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.
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.
The dataset used in this project consists of product reviews obtained from credit card transactions.
The collected data undergoes preprocessing to clean and prepare it for analysis, ensuring data quality and consistency.
Exploratory data analysis techniques are applied to gain insights into the dataset's characteristics, distribution, and patterns.
Data visualization techniques are utilized to represent the dataset graphically, aiding in understanding and interpreting the data effectively.
Feature extraction methods are employed to identify and extract relevant features from the dataset, enhancing model performance.
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.