video1.0<iframe src="https://www.loom.com/embed/ecfdfdaed2eb4cc48ff7e95aebf8c7a8" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/ecfdfdaed2eb4cc48ff7e95aebf8c7a8-2e44302318b9de69.gif1434.859In this video, I dive into exploratory data analysis (EDA) to understand user behavior and identify potential fraud patterns. I highlight that about 3% of users in our dataset are flagged as fraudsters, which is a small but significant number. I also discuss the importance of analyzing categorical variables and their association with fraud rates. Please take a look at the data insights and let me know your thoughts on the findings.