{"id":26013342,"url":"https://github.com/halyusa16/diabetes-prediction-using-machine-learning","last_synced_at":"2025-03-06T01:36:26.495Z","repository":{"id":275404072,"uuid":"925984091","full_name":"halyusa16/Diabetes-Prediction-using-Machine-Learning","owner":"halyusa16","description":"This project focuses on predicting diabetes using machine learning techniques. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🩺 Diabetes Prediction using Machine Learning\n\n## 🔍 Overview\nThis project explores health-related indicators to classify individuals into three categories:  \n- **0:** No Diabetes  \n- **1:** Prediabetes  \n- **2:** Diabetes  \n\nVarious preprocessing steps such as handling imbalanced data (SMOTE, RandomUnderSampler), feature selection (RFE), and outlier removal (Z-score) were applied to improve model performance.\n\n## 📂 Dataset\n- **Name:** `diabetes_012_health_indicators_BRFSS2015.csv`\n- **Description:** A dataset containing health indicators associated with diabetes classification. \n\n## 🛠️Technologies Used\n- 🐍 **Python**  \n- 📊 **Pandas, NumPy** (Data Handling)  \n- 🎨 **Seaborn, Matplotlib** (Data Visualization)  \n- 🤖 **Scikit-learn** (Machine Learning)  \n- ⚖️ **Imbalanced-learn** (SMOTE, RandomUnderSampler)  \n\n## 🚀 Project Steps\n### 1️⃣ Data Loading 📥  \n- Read and explore the dataset.  \n- Identify missing values and compute basic statistics.  \n\n### 2️⃣ Exploratory Data Analysis (EDA) 📊  \n- Visualized feature distributions.  \n- Checked for class imbalance in target labels.  \n\n### 3️⃣ Data Preprocessing 🔄  \n- **Outlier detection \u0026 removal** using **Z-score**.  \n- **Feature selection** with **Recursive Feature Elimination (RFE)**.  \n- **Class balancing** via **SMOTE (oversampling) \u0026 RandomUnderSampler (undersampling)**.  \n\n### 4️⃣ Machine Learning Models 🤖  \nTested multiple models to compare their effectiveness:  \n✅ **Logistic Regression**  \n✅ **Gradient Boosting**  \n✅ **Decision Tree**  \n\n### 5️⃣ Results \u0026 Insights 📈  \n- **Compared model performance** under different preprocessing techniques.  \n- Assessed the impact of **feature selection** and **outlier removal** on accuracy.  \n\n📌 **Evaluated using:**  \n- ✅ Accuracy  \n- ✅ Precision  \n- ✅ Recall  \n- ✅ F1-score  \n\n## Future Improvements\n- Experiment with deep learning models.\n- Implement additional feature engineering techniques.\n- Deploy the model using a web app.\n\n---\n✨ *Created by Halyusa Ard Wahyudi as part of a data science portfolio.* 🚀 \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-using-machine-learning/lists"}