- Jupyter Notebook Script displaying the code used to analyse and visualise the data
- Analysis file stating the three observed trends from analysis
Congratulations! After a lot of hard work in the data munging mines, you've landed a job as Lead Analyst for an independent gaming company. You've been assigned the task of analyzing the data for their most recent fantasy game Heroes of Pymoli.
Like many others in its genre, the game is free-to-play, but players are encouraged to purchase optional items that enhance their playing experience. As a first task, the company would like you to generate a report that breaks down the game's purchasing data into meaningful insights. Your final report should include each of the following:
* Total Number of Players
* Number of Unique Items
* Average Purchase Price
* Total Number of Purchases
* Total Revenue
* Percentage and Count of Male Players
* Percentage and Count of Female Players
* Percentage and Count of Other / Non-Disclosed
The below each broken by gender
* Purchase Count
* Average Purchase Price
* Total Purchase Value
* Average Purchase Total per Person by Gender
The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.)
* Purchase Count
* Average Purchase Price
* Total Purchase Value
* Average Purchase Total per Person by Age Group
Identify the the top 5 spenders in the game by total purchase value, then list (in a table):
* SN
* Purchase Count
* Average Purchase Price
* Total Purchase Value
Identify the 5 most popular items by purchase count, then list (in a table):
* Item ID
* Item Name
* Purchase Count
* Item Price
* Total Purchase Value
Identify the 5 most profitable items by total purchase value, then list (in a table):
* Item ID
* Item Name
* Purchase Count
* Item Price
* Total Purchase Value