​Machine Learning in Agriculture: Maize Price Prediction with XGBoost & Climate Indices

Forecasting System to Predict Future Soybean Prices Using:
✅ Historical Soybean Price Data
✅ Climate Indices (NINO3.4, SOI, IOD, NAO)
✅ Economic Indicators (CPI & GPR — Consumer Price Index & Geopolitical Risk Index)
✅ Time-Series Forecasting
📐 This Project Uses
Feature Engineering: Extracts cyclical temporal insights using trigonometric sine/cosine monthly transformations to capture crop seasons natively.

Lag Features: Generates t-1 to t-3 past target variables to capture immediate market inertia.

Rolling Mean: Implements moving windows to smooth out random price volatility and catch macroeconomic baselines.

Walk-Forward Validation: Employs a strict out-of-sample temporal split to respect time order and completely eliminate data leakage.

Recursive Multi-step Forecasting: Loops predicted outputs back into the feature matrix recursively to build an autoregressive prediction engine for future horizons.
💻 The Dashboard Visualizes:
📈 Actual vs. Predicted Prices: A clean time-series graph tracking model overlay against true market history.
📊 Forecast Skill Metrics: Interactive plots mapping how evaluation scores scale across different forecast leads.
📅 Next 6-Month Forecast: A production-ready projection line outlining expected maize market paths over the next two quarters.
🛠️ Technologies Used
Python: Main development core.
Pandas: For time-series alignment, tracking, and forward-filling dataset gaps.
NumPy: For high-performance matrix algebra and numerical arrays.
Scikit-learn: For data scaling, pipeline building, and ensemble model execution.


🎯 Project Features
✔️ Machine Learning Forecasting:Moves away from static statistics to leverage dynamic ensemble tree regression.
✔️ Climate & Economy Aware: Incorporates planetary weather cycles (El Niño, Monsoons) along with consumer inflation and global risk indicators to provide a true multi-domain predictive feature set.
✔️ Interactive Dashboard: Serves real-time data visualizers directly to a local or web-hosted browser link.
✔️ Time-Series Analysis: Designed defensively to tackle complex temporal dependencies, trend components, and seasonality.

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