Exploratory Data Analysis of Game Sales

This project involves an exploratory data analysis (EDA) of game sales to answer several key questions about the industry trends and preferences.

Key Questions

1. Global Bestsellers: Which games have the highest global sales?
2. Yearly Sales: Which year recorded the highest global sales?
3. Genre Popularity: What are the most popular game genres globally and in each region (North America, Europe, Japan, Rest of the World)?
4. Changing Preferences: How have genre preferences evolved over the years?

Project Structure

The project is structured in a Jupyter Notebook format. Below is an outline of the notebook:

1. Introduction

   • An overview of the objectives and questions to be answered.

2. Data Loading and Initial Exploration

   • Importing necessary libraries (pandas, matplotlib.pyplot).
   • Loading the dataset and displaying the first few rows for an initial understanding.

   import pandas as pd
   import matplotlib.pyplot as plt
   
   df = pd.read_csv('data/XboxOne_GameSales.csv', encoding='latin1')
   df.head()

3. Global Bestsellers

   • Analysis to identify games with the highest global sales.

4. Yearly Sales

   • Analysis to determine which year recorded the highest global sales.

5. Genre Popularity

   • Analysis of the most popular game genres globally and in each region.

6. Changing Preferences

   • Analysis of how genre preferences have evolved over the years.

Getting Started

Prerequisites

• Python 3.x
• Jupyter Notebook
• Pandas library
• Matplotlib library

Installing

1. Clone the repository:

   git clone https://github.com/LeoIvin/Game-Sales-Exploratory-Data-Analysis.git

2. Navigate to the project directory:

   cd Game-Sales-Exploratory-Data-Analysis

3. Install the required libraries:

   pip install pandas matplotlib

4. Run the Jupyter Notebook:

   jupyter notebook

Usage

Open the xbox-analysis.ipynb notebook in Jupyter and run the cells to perform the analysis. Each section of the notebook includes comments and explanations to guide you through the analysis process.

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request with your changes.