README.md

Python Pandas - Video Game Data Analysis

Heroes of Pymoli

In this project, I had been assigned the task of analyzing a gaming companies data for their most recent fantasy game Heroes of Pymoli.

As a first task, the company wanted to generate a report that breaks down the game's purchasing data into meaningful insights.

I was tasked to create multiple dataframes, to discover solutions to the following questions:

Player Count

• Total Number of Players

Purchasing Analysis (Total)

• Number of Unique Items
• Average Purchase Price
• Total Number of Purchases
• Total Revenue

Gender Demographics

• Percentage and Count of Male Players
• Percentage and Count of Female Players
• Percentage and Count of Other / Non-Disclosed

Purchasing Analysis (Gender)

The below each broken by gender

Age Demographics

The below each broken into bins of 4 years (i.e. <10, 10-14, 15-19, etc.)

Top Spenders

Identify the the top 5 spenders in the game by total purchase value, then list (in a table):

Most Popular Items

Identify the 5 most popular items by purchase count, then list (in a table):

Most Profitable Items

Identify the 5 most profitable items by total purchase value, then list (in a table):

Observations:

After reviewing all the data following observation can be made:

1. There are a total number of 576 players in game out of which around 84% are male who are purchasing the items.
2. The highest number of buyers belong to the age group of 20-24, with a total count of 258, which is 44.79% of the total players
3. The most profitable and most popular item is "Oathbreaker, Last Hope of the Breaking Storm" with Item ID = 178, while its not the most expensive its quite popular and high sales have lead to highest revenue from the item.
4. The top spender is "Lisosia93", maybe a targeted promotion can be run to capture more from this and top five buyers.