This repository is about a trained Machine Learning model which predicts Whether the Heart Disease is present or not by considering few factors. This ML model is slected by considering different accuracies of various trained ML models.

JVector: the most advanced embedded vector search engine

Gretl implementation of the knn machine-learning algorithm

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai

Instance-based uncertainty estimation for gradient-boosted regression trees

This study uses predictive analytics to detect stroke risk factors early, aiming to reduce occurrences. By analyzing risk factors with machine learning, it uncovers patterns and correlations. Models such as Logistic Regression, KNN, Decision Trees, Random Forest, and Neural Network.

This repository contains two implementations of a K-Nearest Neighbors (KNN) classifier for predicting online shopping behavior. The classifiers are implemented in Python and use different approaches for finding the nearest neighbors: Naive Implementation, KDTree Implementation

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

A compilation of solutions for the KNN problem.

This Repository consists of algorithms related to AI-ML. Few examples include - KNN, Naive Bayes, Decision Trees, etc.

This repository contains code for building a K-Nearest Neighbors (KNN) model to predict diabetes based on patient data. Includes data cleaning, hyperparameter tuning, and evaluation metrics.

All Assignments of the course, Statistical Methods in AI at IIITH, Monsoon 2024

Predicting Credit Card Defaults This repository provides a step-by-step tutorial on predicting credit card defaults using machine learning algorithms in Python with scikit-learn. Learn to implement SVM, Random Forest, Decision Trees, k-Nearest Neighbors, and Artificial Neural Networks to forecast default payments for credit card clients.

This project classifies images from the Flower102 dataset using k-means clustering followed by K-Nearest Neighbors (KNN) classification. It optimizes KNN parameters to achieve high accuracy, with the best results obtained using 7 clusters and 5 nearest neighbors.

building machine learning models that use classic algorithms and deep learning, as well as comparing their accuracy

PostgreSQL vector database extension for building AI applications

The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.

Dive into the world of Machine Learning in this immersive lab course, exploring open-source tools and algorithms such as random forest, SVM, linear regression, PCA, K-means, LDA, KNN, decision tree, and more. Engage in real-world ML projects and deploy your models, gaining practical experience in the forefront of AI technology.