This repository contains all my Machine Learning projects.

the application of an intelligence system to classify and identify fraud situations.

contains project related to python

Credit card fraud is a significant global issue, posing challenges for financial institutions due to the low incidence of fraud amid a high volume of legitimate transactions.

Data Science Internship at CodSoft

Credit Card Fraud Detection using ML

The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.

Machine Learning for Credit Card Fraud Detection

Using a self organizing map (SOM) to identify frauds in the credit card application process.

Titanic Survival Prediction, Movie Rating Prediction With Python, Iris Flower Classification, Sales Prediction Using Python, Credit Card Fraud Detection

The objective of this project is to develop and utilize autoencoders for detecting anomalies in credit card transactions.

In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.

Implementation of an intelligence system to detect the fraud cases on the basis of classification.

Creditcard validator by Using Luhn's algorithm to verify the validity of credit card numbers. It also fetches credit card number data from datasets for testing and analysis.

Credit Card Fraud Detection is a crucial machine learning project with profound implications. It aims to safeguard financial transactions by identifying fraudulent activities. Leveraging advanced algorithms and historical transaction data, this project analyzes patterns and anomalies in credit card usage.

This Python script uses machine learning models to detect fraudulent credit card transactions in a dataset. The dataset is loaded using the pandas library and preprocessed for machine learning by removing irrelevant features and rescaling the data.

This project uses logistic regression models to analyze credit risk. The recommended model, trained with resampled data, shows higher precision and recall scores for predicting high-risk loans. This model helps mitigate credit risk for lending companies.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

This repository contains an implementation of credit card fault detection using Luhn's algorithm. Luhn's algorithm is a checksum formula used to validate credit card numbers, as well as other identification numbers. The algorithm is based on performing a set of arithmetic operations on the digits of a given number, resulting in a checksum value.

This project aims at creating a classifier. It detects whether or not the card transaction is valid. Diverse machine learning algorithms are applied in this project to distinguish between a non-fraudulent and fraudulent transactions.