salimbello1025/NIGERIA-RAINFALL-vs-FLOOD-ANALYSIS-1988---2025-
Domaine:
climateenvironment and energyType de record:
datasetproject
Créateur:
sal
Hôte:
End-to-end data analytics project combining Python (pandas) for cleaning and exploratory analysis with an interactive Power BI dashboard, testing whether rainfall volume predicts flood impact in Nigeria using 45 years of rainfall data (1981–2026) and flood disaster records (1988–2025) — 3MTT Data Analytics Capstone (DA-16).
# 🇳🇬 Nigeria Rainfall vs Flood Analysis Dashboard
**An end-to-end data analytics project investigating whether rainfall volume predicts flood impact in Nigeria — combining Python for data cleaning and exploratory analysis with an interactive Power BI dashboard.**
**Capstone Project DA-16** · 3MTT Data Analytics NextGen Cohort · Group 32 · Blue Sapphire Hub, Kano
Author: **Salim Bello Muhammad** — Track ID `FE/23/80109123`
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## 📌 Overview
Nigeria experiences some of the most severe flooding in West Africa. The common assumption is simple: *more rain means more flooding.* This project tests that assumption directly using real data — combining **45 years of CHIRPS satellite rainfall data (1981–2026)** with **EM-DAT international disaster records (1988–2025)** — to find out whether rainfall alone explains Nigeria's flood crisis, or whether something else is driving it.
### 🔑 Key Finding
> **The correlation between annual rainfall and flood impact is r = 0.164 — a very weak relationship.**
> Despite South Nigeria receiving **99% more rainfall** than the North, the North accounts for **71.43%** of all recorded flood events. This suggests flood risk in Nigeria is driven less by rainfall volume and more by factors such as **dam releases and infrastructure** — a finding with real implications for how flood early-warning systems should be designed.
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## 📊 Key Figures
| Metric | Value |
|---|---|
| Total Deaths (1988–2025) | 4,645 |
| Total People Affected | 17.6 million |
| Total Flood Events | 63 |
| Total Economic Damage | $5.4 billion USD |
| Deadliest Year | 2025 (1,390 deaths) |
| Most People Affected (single year) | 2012 (7,000,867 people) |
| Increase in Flood Deaths Since 1988 | +373% |
| Peak Flood Season | July – September |
| Rainfall–Flood Correlation | r = 0.164 (very weak) |
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## 🛠️ Tech Stack
| Stage | Tools |
|---|---|
| **Data Cleaning & EDA** | Python, pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook |
| **Data Modeling & Dashboar …