irshadulibad/Illness-prediction-arban-mali

Illness Prediction in Children (Urban Mali Dataset) This project focuses on predicting illness outcomes among children in urban Mali using machine learning techniques. ## 🛠 Tech Stack - **Python**: Data processing, modeling - **Pandas & NumPy**: Data manipulation - **Scikit-learn**: Model training and evaluation - **Imbalanced-learn (SMOTE)**: Handling class imbalance - **Matplotlib & Seaborn**: Visualization - **Flask**: Deployment of prediction API ## 🔹 Key Steps in the Project 1. **Data Preprocessing** - Handling missing values - Encoding categorical variables - Scaling numerical features 2. **Feature Selection** - Applied **Recursive Feature Elimination (RFE)** with Random Forest 3. **Handling Class Imbalance** - Used **SMOTE** to balance target classes 4. **Model Training & Optimization** - Implemented **Random Forest Classifier** - Hyperparameter tuning with **GridSearchCV** 5. **Model Evaluation** - Confusion Matrix, Accuracy, Precision, Recall, F1-score 6. **Deployment** - Built a **Flask API** for real-time illness prediction ## 📌 How to Run 1. Clone the repository: ```bash git clone github.com