Joylynn-tech/kesra_logistics_analysis

Analysis of 10,532 messages from 652 East African logistics professionals to identify trade and customs pain points # KESRA Logistics Analysis Overview Analysis of 10,532 messages from a 652-member WhatsApp community of Kenya customs and logistics professionals, spanning July 2024 – June 2026. The goal extract structured intelligence from two years of unstructured conversations to identify operational pain points in East African trade and logistics. The Problem East African logistics professionals lack a centralised knowledge base for customs classification and trade corridor intelligence. This analysis quantifies that gap using real community data. Key Findings | Customs Clearance is the #1 point | 622 messages — most discussed topic | HS Code classification gap | 181 questions asked, only 14 codes shared — 13:1 gap | Dominant trade corridor | Mombasa (463) → Nairobi (340) → Uganda (105) | Network activity peak | February 2025 — 897 messages in one month | Industry operating rhythm | Tuesday is peak day — 2,204 messages over 2 years Tools Python · pandas · Matplotlib · Seaborn · regex · Jupyter Notebook Privacy Note Raw WhatsApp data is not included in this repository to protect member privacy. Author **Joylynn Mumbi Ngari** — Data Analyst, Supply Chain & Logistics linkedin.com · github.com