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3. **Prediction Decoding**: After making predictions, we use `inverse_transform` on the `LabelEncoder` to convert the numerical predictions back to their original categorical labels. ### Additional Improvements: - **Feature Engineering**: Consider adding more features to your model if available, such as file extensions, metadata, etc. - **Hyperparameter Tuning**: Experiment with different hyperparameters of the `RandomForestClassifier` to potentially improve accuracy. - **Cross-validation**: Use cross-validation to ensure the model generalizes well to unseen data. ### Example Usage: Assuming your CSV file `document_types.csv` looks something like this: ```csv Document Type,Size (MB) Text,1.0 Image,2.0 Text,1.5 Video,3.0 Text,1.2 Spreadsheet,0.5 Presentation,0.8 Audio,0.3 Database,2.5 Email,0.7 ``` Running the above code will preprocess the data, train the model, make predictions, and print the predicted document types in their original categorical form. Let me know if you need further assistance or have additional questions!