There are 4 repositories under classifiers topic.
Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently trajectories.
A Zeek script to generate features based on timing, volume and metadata for traffic classification.
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
All Machine Learning Algorithms
Fake News Detection - Feature Extraction using Vectorization such as Count Vectorizer, TFIDF Vectorizer, Hash Vectorizer,. Then used an Ensemble model to classify whether the news is fake or not.
ACL 2021 paper "Style is NOT a single variable: Case Studies for Cross-Style Language Understanding " by Dongyeop Kang and Eduard Hovy
The main aim of the project is to develop a sentiment analyzer that can be used on twitter data to classify it as positive or negative. Our project takes care of the challenge of bilingual comments, where people tweet in two languages, in this case Hindi and English, in the Latin Alphabet.
Machine Learning Modelling On Regression & Classification Problems
Deploy image classifier on a static website using javascript.
ObjectDetector uses OpenCV Haar cascade classifiers to detect different objects in images and videos with Python. For now, this repository includes my trained haar cascade classifier for detecting cars, the default haar cascade classifier for human faces (haarcascade_frontalface_default), a classifier for bananas from CodingRobin and a classifier for wallclocks which are used and tested in programs, detecting the objects from image/video, comparison between different human body parts classifiers and some other programs, which (will) help training the classifiers (for example, a program downloading the "cat box" synset images from ImageNet).
Library for computing classifier Learning Curves & iPython notebooks to improve your learning curve for using Learning Curves for ML research and practice!
Various classifiers using bayesian networks, for Knowledge Representation class at UNIBO
This is an official Leaderboard for the RuSentRel-1.1 dataset originally described in paper (arxiv:1808.08932)
Registered Software. Official code of the published article "Automatic design of quantum feature maps". This quantum machine learning technique allows to auto-generate quantum-inspired classifiers by using multiobjetive genetic algorithms for tabular data.
Airbnb Price in NYC ( Select Boroughs )
🏀 Hardcoded ML classifiers from scratch to create predictive models on the outcomes of NBA games!
Making a dataset of poems and try categorising them in 4 genres using two different classifiers.
An AI application that can detect the smile on the face(s) in real time i-e from a webcam or even a captured video.
A research on how macroeconomic, microeconomic factors and personal data could affect mortgage risk using Machine Learning techniques.
A convolutional neural network for human emotion classification
Hotspotter is software used to classify energetic hot spots of protein:protein interaction
Fake News Detection on Liar dataset
Following tutorials from Josh Gordon.
Machine learning course for graduate students
Classification of a music sample among 10 genres (rock, jazz, blues...) using several models
Combinin' several ML techniques into one predictive model.
Photometric light curves classification with machine learning
Driver's Consciousness Level Analysis using EEG Signals
A collection of substantial projects I completed for the CS156 Machine Learning course.
This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.
Analyzed a 23-feature dataset, targeting 'RainTomorrow' for weather insights. Conducted thorough data gathering, preprocessing, and feature selection. Evaluated diverse models (Logistic Regression, Random Forest, Decision Trees, K-means, K-nearest neighbors, Hierarchical clustering) and employed technical metrics for in-depth performance analysis.
Decision Tree Classifier is used in this wine data to test the quality of wine. Additional study about entropy and information gain provides overall understanding about how this classifier works in order to predict.
The Diabetes Prediction Application serves as a valuable tool for individuals to assess their risk of diabetes based on personal health information. By leveraging machine learning techniques, the application provides accurate predictions, aiding users in making informed decisions about their health.
This project is a web application for predicting loan approval status based on various financial and personal attributes. It uses a machine learning model that I trained on historical loan data to make predictions. I built the web application using Flask for the web framework, SQLite for the database, and the pre-trained model saved with joblib.