A fast, robust Python library to check for offensive language in strings.

A cluster of Machine Learning algorithms

Detecting hate speech using the spoken content of videos using Machine Learning

Tour of Machine Learning Algorithms for Binary/Multiclass Classification

Wrapper on top of liblinear-tools

Using deep learning and transfer learning techniques to differentiate plain roads and those with potholes using three different classifiers to obtain the best accuracy with the same convolutional base

Android malware detection from static features with ML classification algorithms- Decision Tree, K-Nearest Neighbours, Linear SVM and Random Forest.

Forest cover type classification/detection using linear support vector machine implemented with gradient descent (from scratch)

Twitter Sentiment Analysis (Ml hackathon)

Semantic Enrichment, Data Augmentation and Deep Learning for Boosting Invoice Text Classification Performance: A Novel Natural Language Processing Strategy

SMS spam detection pipeline: dual TF-IDF (word+char) → calibrated Linear SVM, nested CV + threshold tuning (F1) + explainability + robustness tests.

Face detection using Facial Landmark prediction and Comparision of standard emotion recognition algorithms

Syracuse University, Masters of Applied Data Science - IST 736 Text Mining

Package provides javascript implementation of support vector machines

This repository demonstrates a complete machine learning workflow for banknote authentication using a hand coded Linear SVM model. It includes data preparation, training loop design, vectorized computation of gradients, model metrics, and a visualization module based on two selected features.

🧠 TumorClassifier-RAW-vs-DIP – an advanced 🔬 medical imaging platform 🏥 with 🤖 AI-powered brain tumor classification 🧬 (MRI analysis 📊), 🔄 image preprocessing pipeline (RAW vs DIP comparison 📈), ⚡ Linear SVM classifier 🎯 for tumor detection, 📊 performance metrics visualization 📉, interactive web 🌐 interface for real-time predictions.

Performed feature selection using F-score method to filter out the important features in order to optimize the performance of Linear SVM machine learning model. The accuracy achieved was above 63%.

Prediction of diabetes health indicators for machine learning class final project

Statistical Pattern Recognition (classic machine learning)