Exploratory Data Analysis In Python: Machine Learning Project Transactional Data

This is the first tutorial video in a series of three, starting-off with understanding the data through exploratory data analysis using the essential Python libraries: pandas, sklearn and seaborn. The data is wrangled by merging dataFrames, pivoting tables, melting column names into categories, reindexing, aggragating, grouping and more available in pandas library. I am walking you through a unique machine learning #project using the transactional data of online casino players. The content of the video is as follows: 00:00 - Intro 00:51 - Data loading 01:45 - Cast datetime data type to a column 02:22 - Describe of non-numeric variables of Data Frame 04:25 - Remove rows on datetime condition 06:08 - Variable understanding 08:03 - Change values in pandas DataFrame column with .map 09:00 - Group data and generate Histograms and Density plots 13:20 - Subset pandas DataFrame rows based on two conditions 14:14 - Generate TOP N barchart 19:39 - Datetime date type operations 21:40 - Heatmap with time data 26:00 - Apply multiple functions on pandas DataFrame columns at once 29:45 - Fill in the missing datetime slots in pandas Data Frame 35:39 - Create a Sankey Diagram for customer lifetime 45:38 - Format a pandas DataFrame for a grouped bar plot .melt 53:27 - Two project ideas and Bye! Datasets: https://data.mendeley.com/datasets/9j5gcygnwg/1 Article: https://www.sciencedirect.com/science/article/pii/S2352340923001956 Give a 🌟 to my code repository: https://github.com/giraffa-analytics/YT_casino_ml_project