Enhanced Model Training

Train machine learning models for energy production and price prediction

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1. Dataset

2. Features

3. Model

4. Configure

5. Train

6. Results

Select or Upload Dataset

Upload Custom Dataset Supported formats: CSV, Excel (.xlsx, .xls)

Save dataset for future use If unchecked, dataset will only be used for this training session

— OR —

Select from Available Datasets

Public Datasets

My Datasets

Select Features and Target

Target Column (what to predict)

Select Features (input variables) Select the columns to use as input features for training

Choose Model Types (Select one or more)

Select multiple models to train and compare their performance side-by-side.

Configure Training

Test Set Size Portion of data to use for testing (0.1-0.5)

Random Seed For reproducible results

Enable Hyperparameter Tuning (slower but may improve results)

Save trained model to disk (uncheck to only download) If unchecked, model will only be available for download after training

Model Name Leave empty for auto-generated name (model_type_timestamp.pkl). Don't include .pkl extension. Save Location Choose where to save your model

Training in Progress

Initializing training...

Waiting to start...

Training Results

Model trained successfully!

Performance Metrics

Training Set

Test Set

Visualizations

Model Information

Training History

View all past training runs and their metrics

Model Type

Status

Limit

Date	Model Type	Target	Test R²	Test RMSE	Test MAE	Duration	Status
No training history found. Start training a model to see results here.

Next Steps

Use this model for predictions → Compare with other models → Run a battery simulation →