PROBLEM STATEMENT-
To build a predictive model for crop yield estimation using Soil Quality (pH), Weather Conditions, and Past Agricultural Data (past_crop_yield) to help in agricultural decision-making.
MODEL PERFORMANCE SUMMARY -
| Model | R² Score | RMSE | MAE |
|---|---|---|---|
| Linear Regression | 82.44% | 92.88 | 74.87 |
| Random Forest Regressor | 99.30% | 18.54 | 8.05 |
| Decision Tree Regressor | 99.62% | 13.65 | 1.15 |
| Gradient Boosting Regressor | 87.32% | 78.92 | 64.69 |
| XGBoost Regressor | 99.33% | 18.10 | 10.91 |
Best Model: Decision Tree — highest accuracy and lowest error, making it ideal for crop yield prediction.
RESULT-
