Data Mining Projects
- Built and evaluated decision tree classifiers for a dataset.
- Analyzed feature importance and model performance metrics such as accuracy, precision, and recall.
- Applied hyperparameter tuning to optimize tree depth and splitting criteria.
- Conducted data preprocessing steps, including handling missing values and normalization.
- Applied K-Means clustering to identify patterns and groups in the dataset.
- Evaluated clustering results using silhouette scores and other validation metrics.
- Implemented Apriori algorithm to discover frequent itemsets in transaction data.
- Generated association rules and analyzed confidence and support metrics.
- Provided actionable insights based on the extracted rules for decision-making.