Turning raw data into decisions that move the business.
I'm a data analytics professional who loves finding the story hidden in the numbers. I work across the full analytics stack β from SQL databases and Python modeling to Power BI and Tableau dashboards β to help businesses make faster, smarter, data-driven decisions.
- π Focused on predictive & prescriptive analytics
- π± Always exploring new ways to visualize and communicate insights
- π¬ Ask me about machine learning, dashboards, and turning data into business impact
| Domain | Tools |
|---|---|
| Languages | Python, SQL |
| ML / Analytics | scikit-learn, Predictive Modeling, Clustering, Regression, Time-Series |
| Data Wrangling | Pandas, NumPy, Excel |
| Visualization | Power BI, Tableau, Matplotlib, Seaborn |
| Optimization | Google OR-Tools, Linear/Integer Programming |
| Databases | MySQL, Relational Design (3NF) |
| Project | Highlight | Tech |
|---|---|---|
| π¦ Bank Loan Repayment Analysis | Predicts loan defaults β ROC-AUC 0.915 | Python scikit-learn |
| π± Google Play Store Predictive Analytics | Sentiment + rating forecasting, 5 ML models | Python ML |
| π«οΈ PM2.5 Air Quality Forecasting | Time-series pollution prediction across cities | Python ARIMA |
| π End-to-End Python Data Analysis | Full pipeline: cleaning β EDA β modeling | Python Pandas |
| π Customer Behavior Models | Regression, classification & clustering combined | Python SQL Power BI |
| Project | Highlight | Tech |
|---|---|---|
| π π² Housing & Bike-Sharing Analytics | Predictive + prescriptive with Power BI | Python Power BI |
| β‘ Electric Vehicle Adoption Analysis | 14.8K EV registrations, BEV vs PHEV trends | Tableau |
| Project | Highlight | Tech |
|---|---|---|
| π― Marketing Segmentation Analysis | K-Means β 4 actionable segments | Python Excel |
| π¦ Route Optimization (Public Transit) | Bus scheduling with Google OR-Tools (MIP) | Python OR-Tools |
| ποΈ Car Dealership Database System | MySQL relational DB, 9 entities, 3NF | MySQL SQL |
| Project | Highlight | Tech |
|---|---|---|
| π Uber vs Lyft Comparative Analysis | 650K rides β statistical hypothesis testing | Python Statistics |
| π± PESTEL Analysis β Ethanol Blending (India) | Policy analytics with correlation insights | Python Analytics |
| βοΈ Ethics in Prescriptive Analytics | Responsible optimization & data privacy | Optimization |
π Each repository is a full case study β Business Problem, Dataset, Methodology, Key Insights, and Business Impact, with custom visuals and dashboards.