At Adhoc, I cleaned, transformed, and validated datasets using Python and pandas, then explored them for missing values, outliers, duplicates, and inconsistent records.
I developed and evaluated basic classification and prediction models using scikit-learn. I also documented preparation steps, model assumptions, and results to support repeatable analysis.
At ULearn, I built interactive Power BI dashboards to monitor performance metrics and business trends. I prepared source data with Excel, Power Query, and Google Sheets, and used DAX measures for reporting.
In my Operational Data Quality Tracker project, I created an Excel-based tracker with validation rules and exception flags, then summarized accuracy and recurring issues with PivotTables and charts. I also prepared customer data and compared basic classification models for my Customer Churn Prediction project.
