{"id":26166279,"url":"https://github.com/coding-for-it/diabetes-prediction-system","last_synced_at":"2026-04-19T08:33:46.798Z","repository":{"id":298823412,"uuid":"994530015","full_name":"coding-for-it/Diabetes-Prediction-System","owner":"coding-for-it","description":"A machine learning-based system to predict diabetes using Logistic Regression, Decision Tree, and Random Forest with up to 85% accuracy. 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It explores various classification models to predict whether a patient is likely to have diabetes based on diagnostic measurements.\r\n\r\n## 📊 Dataset\r\n\r\nThe dataset includes the following health-related features:\r\n- Pregnancies\r\n- Glucose\r\n- Blood Pressure\r\n- Skin Thickness\r\n- Insulin\r\n- BMI\r\n- Diabetes Pedigree Function\r\n- Age\r\n- Outcome (Target: 1 = diabetic, 0 = non-diabetic)\r\n\r\n## 🧰 Libraries Used\r\n\r\n- `pandas` – for data manipulation\r\n- `numpy` – for numerical operations\r\n- `matplotlib`, `seaborn` – for data visualization\r\n- `scikit-learn` – for data preprocessing, model training, and evaluation\r\n\r\n## 🔹 Features\r\n\r\n✅ **Comprehensive Exploratory Data Analysis (EDA)**  \r\n✅ **Clean and Preprocessed Data** (handled missing values, duplicates, and scaling)  \r\n✅ **Model Evaluation:** Logistic Regression, Decision Tree, and Random Forest  \r\n✅ **Performance Metrics:** Accuracy, Classification Report, and Confusion Matrix  \r\n✅ **Visualizations:** Distribution, Pairplot, Heatmap of correlations, and Model Evaluation charts  \r\n\r\n## 🔍 Project Workflow\r\n\r\n### 1. Data Cleaning\r\n- Zeros in certain health-related fields are replaced with median values to handle invalid entries.\r\n\r\n### 2. Exploratory Data Analysis (EDA)\r\n- Visualizations such as heatmaps and class distribution charts help understand relationships and feature importance.\r\n\r\n### 3. Feature Scaling\r\n- StandardScaler is used to normalize the feature set for improved model performance.\r\n\r\n### 4. Model Training\r\nThree different models are trained:\r\n- Logistic Regression\r\n- Decision Tree Classifier\r\n- Random Forest Classifier\r\n\r\n### 5. Model Evaluation\r\n- Evaluation is done using:\r\n  - Accuracy Score\r\n  - Confusion Matrix\r\n  - Classification Report\r\n \r\n\r\n### Tech Stack\r\n\r\n- **Python 3**\r\n- **Pandas**, **Numpy**\r\n- **Scikit-learn**\r\n- **Matplotlib**, **Seaborn**\r\n- **Jupyter Notebook**\r\n\r\n## 🧪 How to Run\r\n\r\n1. Clone the repository:\r\n   ```bash\r\n   git clone https://github.com/coding-for-it/Diabetes-Prediction-System.git\r\n   cd Diabetes-Prediction-System\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoding-for-it%2Fdiabetes-prediction-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoding-for-it%2Fdiabetes-prediction-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoding-for-it%2Fdiabetes-prediction-system/lists"}