Real Time Face Recognition with Python and OpenCV2, Create Your Own Dataset and Recognize that. #FreeBirdsCrew

For this project, I used four different classification algorithms to detect fraudulent credit card transactions.

Classification problem using multiple ML Algorithms

This is about machine learning model where there are many algorithms is using to find out best accuracy.

This repository contains some Machine learning algorithms from scratch to better understand how they work, and are implemented under the hood.

Testing 6 different machine learning models to determine which is best at predicting credit risk.

In this data analysis project, we embarked on a comprehensive exploration of Oracle's interview review data scraped from Glassdoor. Our objective was to gain valuable insights into the interview experiences of candidates applying for specific job postings at Oracle.

ML Based Firewall System

Various Machine learning algorithms

ML project focused on predicting Titanic passenger survival using various algorithms and extensive data analysis techniques. This project includes detailed data visualization and interpretation to uncover key factors affecting survival. By leveraging various ML models the analysis aims to achieve high predictive accuracy.

Project on course "Data Mining 2"

Supervised Machine Learning and Credit Risk

Boston Crime Analisys test.

Classifying customers into segments

Exploratory data analysis and machine learning classification models to predict employee attrition.

Develop a prediction model capable of learning to detect whether a transaction is fraudulent or a genuine purchase.

Performed supervised machine learning using oversampling, undersampling and combination sampling techniques to determine credit risk for bank customers.

In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.