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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning (ML)-based and Statistical Analysis Mental Resilience of Students\n# Descriptions of each python file:\n•\tRegression \u0026 Correlation folder contains python code for finding out correlations and regression results.\n\n•\tOutputs folder contains results of different programs generate.\n\n•\tageBinning.py: This code was used for making age categorical using binning.\n\n•\tClusteringResilienceK_1_10.py: This program uses find cluster on resilience score (cluster 2 to 10) \n\n•\tClusteringResilienceK_Fixed_GenerateLevels.py: This program generates resilience levels based on input value and save two csv files.\n\n•\tCrossValidation.py: For classification of 6 classes with SMOTE.\n\n•\tfeatureImportance_Correlation.py: Find the feature importance and correlation saves in CSV file in outputs folder.\n\n•\tFindCorrelHeatmap.ipynb: This file should be run in Jupyter Notebook to find the correlation heatmap.\n\n•\thypterparameterTuning.py: This program generates the hyper-parameter tuning results on validation set and test set also. 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