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Predicts customer ratings on a scale of 1 to 5 using review and summary data. Employs XGBoost and LSTM models, showcasing data exploration, cleaning, and NLP feature engineering for accurate multi-class rating predictions.
Contains my project code for two CNN models, one trained for binary classification while the other made for multi-class classification. It utillises the CIFAR-10 dataset.
The CGI2Real_Multi-Class_Image_Classifier categorizes humans, horses, or both using transfer learning from Inception CNN. Trained on synthetic images, it can also classify real ones.
This project showcases a dataset of Amazon Reviews in Hindi, which we created ourselves. We applied various machine learning methods including Naive Bayes, SVM, and Decision Tree, using both Bag-of-Words and TF-IDF. Additionally, we experimented with deep learning techniques such as Feedforward Neural Networks and LSTM with ELMO embeddings.
This repository contains assignments, the final course project, and the project work assigned for the Natural Language Processing (NLP) course within the Artificial Intelligence Master's program.
This project applies SVM classifiers and K-Means clustering to the Anuran Calls (MFCCs) dataset for multi-class, multi-label classification, evaluating techniques like binary relevance, SMOTE, and Classifier Chains to optimize label prediction accuracy.