Everyone knows now a days ,how the gaming industry is booming up in the current generation across the years . A single game is performing the well- known sales in global market. Making a billion or million in dollars for publishing company. In 1971 the first digitalized game is created and impacting on the world and started to attract the people. Later gaming industry become the well- known in global market.
So, i had an thought about the building analysis project about the video game sales. One of the video game selling platform is given below in a image.
When i started to to search the dataset for analysis, found out a amazing website (https://mavenanalytics.io/). Under this website found a video games sales dataset(1971–2020).
Let's dive into the technical works of the analysis and visualization.
For this i have used a tools like the python and powerbi.
Using the pandas library in python imported the excel dataset in python interface. Then started to check the columns in the dataset. Afterwards then started to check the count of the duplicated values and null values. Dropped the unwanted columns present in the dataset(eg: "img","last_update"). and replaced the null values. Removed the value named "all" in console column because i want analysis the every console individually. check the below for the codes.
lib:
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
code:
df=pd.read_csv(r"C:\Users\karth\Downloads\Video+Game+Sales\vgchartz-2024.csv")- import. df.isnull().sum()- null values count check.
df.duplicated().sum()- duplicate value count check.
df=df.drop(["img","last_update"], axis= 1) — drop columns.
df[['total_sales', 'na_sales', 'jp_sales', 'pal_sales', 'other_sales']] = \ df[['total_sales', 'na_sales', 'jp_sales', 'pal_sales', 'other_sales']].fillna(0)- replacing null value.
df = df[df['console'] != 'All']- removing particular value in column.
df.to_csv("video game analysis.csv", index=False)- exporting to excel.
Then move forward to powerbi visualization.
For a intro :
from the above image we can analyze the "how the region market performed in global sale".
from the above image we can analyze the "video games sales by year and genre-based filtering".Here powerbi tool helped alot for it.
from the above image we can analyze the "how well the developer and published the game with more count of sales and unique games released those years".
from the above image we can analyze the "which game is saled mostly in a year, console, genre".
By analyzing these patterns, stakeholders such as game developers, publishers, and marketers can make more informed decisions regarding game development, marketing strategies, and platform investments. The dashboard also highlights how consumer preferences evolve over time, helping businesses stay competitive in the rapidly growing gaming industry.
Overall, this project demonstrates how data analytics and visualization tools can simplify complex datasets and reveal meaningful insights. With further enhancements — such as predictive analytics and real-time data integration — the dashboard could become an even more powerful tool for understanding and forecasting trends in the video game market.