Video Game Sales Data Analysis

Project Overview

The Video Game Sales Data Analysis project aims to explore and analyze a dataset containing sales information for video games across different platforms. Utilizing Databricks, the analysis leverages the power of Apache Spark for large-scale data processing and visualization. The primary goal is to provide insights into trends, performance across platforms, genres, and sales over time.

Objectives

• Perform Exploratory Data Analysis (EDA) on the video game sales dataset.
• Identify the top-selling games, platforms, and genres.
• Visualize sales trends over time.
• Understand regional sales distribution.

Dataset Description

The dataset consists of several attributes, including:

• Name: Title of the video game.
• Platform: Console/Platform on which the game is available.
• Year: Year of release.
• Genre: Genre of the game (Action, Sports, Adventure, etc.).
• Publisher: 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 worldwide sales (in millions).

Requirements

• Databricks account and workspace.
• Spark environment (included in Databricks).
• Libraries: PySpark, Pandas, Matplotlib/Seaborn (installed via Databricks notebook).

Implementation Steps

1. Set up Databricks Workspace

   • Create a new notebook in Databricks.
   • Configure the notebook for Python.

2. Load the Dataset

   • Upload the dataset to Databricks and load it into a Spark DataFrame.

3. Data Cleaning

   • Inspect the DataFrame for missing values and data inconsistencies.
   • Clean the dataset by handling missing values appropriately.

4. Exploratory Data Analysis

   • Calculate summary statistics.
   • Analyze trends over time for game sales.
   • Visualize the sales distribution by genre and platform.

5. Visualization

   • Create plots to visualize relationships and trends, such as:
     • Top games by sales.
     • Sales comparisons across platforms.
     • Yearly sales trends.

6. Insights and Conclusions

   • Summarize the key findings from the analysis.
   • Discuss implications for stakeholders in the video game industry.

How to Run the Analysis

1. Access Databricks

   • Log into your Databricks account and go to your workspace.

2. Create a New Notebook

   • Create a new notebook and select the Python language.

3. Upload the Dataset

   • Upload the video game sales dataset into Databricks.

4. Run the Analysis Cells

   • Run the cells in the notebook step by step to perform the analysis.

Results

The results will include various visualizations and a summary of insights regarding video game sales, highlighting patterns and significant findings from the dataset.