-prediction-using-machine-learning
• Owner: halyusa16
• Created: 2025-02-02T08:47:31.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-23T10:09:31.000Z (over 1 year ago)
• Last Synced: 2025-02-23T11:20:23.387Z (over 1 year ago)
• Topics: data-mining, data-science, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 4.08 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

## 🔍 Overview

This project explores health-related indicators to classify individuals into three categories:  

- **0:** No Diabetes  

- **1:** Prediabetes  

- **2:** Diabetes  

Various preprocessing steps such as handling imbalanced data (SMOTE, RandomUnderSampler), feature selection (RFE), and outlier removal (Z-score) were applied to improve model performance.

## 📂 Dataset

- **Name:** `diabetes_012_health_indicators_BRFSS2015.csv`

- **Description:** A dataset containing health indicators associated with diabetes classification. 

## 🛠️Technologies Used

- 🐍 **Python**  

- 📊 **Pandas, NumPy** (Data Handling)  

- 🎨 **Seaborn, Matplotlib** (Data Visualization)  

- 🤖 **Scikit-learn** (Machine Learning)  

- ⚖️ **Imbalanced-learn** (SMOTE, RandomUnderSampler)  

## 🚀 Project Steps

### 1️⃣ Data Loading 📥  

- Read and explore the dataset.  

- Identify missing values and compute basic statistics.  

### 2️⃣ Exploratory Data Analysis (EDA) 📊  

- Visualized feature distributions.  

- Checked for class imbalance in target labels.  

### 3️⃣ Data Preprocessing 🔄  

- **Outlier detection & removal** using **Z-score**.  

- **Feature selection** with **Recursive Feature Elimination (RFE)**.  

- **Class balancing** via **SMOTE (oversampling) & RandomUnderSampler (undersampling)**.  

### 4️⃣ Machine Learning Models 🤖  

Tested multiple models to compare their effectiveness:  

✅ **Logistic Regression**  

✅ **Gradient Boosting**  

✅ **Decision Tree**  

### 5️⃣ Results & Insights 📈  

- **Compared model performance** under different preprocessing techniques.  

- Assessed the impact of **feature selection** and **outlier removal** on accuracy.  

📌 **Evaluated using:**  

- ✅ Accuracy  

- ✅ Precision  

- ✅ Recall  

- ✅ F1-score  

## Future Improvements

- Experiment with deep learning models.

- Implement additional feature engineering techniques.

- Deploy the model using a web app.

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✨ *Created by Halyusa Ard Wahyudi as part of a data science portfolio.* 🚀