- This is a machine-learning-based system that predicts diseases based on user-provided symptoms. It cleans raw medical data, analyzes relationships between diseases and symptoms, and trains models for accurate predictions.
- Symptom Mapping: Matches symptoms to diseases using encoded datasets.
- Data Visualization: Uses graphs to explore symptom-disease relationships.
- Model Training: Trained with Naive Bayes and Decision Tree models for predictions.
- Prediction Accuracy: Provides probabilities for predicted diseases.
Programming Language: Python
Libraries: pandas, numpy - Data handling and cleaning
seaborn, matplotlib - Data visualization
sklearn - Machine learning models (Naive Bayes, Decision Tree)
csv - Raw data parsing
Visualization Tools: Exported decision trees for insights.