Prediction of the stage of liver cirrhosis of a patient

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.

Multi-label classification using LLMs, with additional enhancements using quantization and LoRA (Low-Rank Adaptation). Get better performance on GPU.

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.

Validation (like Recursive Feature Elimination for SHAP) of (multiclass) classifiers & regressors and data used to develop them.

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 a multiple disease prediction based on user input which can predict upto 40 disease and trained on 131 parameters

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.

Class-imbalanced Ensemble Learning in Python. | 类别不平衡/长尾机器学习库

Condensed-Gradient Boosting

Multi-Classification ML modelling on a payment's database to predict any of the 3 states of a payment in a company.

Multi-class classification of drug resistance in MTB clinical isolates

Models for multi-class classification implimented in pytorch

Extremely simple and fast extreme multi-class and multi-label classifiers.

Identifying handwritten digits with a single layer perceptron based multi-class linear regression model.

KNN Classifier for Car Evaluation Dataset: Explored impact of training sample sizes & optimal K value selection. Python implementation in a Machine Learning course project (uOttawa 2023).

The objective of this study is to classify customers based on their information about their demographics and financial information into these 3 categories and predict if the customer is going to have good or bad credit score.

Detects the stress level of an individual using Machine Learning Algorithms

Implementaiton of BSC-Densenet-121 in Pytorch from research paper "Adding Binary Search Connections to Improve DenseNet Performance".

'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.