amsdal_ml

Create a predictive maintenance tool using the 'amsdal_ml' package, which is integrated within the AMSDAL framework. This tool will be designed to predict potential failures in industrial machinery based on historical data. The application should include the following key features:

1. Data Import: Users should be able to upload their own dataset containing historical machine performance metrics and failure events.
2. Preprocessing: Implement basic data cleaning and preprocessing steps such as handling missing values, scaling numerical features, and encoding categorical variables.
3. Model Training: Use 'amsdal_ml' to train a predictive model. This could involve selecting an appropriate algorithm from the package, training it on the preprocessed data, and tuning hyperparameters for optimal performance.
4. Prediction: Once trained, the model should be capable of predicting whether a given set of machine performance metrics indicates an impending failure.
5. Visualization: Provide visual representations of the prediction results, including graphs showing predicted vs actual failure times and other relevant statistics.
6. Documentation: Include clear documentation explaining how each part of the application works, especially regarding the use of 'amsdal_ml'.

The goal is to create a user-friendly interface where non-expert users can easily input their data, run predictions, and understand the outcomes without needing deep knowledge about machine learning techniques.