Global Video Game Sales Analysis

This project involves performing data analysis on global video game sales data imported from Kaggle. Using various Python libraries (Numpy, Pandas, Matplotlib, and Seaborn), I've analyzed and visualized changing trends over the years, focusing on the most played video game genres and their sales.

Project Overview

The primary objective of this project is to explore and understand the trends in video game sales globally. The analysis includes:

1. Data Collection: The dataset was sourced from Kaggle, contributed by The Devastator. You can find the dataset here.

2. Data Cleaning: Processed the raw data to handle missing values, duplicates, and other inconsistencies.

3. Data Analysis and Visualization: Performed exploratory data analysis (EDA) and created visualizations to uncover patterns and insights.

Steps Taken

[+] Data Collection:

Source: Kaggle - Global Video Game Sales

[+] Data Cleaning:

(i) Handled missing values

(ii) Removed duplicates

(iii) Standardized data formats

[+] Data Analysis/Visualization:

1) Explored trends in video game sales over the years

2) Analyzed the popularity of different video game genres

3) Visualized sales data using Matplotlib and Seaborn

How to Use

All the steps taken and detailed explanations are within the comments of the ipynb file. To view and run the notebook:

Clone this repository

Dependencies

Ensure you have the following libraries installed:

Numpy

Pandas

Matplotlib

Seaborn

Acknowledgements

Kaggle for providing the dataset.

The Devastator for contributing the dataset to Kaggle.