Bank Marketing Classifcation machine learning using 6 Models each of models given another accuracy

Machine learning Classification for Family Determination for various generations by their age, height, weight, etc...

UNI S6: K medoids, Gaussian naive bayes & dbscan on SORLIE dataset

Credit-Card-Fraud-Detection project is a binary classification project which predicts the Fraud by using the different classification algorithms

The Titanic dataset includes passenger information such as survival status, ticket class, gender, age, family relations aboard, fare, cabin, and port of embarkation. It's widely used for predictive modeling to understand survival patterns based on passenger attributes.

A simple machine learning model using KNN,GaussianNB,SVC algorithms

This repository contains my work for Summer Analytics '22 conducted by IIT Guwahati

🧠 DMAD - Differential Morphing Attack Detection. A project for Fundamentals of Computer Vision and Biometrics course at the University of Salerno.

（陆续更新）重新整理过的基于机器学习的股票价格预测算法，里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测，包含：LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM、随机森林等

The aim is to create a classifier that indicates whether a requested transaction is genuine or fraudulent.

Performance Comparison of Three Classifiers for the Classification of Breast Cancer Dataset

Final practical work of the Machine Learning Course with Python dictated by the UTN.

This repository contains introductory notebooks for Naive bayes algorithm

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

‘Buy Now, Pay Later’ (BNPL) is a type of short-term financing used by start-ups like Slice, ZestMoney, Simpl, LazyPay, and Uni, are lowering the bars while approving applications. Building models to detect such customers beforehand.

classifying employee attrition

Classifying iris flower dataset by using Naive Bayes classifier

This project is done during the data science T5 bootcamp. Using HR dataset and applying machine learning classification algorithms to predict eligible employees for promotions. Hence save time and effort and expedite the process of promotions in the company.

Predicts the qualified employee for promotion using Classification

Analysis of SMS tagged 5K+ messages collection to classify them as spam or ham. Used Natural Language Processing techniques to transform data into an understandable format.