# Instamart Copilot Part B

I implemented Part B of the Instamart Copilot, an end‑to‑end pipeline that computes RFM‑based customer segmentation with K‑Means and builds a binary lapse‑prediction model for churn risk. All code resides in `src/part_b.py` and results are written under the `outputs/` folder. I launch the pipeline with the command shown in the first quote below.

Features are computed using completed orders only, evaluated at the fixed reference snapshot **2025-10-01 00:00:00**.

The RFM step processes **1,175 customers** with at least one completed order.

K‑Means clustering was evaluated for K = 2 … 8; **K = 4** achieved the highest silhouette score of **0.5255** and an inertia of **743.07**.

The dynamic “Premium” segment contains **138 customers** (11.7 % of the population) with a mean monetary spend of **INR 7,129.70**.

The selected RandomForest model (`max_depth=3, min_samples_leaf=20`) achieved a held‑out test F1 score of **0.6595** for the lapsed class, surpassing the business target of 0.55.

The trained model was persisted to `outputs/lapse_model.joblib` and re‑loaded without any prediction drift.

An illustrative retention priority list of the top‑10 highest‑risk Premium customers was saved as `outputs/retention_list.csv`.

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