Skip to content

This python project is about Credit card fraud detection using machine learning algorithm

Notifications You must be signed in to change notification settings

raz455/Credit_Card_Fraud_Detection_Project

Repository files navigation

Credit Card Fraud Detection Project

Algorithm: Random Forest Supervised Machine Learning Algorithm

Random Forest is a supervised machine learning algorithm based on ensemble learning principles. It combines multiple decision trees to make predictions, resulting in robust and accurate models.

Overview

This project focuses on detecting credit card fraud using the Random Forest Algorithm. The dataset used comprises product reviews collected from credit card transaction records.

Data Collection

The dataset used in this project consists of product reviews obtained from credit card transactions.

Data Pre-processing

The collected data undergoes preprocessing to clean and prepare it for analysis, ensuring data quality and consistency.

Data Exploration

Exploratory data analysis techniques are applied to gain insights into the dataset's characteristics, distribution, and patterns.

Data Visualization

Data visualization techniques are utilized to represent the dataset graphically, aiding in understanding and interpreting the data effectively.

Feature Extraction

Feature extraction methods are employed to identify and extract relevant features from the dataset, enhancing model performance.

Model Evaluation

Model evaluation is conducted to assess the performance of different machine learning models and select the most suitable one for credit card fraud detection.


🔍 This project utilizes machine learning techniques to identify and prevent credit card fraud, safeguarding financial transactions and enhancing security measures.

About

This python project is about Credit card fraud detection using machine learning algorithm

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published