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Framework for ranking prediction based on Multi-Layer Perceptron (MLP) regressor model and historical datasets evaluated by experts using Multi-Criteria Decision Analysis (MCDA) methods in Python.

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MLP-MCDA

Framework for ranking prediction based on Multi-Layer Perceptron (MLP) regressor model and historical datasets evaluated by experts using Multi-Criteria Decision Analysis (MCDA) methods in Python.

The main_ann.py file includes:

  • Application of machine learning models from scikit-learn Python library:

    • MLPRegressor
    • LinearRegression
  • And other methods:

    • GridSearchCV
    • cross_val_score
    • r2_score
    • train_test_split
  • This framework uses the TOPSIS method from pyrepo-mcda Python package. You can install it via the pip command:

pip install pyrepo-mcda
  • And Gini coefficient-based weighting method from crispyn Python package. You can install it via the pip command:
pip install crispyn
  • Preparation of training and test datasets with feature values.
  • Generation of the target variable representing MCDA score.
  • Splitting dataset to train and test.
  • Selection of the best hyper-parameters for MLP regressor model using GridSearchCV.
  • Training and testing MLP regressor model in prediction rankings.
  • Comparing MLPRegressor model with LinearRegression model.
  • Determining the correlation between rankings.
  • Results visualizations using column, line, scatter, and heat map.

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Framework for ranking prediction based on Multi-Layer Perceptron (MLP) regressor model and historical datasets evaluated by experts using Multi-Criteria Decision Analysis (MCDA) methods in Python.

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