This project builds a Random Forest Regressor model to predict a target variable using a provided dataset. It includes code for data loading, preprocessing, model training, evaluation, and feature importance visualization.
- Loads CSV data using Pandas.
- Preprocesses data by handling missing values and scaling features.
- Trains a Random Forest Regressor with 10-fold cross-validation.
- Evaluates model performance using mean absolute error (MAE).
- Visualizes feature importances to understand their impact on predictions.
- Python 3.x
- Installed libraries: pandas, scikit-learn, matplotlib, numpy
git clonethe repositorypython gala-groceries__modelling.py
- Hyperparameter Tuning: Explore optimizing model parameters for better performance.
- Experimentation: Test with different datasets and target variables to assess model's adaptability.