Get insights on video game sales industry through data visualization and analysis using Pandas, Numpy, Matplotlib and Seaborn libraries. Learn how to download dataset and run code locally or online.
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 :
Thanks Jovian for the course project.
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.
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".
Install Conda by following these instructions. Add Conda binaries to your system
PATH, so you can use the
conda command on your terminal.
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
jovian clone notebook-owner/notebook-id
cd directory-nameand start the 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.
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
!pip install jovian opendatasets --upgrade --quiet
Let's begin by downloading the data, and listing the files within the dataset.