Steam Games Analytics - Which Games Are the Most Profitable (BeCode Deployment Challenge)

Table of Contents

1. Description
2. Objectives
   1. Challenges
   2. Limitations
   3. Further developments
3. Repo Architecture
4. Installation
5. Usage
6. Visuals
7. Timeline
8. Personal situation

Description

This project is a part of the Becode.org AI Bootcamp programme. The goal is to to create a dashboard with insights about games on Steam platform based on scraped data provided in JSON format (see in the repo: database.txt). The dashboard should be deployed with Flask, Docker and Heroku. The project is ended with presenting the data insights to the client.

Objectives

• Be able to parse JSON files
• Be able to build and deploy an app using Flask, Docker, and Heroku
• Be able to save json data into an SQL database
• Be able to design a relational SQL database
• Be able to visualize data from a SQL database
• Be able to deploy said database alongside (interactive) visualizations

Strengths

• Normalised SQL database (See: the ER diagram below)
• Implemented the full pipeline: JSON to SQL, data insights, Flask + HTML, Docker, Heroku.

Limitations

• Not all data was transformed to a normalised database, which limits potential insights.
• Not all columns are added to the Flask app.
• Flask app is displaying stativ visuals with insights. They are not interactive.

Further Developments

• Creating more columns in the database and adding all the data in a normalized format.
• Accessing more columns wit the Flask app.
• Performing more insights.
• Creating an interactive dashboard with Dash.

Repo Architecture

1. /img - folder contains images: an ER diagram of the existing SQL database and suggested data pipelines (1 - implemented, 2 - work in progress; in a separate repo)
2. /static - folder contains images used in the app
3. /templates - folder contains .html file used for the app layout
4. README.md* - project documentation
5. Dockerfile - Docker file
6. ETL.ipynb - Python notebook contains code necessary to extract data from json and load it to SQL database
7. Insights.ipynb - Python notebook contains data insights along with graphs which can be saved to .png
8. Procfile - a procfile, which is required to deply with Heroku
9. app.py - app written in Flask, which connects to the .db and displays visuals and table columns
10. database.txt - a text file in which data from Steam is stored (in JSON format)
11. requirements.txt - a text file with a list of libraries required to run the app
12. runtime.txt - a text file which sets the correct version of Python in which app runs.
13. steam_games.db - a generated SQL database with normalized tables (a prototype - does not contain all the initial data). See visuals for the ER diagram of the database structure.
14. STEAM ANALYTICS.pdf - a presentation with the insights

Installation

git clone the repo

Usage

1. Access the app online: https://steam-analytics-kasia-docker.herokuapp.com/
2. Or: to run the app in your local environment, open a command line and run python app.py.
3. Or: to generate more insights use, Insights.ipynb jupyter notebook. In case you'd like to incorporate more visuals to the app, you need to add them to app.py.
4. Or: to add more data to the SQL database, use ETL.ipynb, where you can create more columns (see the exisiting ones on the ER diagram in the Visuals section of this README.md file Note: I used the following SQL client to display the created database and automatically create the ER diagram: DBeaver

Visuals

Timeline

The project took 10 working days.

Personal situation

I am currently participating in the Becode.org AI Bootcamp to upskill into a career in data science. My primary interest is in language as well as environmental issues.

Feel free to reach me on LinkedIn: kpranke

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