This project analyzes agricultural data to understand the factors influencing crop productivity. The analysis was performed using SQL for data exploration and Power BI for interactive visualization.
To analyze crop yield trends and identify how soil type, rainfall, temperature, irrigation, fertilizer usage, and regional conditions affect agricultural output.
| Tool | Purpose |
|---|---|
| SQL | Data querying and exploration |
| Power BI | Interactive dashboard visualization |
| Kaggle | Dataset source |
Before building the dashboard, SQL was used to analyze the dataset and answer key business questions:
- Crop-wise average yield analysis
- Region-wise productivity comparison
- Soil type impact on crop yield
- Irrigation method effectiveness
- Rainfall and temperature band analysis
- Fertilizer and pesticide efficiency calculations
- Previous crop influence on yield
- Identification of high-performing crop–region combinations
SQL operations used:
✔ Aggregations (AVG, SUM, COUNT) ✔ Grouping and filtering ✔ CASE statements for segmentation ✔ Subqueries ✔ Data quality checks
📄 Full SQL queries → sql_analysis_queries.sql
- 🌱 Soil-based yield insights
- 🌾 Crop performance comparison
- 🗺️ Region-wise productivity trends
- 💧 Irrigation and environmental impact analysis
- 📈 KPI-driven visualization
Overview
Soil Type Analysis
Region Analysis
This project demonstrates a complete end-to-end data analysis pipeline:
SQL → Data Insights → Power BI Dashboard
Dataset sourced from Kaggle and analyzed using SQL before visualization.
⚠️ Dataset not included — download from Kaggle
Shiva Keerth G
📧 gantishivakeerth@gmail.com
🔗 GitHub | LinkedIn