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 company can earn on average from new customers based on these segments.
For example: The company wants to determine how much a 25-year-old male user from Turkey, who is an IOS user, can earn on average to the company.
Persona.csv data set 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 singularized. In other words, a user with certain demographic characteristics may have made more than one purchase.
- Price: Customer's Spending Amount
- Source: Customer's Device
- Sex: Customer's Gender
- Country: Customer's Country
- Age: Customer's Age
View of dataset before process
PRICE SOURCE SEX COUNTRY AGE
39 android male bra 17
39 android male bra 17
49 android male bra 17
29 android male tur 17
49 android male tur 17
View of dataset after process
CUSTOMER_LEVEL_BASED PRICE SEGMENT
bra_android_female_0_18 35.6453 B
bra_android_female_19_23 34.0773 C
bra_android_female_24_30 33.8639 C
bra_android_female_31_40 34.8983 B
bra_android_female_41_66 36.7371 A
- Clone this repository
https://github.com/nedimcanulusoy/Rule-Based-Classification.git
- Change directory to the cloned repository
cd Rule-Based-Classification
- Open the notebook and run the cells
The data set is not included due to General Data Protection Regulation (GDPR) rules.