SUBMISSION: Project 3 — Feature Engineering & Preprocessing Pipeline with Scikit-Learn Pipelines

# SUBMISSION: Project 3 — Feature Engineering & Preprocessing Pipeline with Scikit-Learn Pipelines > 🎯 **Hands-on Portfolio Deliverable.** Feature engineering decides more leaderboard positions than model choice. This project proves you can build preprocessing that is correct (no leakage), robust (unseen categories don't crash), and measurable (ablate every transformation). ## 🏢 Business Scenario & Executive Prompt **FreshKart**, an online grocer, forecasts next-week demand per SKU-store to cut both stockouts (lost margin) and overstock (spoilage). An intern's notebook scores R² 0.94 — computed with imputation and target-encoding fit on the full dataset. The head of DS asks you: *"Rebuild the pipeline so the number is honest, then show me which features actually pay for themselves."* ## 📋 Source Data & Input Schemas **`freshkart_demand.csv`** — 180,000 rows (SKU-store-weeks; mock shape): | column | dtype | description | |---|---|---| | `sku_id` | str | 2,400 SKUs (high cardinality) | | `store_id` | category | 60 stores | | `week` | date | week start (78 weeks of history) | | `price_inr` | float | selling price (8% missing) | | `promo_flag` | 0/1 | promotion active | | `category` | category | 18 grocery categories | | `units_sold` | float | target: next-week units | Expected shapes: strong promo lift; `price_inr` missingness correlates with new SKUs; demand is weekly-seasonal. ## 🎯 Required Deliverables 1. **`features_pipeline.py`** — importable `build_pipeline()` returning an unfitted sklearn `Pipeline`; no data loading inside. 2. **`pipeline.pkl`** — fitted pipeline artifact + `feature_list.json` (exact post-transform column contract). 3. **`cv_scores.json`** — time-based CV scores: baseline (numeric-only) vs +encoding vs +target-encoding vs leaky reference. 4. **`leakage_report.md`** — the leaky-vs-clean gap with an explanation of *which* step leaked and the structural fix. ## 🛠️ Step-by-Step Implementation Guide 1. **Time-based splits.** Sort by week; expanding-window folds (train past → validate future). No shuffling — demand has a time axis. 2. **Baseline pipeline.** Median-impute + scale numerics, one-hot low-cardinality cats, Ridge regression. Record CV MAE/R². 3. **Handle `sku_id`.** Never one-hot 2,400 levels: implement in-fold target encoding (smoothed means computed inside each training fold) or hashing. Show the dimensionality math. 4. **Prove the leak.** Deliberately fit one *leaky* variant (encoders/imputers on full data) and measure the optimism gap vs the clean pipeline. 5. **Ablate.** Remove each feature group once; keep only groups that improve time-CV beyond noise. Document the keep/drop table. 6. **Harden.** `handle_unknown="ignore"`, missing-price path for new SKUs, and a 1-row inference smoke test with an unseen `sku_id`. ## 💻 Complete Runnable Starter Scaffold ```python from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder, StandardScaler from sklearn.linear_model import Ridge NUM = ["price_inr", "promo_flag"] CAT_LOW = ["store_id", "category"] def build_pipeline() -> Pipeline: pre = ColumnTransformer([ ("n", Pipeline([("imp", SimpleImputer(strategy="median")), ("sc", StandardScaler())]), NUM), ("c", Pipeline([("imp", SimpleImputer(strategy="most_frequent")), ("oh", OneHotEncoder(handle_unknown="ignore"))]), CAT_LOW), ]) return Pipeline([("pre", pre), ("reg", Ridge(alpha=1.0))]) # In-fold target encoding for sku_id (sketch): compute smoothed means with # statistics from the TRAIN fold only, inside each CV iteration — never globally. # expanding-window CV: sklearn.model_selection.TimeSeriesSplit ``` ## 🧪 Acceptance Criteria & Verification Checks - [ ] `features_pipeline.py` imports cleanly; `build_pipeline()` returns an unfitted pipeline with no data I/O. - [ ] `cv_scores.json` shows time-based CV for ≥ 3 variants plus a leaky reference with a documented optimism gap. - [ ] `leakage_report.md` names the leaking step(s), explains the mechanism, and states the structural fix. - [ ] Unseen `sku_id` + missing `price_inr` score without exceptions; `feature_list.json` matches transform output width. ## 📊 Evaluation Rubric (100 points — passing ≥ 80) | # | Criterion | Points | What earns full marks | |---|---|---|---| | 1 | Modeling correctness | 25 | Right algorithm for the task; leakage-free splits/pipelines; metrics match the business decision; numbers reproducible | | 2 | Code quality & reproducibility | 25 | Seeded RNG, re-runnable notebook/scripts, typed schemas, no hardcoded paths or magic numbers | | 3 | Business reasoning & memo | 25 | Decision-oriented write-up with costed trade-offs; honest about uncertainty, limits, and risks | | 4 | Visualization & communication | 25 | Labeled ROC/PR, calibration, SHAP or residual plots where relevant; figures referenced in the