✨ Welcome!!! This project is ongoing as I add more and more steps towards data-driven insights. This also presents an excellent chance to explore novel and intriguing Python libraries. Stay tuned for updates and enhancements! For any inquiries or collaboration opportunities, feel free to reach out. Enjoy exploring the project!
📑 Project Report --> Reflects the whole data analysis process and corresponding insights with charts and tables.
💻 Project Notebook --> You are more than welcomed to explore code and visuals.
| Section | Status | Comments |
|---|---|---|
| Data Quality Assessment | ✔️ Done | Data Profiling, DQ dimensions, Data Assessment Matrix |
| Exploratory Data Analysis | ✔️ Done | Data Understanding, Data Cleaning, Data Insights |
| RFM Analysis | ✔️ Done | RFM Segmentation Matrix, RFM Distribution |
| Cohort Retention Analysis | ✔️ Done | Customer`s Retention heatmap |
| Time Series Analysis | ⌛ ongoing | ARIMA model, Forecasting |
| Predict high-value customers | 📃 to do | LogisticRegression, RandomForest |
| Dashboard | 📃 to do | The interactive web-app |
| Findings and Recommendations | 📃 to do | Summary of Analysis |
Product performance: Popularity vs Profit:
Purchase Frequency
RFM Analysis with seaborn heatmaps
Cohort Retention with seaborn heatmaps
EDA with PygWalker (Tableau-style UI in Python)
Comparing Test and Training sets with SweetViz (fully self-contained HTML application)