A collection of 4 end-to-end data analytics projects demonstrating skills in anomaly detection, operational KPI monitoring, data validation, and trend analysis. Built using real-world datasets from Kaggle and Python-based tools.
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Fraud & Anomaly Detection — Detects fraudulent transactions using Z-score, IQR, and rule-based risk scoring on 284k real credit card records.
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Interactive KPI Dashboard — Streamlit dashboard tracking 12 operational KPIs across Logistics, Sales, and Operations with live anomaly alerts.
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Financial Data Integrity Checker — 10-rule validation engine that audits financial records and generates an integrity score and violation report.
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Sales & Inventory Trend Analyzer — Identifies seasonal patterns, MoM growth, and stock risk flags across 2 years of regional sales data.
Python | Pandas | NumPy | Matplotlib | Seaborn | Streamlit | Plotly | Scipy | Kaggle API
| Project | Tools | Key Skills |
|---|---|---|
| Automatidata × NYC TLC — Taxi Fare Prediction | Python · Pandas · Matplotlib · Seaborn | EDA, PACE workflow, outlier analysis, regression prep, executive summary |
Completed as part of the Google Advanced Data Analytics Certificate.
Projects 5–8 (SQL, Power BI, R, Python Automation) are in data-portfolio-2.