{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/ecfdfdaed2eb4cc48ff7e95aebf8c7a8\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/ecfdfdaed2eb4cc48ff7e95aebf8c7a8-2e44302318b9de69.gif","duration":1434.859,"title":"Exploring Data Analysis for Fraud Detection 📊","description":"In 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."}