This is the first tutorial video in a series of three, startingoff 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 nonnumeric 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/9j...
Article: https://www.sciencedirect.com/science...
Give a to my code repository: https://github.com/giraffaanalytics/...