This is for AI Project team project.

Data Pipeline[data_process.py] : Data loading, Data preprocessing, Data visualization, Data save(Make new file after processing)

- input : .csv file

- output : processed .csv files(train/test/valid[optional])

Evaluation and Visualization[eval_vis.py] : Make evaluation score for each result of three models and visualize the result for comparing the effects of models

- input : np.array which is the result of each models

- Calculate RMSE, R-Squared

- Visualizer these scores with graph

Three models : MLP[MLP.py] , LSTM[LSTM.py] , Transformer[Transformer.py]

- Develop customized model for regression based on nn.Module

- forward, train, eval method

- eval output : np.array

Main[On colab notebook] :

- Preprocess Data 
    
- load train , test , valid[optional] dataset
    
- Train three models with train() method
    
- Evaluation and Visualization
    - Grid Search for hyperparameterization