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training_features_anonymized.csv - Dataset for model training (926 rows × 589 columns)BuildModels_open_source.ipynb - Self-contained notebook for training prediction modelstraining_features_anonymized.csv (926 harvest observations).joblib files for future usetch_model.joblib - Yield prediction modeltch_encoders.joblib - Label encoders for categorical variablesnumpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
lightgbm>=3.3.0
matplotlib>=3.4.0
seaborn>=0.11.0
jupyter>=1.0.0
joblib>=1.0.01# Install dependencies
2pip install numpy pandas scikit-learn lightgbm matplotlib seaborn jupyter joblib1# Navigate to the notebook directory
2cd open_source_model/
3
4# Launch Jupyter
5jupyter notebook
6
7# Open BuildModels_open_source.ipynb and run all cells1import joblib
2import pandas as pd
3
4# Load models and encoders
5tch_model = joblib.load('tch_model.joblib')
6tch_encoders = joblib.load('tch_encoders.joblib')
7
8# Prepare your data (must have the same features)
9# X = pd.DataFrame(...) # Your feature data
10
11# Make predictions
12tch_predictions = tch_model.predict(X)