For interviewers and reviewers: This document exists because honest data work requires disclosing what the data cannot tell you. Every limitation here was discovered during analysis and is documented here rather than hidden.
Affected pages: Market Basket, Customer Segments (RFM), Cohort Retention, Customer Journey
The grocery transactions dataset (groceries_clean.csv) is a public benchmark dataset of
Western grocery store transactions from 2014–2015. Zepto was founded in 2021.
Impact:
- RFM segments, cohort retention rates, and basket rules describe a generic grocery store customer base, not Indian quick-commerce users
- Customer counts (3,898), transaction counts (38,765), and retention rates (14.3% avg M1) apply to this proxy dataset, not any real platform
Why it's still valid:
- All methodology (quintile scoring, cohort period arithmetic, Apriori pipeline) is textbook-correct and transferable to any transactional dataset
- The technical implementation is what the project demonstrates — not real business intelligence about Zepto
Affected page: Market Basket
At min_support=0.005 (0.5%), the Apriori algorithm on this dataset found:
- 1 statistically significant rule:
frankfurter → vegetables, lift=1.12x, confidence=13.6%
Root cause: The dataset is in single-item-per-row format with ~2.6 average items per shopping trip. This is a data density limitation — not a pipeline error. Association rules require dense co-occurrence patterns that this dataset structure does not support at standard thresholds.
What was NOT done (integrity note):
The run_pipeline.py originally silently lowered min_support from 0.005 to 0.0015 during
execution to produce "more rules." This undisclosed parameter change has been removed.
The pipeline now uses the threshold as written in the notebook.
Affected page: Price Intelligence
Notebook 03 revealed:
- Zepto Beverages median price: ₹9,500 (real Zepto Beverages = ₹50–500 range)
- Zepto Personal Care median price: ₹16,200 (implausible for a grocery platform)
These appear to be data entry errors or currency mismatches in the source Kaggle CSV.
Impact:
- The "6% Zepto median discount" claim that previously appeared in the README was removed because it cannot be verified from this corrupted data
- The Data Quality dashboard page (Page 9) flags these categories automatically using IQR detection
Affected page: Business Simulator
All financial projections (ROI, LTV, payback period) are based on:
| Parameter | Value | Type |
|---|---|---|
| Avg order value | ₹350 | Industry estimate (not from this dataset) |
| Orders/month per recovered customer | 2 | Conservative assumption |
| Price elasticity | -2.0 | Standard retail textbook value |
| Discount-to-retention boost | +0.3pp per 1% discount | Heuristic |
| Recovered LTV fraction | 30% of Champion LTV | Conservative heuristic |
The Simulator is a modelling framework demonstrating how to structure these calculations — not a forecast derived from the datasets in this project.
Affected pages: Overview, Price Intelligence (radar chart)
The Blinkit raw CSV does not contain a discount_pct column. data_loader.py
generates synthetic discount percentages using a Beta distribution:
blinkit['discount_pct'] = np.random.beta(a=2.5, b=12.0, size=len(blinkit)) * 100This produces a center-weighted distribution around ~14.5% to prevent a flat radar chart.
The values are not from real Blinkit discount data. np.random.seed(42) is used for
reproducibility, so the same values are generated every run.
| ID | Limitation | Severity | Mitigated? |
|---|---|---|---|
| L1 | Proxy grocery dataset, not real QC data | 🔴 High | ✅ Disclosed in README, dashboard, notebook headers |
| L2 | Basket analysis: 1 rule at standard threshold | 🟡 Medium | ✅ Honest analyst note on dashboard page |
| L3 | Zepto price data corrupted in 2+ categories | 🟡 Medium | ✅ IQR detection flags these automatically |
| L4 | Simulator uses assumed ₹ parameters | 🟡 Medium | ✅ Assumptions expander in dashboard |
| L5 | Blinkit discount_pct is imputed, not real | 🟡 Medium | ✅ Documented in data_loader.py comments |
Last updated: June 2026