Welcome back to AnalyticalAnuj!

In this Part 2 of the UPI Fraud Detection Machine Learning series, we focus on Exploratory Data Analysis (EDA) and Data Visualization, essential steps in building a fraud detection model. This uncut video walks you through how to deeply understand your dataset and uncover hidden patterns in UPI transactions, vital for creating a robust machine learning model.

We’ll cover key steps, including handling missing data, visualizing distributions, and generating meaningful insights through Python’s Pandas and Seaborn libraries. This content is perfect for data scientists, Python developers, and anyone preparing for interviews in data science, financial fraud detection, and AI.

If you missed Part 1, be sure to check it out where we dive into the fundamentals of RAW data analysis for UPI Payment Systems.

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Timestamps:

00:23 - Introduction
04:00 - Extracting fraud dataset
07:08 - Analyzing fraud dataset
08:37 - Correlation analysis
14:54 - Visualizing the entire dataset
16:45 - Fraud distribution by transaction type
19:23 - Fraud distribution by payment gateway
20:16 - Fraud distribution by merchant category
21:30 - Distribution of transaction amount
22:32 - Transaction frequency vs. fraud (scatter plot)
23:28 - Days since last transaction vs. fraud (scatter plot)
24:08 - Transaction state vs. fraud (bar plot)
24:32 - Device OS vs. fraud (bar plot)
Sample Dataset Link -

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