Core Concepts:
Supervised and unsupervised machine learning techniques including regression, classification, and clustering
Evaluating model performance using metrics like RMSE, AUC-ROC, and F1-score
Combining SQL queries with Power BI for data extraction and visualization
Tools:
Python (scikit-learn), SQL, Power BI, Jupyter Notebook
Project:
Build a customer churn prediction model using classification techniques. Evaluate model accuracy using appropriate metrics, extract customer behavior data via SQL, and present insights through interactive Power BI dashboards.