Using AI in Fraud Detection |Beginner Data Science Project
In this video, we will explore the Online Payments Fraud Detection dataset available on Kaggle, which contains information about fraudulent and legitimate transactions. We will be using data science techniques to analyze this dataset and build models that can accurately detect fraud in online payments.
The dataset contains a large number of features, including the transaction amount, time of transaction, and details about the customer and merchant involved in the transaction. We will use these features to train machine learning models that can predict whether a given transaction is fraudulent or not.
Throughout the video, we will walk through the various steps involved in data preprocessing, including data cleaning and feature engineering. We will also explore different machine learning algorithms and evaluate their performance in detecting fraud.
By the end of the video, you will have a better understanding of the challenges involved in fraud detection and how data science techniques can be used to tackle them.
If you're interested in following along with the video, you can download the dataset from Kaggle at the following link: https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detection-dataset.
You can also find the code for this project on the GitHub repository of eusebiocidalia.
00:00 - Introduction
02:00 - Agenda
02:49 - Problem
02:55 - Extract & Explore
09:35 - Feature Scaling
09:40 - Logistic Regression
10:00 - Neural networks
11:41 - SMO
12:52 - Lessons learned
#FraudDetection
#OnlinePayments
#DataScience
#MachineLearning
#Kaggle
#Dataset
#Preprocessing
#FeatureEngineering
#PerformanceEvaluation
#GitHub
In this video, we will explore the Online Payments Fraud Detection dataset available on Kaggle, which contains information about fraudulent and legitimate transactions. We will be using data science techniques to analyze this dataset and build models that can accurately detect fraud in online payments.
The dataset contains a large number of features, including the transaction amount, time of transaction, and details about the customer and merchant involved in the transaction. We will use these features to train machine learning models that can predict whether a given transaction is fraudulent or not.
Throughout the video, we will walk through the various steps involved in data preprocessing, including data cleaning and feature engineering. We will also explore different machine learning algorithms and evaluate their performance in detecting fraud.
By the end of the video, you will have a better understanding of the challenges involved in fraud detection and how data science techniques can be used to tackle them.
If you’re interested in following along with the video, you can download the dataset from Kaggle at the following link: https://www.kaggle.com/datasets/rupakroy/online-payments-fraud-detection-dataset.
You can also find the code for this project on the GitHub repository of eusebiocidalia.
00:00 – Introduction
02:00 – Agenda
02:49 – Problem
02:55 – Extract & Explore
09:35 – Feature Scaling
09:40 – Logistic Regression
10:00 – Neural networks
11:41 – SMO
12:52 – Lessons learned
#FraudDetection
#OnlinePayments
#DataScience
#MachineLearning
#Kaggle
#Dataset
#Preprocessing
#FeatureEngineering
#PerformanceEvaluation
#GitHub
