The popular IRIS dataset is used for the training of linear and non-linear SVM models. The hyper-parameters are fine-tuned of the models are fine-tuned using K-Fold Cross-Validation and GridSearch to improve model performance.

The project involves building models for scoring & segregating users acccording to certain threshold criteria based on features from LinkedIn on threshold criteria-refer ReadMe.

Implemented using Linear SVM(SVC) and Non-Linear SVM(RBF). ML ASSIGNMENT 2 => Q2

This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.

falppy-bird machine-learning program using svm and GAN

A repository that contains practice code for ML algorithms implementation on a cancer treatment medical dataset from Kaggle (Text Processing, Response Encoding, Feature Engineering, Classification).

Made a CNN model for the classification of Dog and Cat images and tested it with lookalike images.

Projet de modélisation supervisée - SCORING

Finding duplicate records using Record Linkage Comparison and BigData through Apache Spark

Optimizing LinearSVC models trained on the MNIST Handwritten Digits dataset, includes ensemble methods and bayesian optimization.

Text Summarization

Machine Learning project - CMP2024 - Computer Engineering - Cairo University

FlairifyMe is a Reddit Flair Detector for r/india subreddit, that takes a post's URL as user input and predicts the flair for the post using a model generated by Logistic Regression.

Linear Support Vector Machine for sentiment analysis of twitter data using Spark MLlib and RDDs.

K-fold cross-validation implemented from scratch to aid in analysis of the MNIST dataset

Research Methodology

Amazon product review sentiment analysis using Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes (NB) multiclass as classifier models, Synthetic Minority Oversampling Technique (SMOTE) as feature oversampler, and TF-IDF vectorization as feature, Synthetic Minority Oversampling Technique (SMOTE) as oversampler, and k-fold CV.

A Google Chrome Extension that estimates the Reliability, Polarity and Subjectivity of any news article on the web. It allows you to like/dislike any article and recommends you articles based on your choices.

Detects facial landmarks in real-time from the input feed of a webcam/usb-camera/pi-cam using a HOG + Linear SVM model.

Machine Learning - Binary Classification