This project builds a fully automated ETL (Extract, Transform, Load) pipeline to analyze how weather conditions affect crop availability across selected regions in Nigeria.
# š¾ Climate Impact on Crop Availability ā Automated ETL Pipeline
## š Overview
This project builds a fully automated **ETL (Extract, Transform, Load) pipeline** to analyze how weather conditions affect crop availability across selected regions in Nigeria.
The system extracts historical weather data and combines it with crop yield data to uncover patterns between **climate variables (temperature & rainfall)** and **agricultural productivity**.
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## šÆ Objective
To determine how weather conditions influence crop yield and availability using a reproducible, automated data pipeline.
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## š§ Key Features
* ā
Modular ETL pipeline (Extract ā Transform ā Load)
* ā
Automated weekly execution (Windows Task Scheduler)
* ā
Incremental data loading (append, not overwrite)
* ā
Data cleaning & validation
* ā
SQLite database storage
* ā
Logging system for monitoring
* ā
Exploratory data analysis & visualization
* ā
Machine learning prediction model
* ā
Streamlit dashboard for interactive insights
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## š„ Data Sources
### š¦ļø Weather Data
* Source: Open-Meteo Historical API
* Endpoint:
archive-api.open-meteo.com
**Features:**
* time
* temperature
* precipitation
* region
* year
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### š¾ Crop Data
* Source: Synthetic dataset (generated programmatically)
**Features:**
* date
* region
* crop (Maize, Rice, Sorghum)
* yield
* year
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## š§± Data Schema
### Crop Dataset
| Column | Description |
| ------ | ---------------- |
| date | Observation date |
| region | Nigerian state |
| crop | Crop type |
| yield | Daily yield |
| year | Year |
### Weather Dataset
| Column | Description |
| ------------- | ------------------- |
| time | Observation date |
| temperature | Average temperature |
| precipitation | Rainfall |
| region | Nigerian state |
| year | Year |
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## š Pipeline Architecture
Crop Data + Weather Data
ā Extract
ā Cl ā¦