Machine learning library for classification tasks

Machine learning library for classification tasks

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

This project focuses on the detection of credit card fraud using various data science and machine learning techniques. The dataset includes a record of credit card transactions over a specific period, with the goal of accurately identifying fraudulent activities. 🚀✨

This repository serves as a storage space for classification projects. Organized by projects, each directory houses code files, documentation, and dataset necessary for running and understanding the project. Feel free to explore the projects. Reach out for inquiries, feedback, or collaboration opportiunities.

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

🟣 Classification Algorithms interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.

Repository to store code and study material for the Internship

Predict loan approval by using different variable selection methods

Identify a list of customers who will subscribe to Term Deposit Account using the classification problem

This is a Machine Learning model designed to analyze various factors that contribute to Employee Turnover including job satisfaction, last evaluation, number of projects, average monthly hours, time spent in company, accidents at workplace, promotion in 5 years, department and salary.

The project deals with predicting the number of persons killed based on the contributing factors such that necessary precautions and actions can be taken in order to avoid the accidents and reduce the death rates and injuries of the person in the New York city.

This project aims to develop a machine learning model that can accurately classify an individual's credit score between ["Good", "Standard","Poor"]. The model was trained using a supervised learning algorithm, Random Forest, on a dataset of credit score data.

Glaucoma and Non-Glaucoma classification using ML/Dl and ensemble approaches using Image Feature Extraction Using HOG (Histogram of Gradient)

This repo contains machine different learning algorithms.

This is the repository for all the resources (code, notes and guides) used during the ML Study Jams 2022-23 program hosted at GDSC-TIU. (Maintainer: Aryan Pareek @diffrxction)

A Streamlit application to play with machine learning models directly from the browser

Comparison of Different Machine Learning Classification Algorithms for Breast Cancer Prediction

This is non-optimized code intended solely to test whether or not quantum classification works with amplitude encoding.