A collection of personal analytics projects spanning marketing analysis, customer segmentation, sales EDA, and time series forecasting. Each project includes end-to-end analysis from data cleaning through visualization and insight generation.
| # | Project | Focus Area | Dataset | Tools |
|---|---|---|---|---|
| 1 | Campaign Performance | Marketing Analytics | iFood Marketing (2,240 customers) | Python, Pandas, Seaborn |
| 2 | Customer Segmentation | Segmentation, Clustering | Mall Customers (200 records) | Python, Scikit-learn, Plotly |
| 3 | Conversion Rate EDA | Campaign Analytics | Marketing Campaign Dataset | Python, Pandas, Seaborn |
| 4 | Site Traffic Forecast | Forecasting | Web Clicks Time Series | Python, Prophet, Matplotlib |
| 5 | Superstore Sales EDA | Business Analytics | Superstore Sales Dataset | Python, Pandas, Plotly |
| 6 | Video Game Sales EDA | Sales Analytics | Kaggle VGSales (16,598 raw rows; 11,675 after cleaning) | Python, Pandas, Seaborn |
| 7 | Google Play Store SQL | SQL Analytics | Google Play Store (9,366 apps after cleaning; 33 categories) | Python, SQLite3, Pandas |
| Category | Stack |
|---|---|
| Languages | Python, SQL |
| Libraries | Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly, Prophet |
| Environments | Google Colab, JupyterLab |
| Methods | EDA, K-Means Clustering, Time Series Forecasting, Regression, SQL Querying |
Notebooks execute automatically on every push via GitHub Actions; outputs, charts, and results are visible directly on GitHub. Use the Open in Colab badges in each project README to run them interactively.