Video Game Sales Analysis

Project Overview

This project involves an analytical exploration of a video game sales dataset, focusing on sales trends, influential game publishers, and platform-specific bestsellers. The analysis aims to uncover insights into the global distribution of video game sales, identify leading publishers, and understand market preferences across different geographic regions and platforms.

Dataset

The dataset comprises historical sales data of video games, including the following attributes:

• Name: The name of the video game.
• Platform: The platform on which the game was released (e.g., PC, PS4, XBox).
• Year: The release year of the game.
• Genre: The genre of the game (e.g., Action, Adventure, Sports).
• Publisher: The publisher of the game.
• NA_Sales: Sales in North America (in millions).
• EU_Sales: Sales in Europe (in millions).
• JP_Sales: Sales in Japan (in millions).
• Other_Sales: Sales in other regions (in millions).
• Global_Sales: Total global sales (in millions).

Data Preprocessing

The dataset is initially loaded into a Pandas DataFrame for cleaning and preprocessing. This step involves handling missing values to ensure data quality.

Exploratory Data Analysis (EDA)

A comprehensive EDA is conducted to visualize sales distributions and trends across different dimensions, including publishers, platforms, and geographical regions. Key analyses include:

• Nintendo Global Sales: Summation of global sales for games published by Nintendo, providing insight into the market impact of one of the industry's major players.
• Data Temporal Coverage: Identification of the range of years covered in the dataset, establishing the temporal context of the sales data.
• Publisher Sales Analysis: Aggregation of global sales by publisher to rank top-performing publishers and visualize their market share using bar plots.
• Sales Trends Over Decades: Examination of sales trends over decades, enabling the identification of shifts in market dominance among publishers and the evolution of consumer preferences.

Feature Engineering

The dataset is enriched with derived features to facilitate more in-depth analysis:

• Decade: The release year is transformed into a categorical feature representing the decade, aiding in the examination of sales trends over longer time periods.

Regional Sales Analysis

Sales data are analyzed across different geographic regions to identify the largest markets and understand regional preferences. This involves:

• Regional Sales Aggregation: Calculation of total sales per region, followed by comparative analysis to ascertain the largest markets for video games.
• Decade-wise Regional Trends: Exploration of how sales in different regions have evolved over the decades, highlighting shifts in market dynamics.

Best-selling Games and Platforms

Focused analysis on identifying best-selling games and the most successful platforms, including:

• Top-selling Games: A ranking of games based on global sales to pinpoint the highest-grossing titles.
• Platform Bestsellers: Determination of the best-selling game for each platform, offering insights into platform-specific consumer preferences.

Visualization

Data visualizations play a crucial role in this analysis, with the use of:

• Bar Plots: To represent sales figures and rankings in a clear, comparative format.
• Line Plots: To illustrate sales trends over time, particularly for decade-wise analysis.
• Scatter Plots: To explore correlations between different sales regions or attributes.

Tools and Libraries

The analysis is conducted using Python, with the following key libraries:

• pandas: For data manipulation and analysis.
• matplotlib.pyplot and seaborn: For data visualization.
• numpy: For numerical computations.
• glob and re: For file manipulation and regular expressions, respectively.

Usage

To replicate this analysis, ensure the vgsales.csv dataset is located within the data directory. Run the Jupyter Notebook to execute the code blocks sequentially, from data loading to visualization.