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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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhalyusa16%2Fdiabetes-prediction-machine-learning/lists"}