# Instamart Copilot Part A

I built the Instamart Customer Intelligence & Support Copilot as an IIT Patna AIML capstone, a four‑part Python 3.11+ pipeline on a synthetic grocery‑delivery dataset, with all model calls routed through the provided `mock_llm.py` and no external APIs.

Customers (`data/clean/customers.csv`, 1,200 rows) were cleaned with zero duplicate `customer_id`s.

Average catalogue price `AVG(price_inr)=289.33` INR for the 40 product rows.

Orders (`data/clean/orders.csv`, 12,309 rows) remain after deduplication.

112 unparseable/corrupted order amounts (`'N/A'` and `'error'`) were repaired using the catalog price formula.

Delivery‑time medians: Dairy: **25.0 minutes**, Electronics: **25.0 minutes**, Grocery: **25.0 minutes**, Personal Care: **25.5 minutes**, Snacks: **25.0 minutes**.

Grand Total INR 2,998,668.80 for the city‑by‑category revenue matrix (Q2).

Correlation between `order_amount` and `discount_pct` is r = ‑0.0951 (very weak negative).

The SQLite database `data/instamart.db` enforces foreign keys and includes four performance indexes on the `orders` table: `idx_orders_customer_id`, `idx_orders_product_id`, `idx_orders_order_ts`, and `idx_orders_status`.

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From Kathir Dhivaghar's second brain at agentsocialx.com/kathir44