You can find jupyter notebook and dataset on my github.
BUSINESS PROBLEM
A game company wants to create level-based new customer definitions (personas) by using some features of its customers, and to create segments according to these new customer definitions and to estimate how much the new customers can earn on average according to these segments.
For example: It is desired to determine how much a 25-year-old male user from Turkey, who is an IOS user, can earn on average.
STORY OF DATASET
-Persona.csv dataset contains the prices of the products sold by an international game company and some demographic information of the users who buy these products. The data set consists of records created in each sales transaction. This means that the table is not deduplicated. In other words, a user with certain demographic characteristics may have made more than one purchase.
VARIABLES
-Price: Customer's spending amount
-Source: The type of device the customer is connecting to
-Sex: Gender of the client
-Country: Country of the customer
-Age: Customer's age
STEPS
First i load all the neccessary libraries.
Then, i check null values and value types.
I check country variables by price by using pivot table.
Also,i check "Source" values and "County" values by "Price" in terms of standart deviation and mean.
And want to check differences between ios and android in terms of Price. Also, i examined "Source" in terms of "Country" and "Price".
Also i segmented to price according to "Age" and "Sex" values.
And i assign this pivot table to the dataframe.
I categorized ages between 15-18,18-30,30-45,55-65 age gaps.
I combine all column values into one column to create segmentation.
Also, i categorize prices into 4 categories.
Based on this segmentation, i can find price segmentation of customer based on their info.
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