End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.

What it does:

Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action

Loads a SQLite warehouse with keys, constraints and window-function SQL

Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis

Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)

6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)

Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard

18 automated tests

Logging every cleaning action is the part I like most: when a number in the report looks strange, you can follow it back to the fix that changed it. With 585 issues, how did you decide which ones the script fixes alone and which ones it only reports?

From Manual Follow-Up Chasing to an Agent-Run Follow-Up Queue

Before / After: follow-ups, from manual chasing to an agent-run queue

Before: every follow-up lived in someone's memory. The founder checked the inbox, guessed who was waiting, and sent reminders by hand. Leads went quiet not because they said no, but because nobody followed up in time.

After: an agent now runs the follow-up queue. Each lead has a next step and a due date, drafts wait for review, and every reply is logged with its source. The founder reviews the queue once, approves, and moves on.

What changed was not the tool. It was the handoff: one clear owner for each step, a runbook anyone can follow, and a log anyone can audit.

If follow-ups are the bottleneck, fix the handoff first — then automate it.

"Fix the handoff first" is the part most teams skip. I see the same thing in email automation: a flow nobody owns keeps firing to the wrong segment for months because no one is checking the log. When the founder is away, who approves the drafts waiting in the queue?