Summary#

Experienced with scikit-learn for training, evaluating, and tuning machine learning models. Applied to classification, data mining, and feature selection in IEEE-published healthcare research.

How I Apply This Skill#

• Applied Apriori algorithm and association rule mining for healthcare data
• Implemented feature selection techniques for dimensionality reduction
• Used train/test splits and cross-validation for model evaluation
• Applied preprocessing pipelines for encoding, scaling, and transformations
• Evaluated models with accuracy, confidence, support metrics

Key Strengths#

• Data Mining: Association rules, Apriori, feature selection
• Model Training: Classification algorithms, ensemble methods
• Evaluation: Cross-validation, confusion matrices, performance metrics
• Preprocessing: StandardScaler, encoding, train_test_split

Related Projects#

• Long COVID Prediction - Feature selection, association rules