🌾 Farm Input Price Volatility Analysis & Forecasting

📉 Forecasting fertilizer, diesel, and seed price swings across Indian states

This project uses time-series forecasting (ARIMA & Prophet) to predict the price volatility of key agricultural inputs — fertilizers, diesel, and seeds — across 10+ Indian states. With an emphasis on seasonality and price shock detection, it aims to support farmer profitability and policy planning.

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🛠️ Tech Stack

• Python: Pandas, NumPy, Prophet, Statsmodels, Matplotlib, Seaborn
• Forecasting Models: ARIMA, Prophet
• Data Cleaning & Wrangling
• SQL: MySQL for multi-source backend storage
• Visualization: Power BI (interactive dashboards, slicers, maps)
• Web Scraping: Requests, BeautifulSoup, Selenium
• Automation: schedule, cron jobs
• Metrics: MAPE, RMSE, MAE

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📊 Project Highlights

• 🔮 Forecasted fertilizer, diesel, and seed prices with 80%+ accuracy across Indian states
• 🌾 Created seasonal cost trend dashboards in Power BI for Rabi/Kharif crop cycle planning
• 🧠 Performed anomaly detection and seasonal decomposition on historical input price data
• 🗃️ SQL backend integration for dynamic, multi-dimensional queries
• ⚙️ Automated pipeline for weekly updates and live forecasts

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📈 Unique KPIs Tracked

• Input Price Volatility Index (IPVI)
• Forecast Accuracy Score
• Early Price Spike Alert Lead Time
• State-wise Seasonal Price Deviation (%)
• Estimated Savings from Forecast Adoption

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📂 Project Structure

farm-input-price-forecasting/
├── data/                  # Raw & processed data
├── notebooks/             # Jupyter notebooks for EDA & modeling
├── src/                   # Core scripts (scraping, forecasting, automation)
├── dashboards/            # Power BI files (.pbix)
├── sql/                   # DB schema and query scripts
├── reports/               # Charts & visual outputs
├── requirements.txt       # Python dependencies
└── README.md              # This file