Video Game Sales What Can We See From The Numbers ?

Video game is always related to our childhood. We played game when we're small and even when we're already an adult. But is the industry doing well these day ? We can analyze the video game sale dataset with graphs visualization to get some insight about that.

The dataset is taken from https://www.kaggle.com/rishidamarla/video-game-sales

Libraries used in project :

• Pandas : a software library written for the Python programming language for data manipulation and analysis
• Numpy : a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
• Matplotlib : a plotting library for the Python programming language and its numerical mathematics extension NumPy.
• Seaborn : a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Thanks Jovian for the course project.

How to run the code

This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.

Option 1: Running using free online resources (1-click, recommended)

The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".

Option 2: Running on your computer locally

1. Install Conda by following these instructions. Add Conda binaries to your system PATH, so you can use the conda command on your terminal.

2. Create a Conda environment and install the required libraries by running these commands on the terminal:

conda create -n zerotopandas -y python=3.8 
conda activate zerotopandas
pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade

3. Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:

jovian clone notebook-owner/notebook-id

4. Enter the newly created directory using cd directory-name and start the Jupyter notebook.

jupyter notebook

You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a .ipynb extension) to open it.

Downloading the Dataset

Firstly We need to download the dataset to use. The link is already provided in the description above. You can also find a lot of interesting datasets on Kaggle

Let's begin by downloading the data, and listing the files within the dataset.