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class-imbalance-problem

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This repository contains the complete project for Emotion Detection using Convolutional Neural Networks (CNNs) and the FER-2013 dataset. The project focuses on addressing class imbalance, applying data augmentation techniques, and leveraging advanced architectures such as VGG16 and ResNet50v2 to improve the robustness and accuracy.

  • Updated Jun 25, 2024
  • Jupyter Notebook

Performed feature engineering, cross-validation (5 fold) on baseline and cost-sensitive (accounting for class imbalance) Decision trees and Logistic Regression models and compared performance. Used appropriate performance metrics i.e., AUC ROC, Average Precision and Balanced Accuracy. Outperformed baseline model.

  • Updated Jul 24, 2023
  • Jupyter Notebook

Classification of movies as 'Fresh', 'Rotten', 'Certified-Fresh' using categorical predictors as well as review sentiment. Performed feature encoding and used Decision Tree, Random Forest Classifiers. Tackled class imbalance issues by assigning weights to classes. Used tokenization to generate word vectors for reviews to predict movie status.

  • Updated Jul 19, 2023
  • Jupyter Notebook

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