emmanuelekopimo/african-febrile-disease-classification-model

# African Disease Surveillance Classification A machine learning project for classifying diseases based on African disease surveillance data. This project implements data preprocessing, feature engineering, and comparative model evaluation for disease classification. ## Project Overview This project analyzes African disease surveillance data to classify patients into different disease categories: - **Malaria** - **Typhoid** - **Viral Fever** - **Co-infection** (Malaria + Typhoid) - **Undifferentiated Fever** The project includes data cleaning, preprocessing, class balancing, and evaluation of multiple machine learning models. ## Dataset The project uses the African Disease Surveillance Dataset (`African_disease_surveillance_dataset.csv`), which contains patient records with: - **Demographic Information**: Sex, Location, Age - **Clinical Symptoms**: Fever, Headache, Fatigue, Vomiting, Diarrhea, Joint Pain, Cough - **Medical Tests**: Malaria_Test, Typhoid_Test - **Laboratory Values**: Temperature_C, WBC_Count, Platelet_Count ## Project Structure ``` csc316_exam/ ├── README.md # Project documentation ├── requirements.txt # Python dependencies ├── .gitignore # Git ignore rules ├── q1.py # Data preprocessing script ├── q2.py # Disease classification script ├── class_balancing.ipynb # Class balancing notebook ├── data_understanding.ipynb # EDA and data exploration ├── model_dev_eval.ipynb # Model development and evaluation ├── African_disease_surveillance_dataset.csv # Raw dataset ├── African_disease_final.csv # Final processed dataset ├── data_understanding.csv # Intermediate data file ├── feature_data_balance.csv # Balanced dataset for modeling └── African_di …