anemoi-training

Create a mini-application named 'WeatherPredictor' using the Python package 'anemoi-training'. This application aims to predict weather conditions based on historical data. It should include the following steps and features:

1. Data Collection: Fetch historical weather data from an API or a dataset. Ensure the data includes temperature, humidity, wind speed, and other relevant parameters.
2. Data Preprocessing: Use 'anemoi-training' to preprocess the collected data. This includes cleaning the data, handling missing values, and scaling the features if necessary.
3. Model Training: Utilize 'anemoi-training' to train a machine learning model on the preprocessed data. Consider experimenting with different types of models such as linear regression, decision trees, or neural networks to find the best fit for the data.
4. Model Evaluation: Evaluate the trained model using appropriate metrics like accuracy, precision, recall, F1-score, etc., provided by 'anemoi-training'. Visualize the performance using plots and graphs.
5. Prediction: Implement a feature where users can input current weather conditions, and the app predicts future weather conditions using the trained model.
6. User Interface: Develop a simple web interface using Flask or Django where users can interact with the application, upload datasets, and view predictions.
7. Documentation: Write comprehensive documentation explaining how to install and use 'WeatherPredictor', including setup instructions and examples.

The goal is to create an engaging and educational application that showcases the capabilities of 'anemoi-training' while providing practical value in predicting weather conditions.