/Analysis_Steam_Videogames_Final_Project

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Upgrade Hub Bootcamp Data Analyst Final Project: Steam Game Analysis

This is my final project for the Data Analysis bootcamp I attended at Upgrade Hub. It focuses on exploring how different characteristics of Steam video games influence their success and reception by the community.

Upgrade Hub is an educational institution dedicated to offering high-quality training programs in technology and data science.

This analysis is based on Steam data, updated as of January 2024.

Project Content

  • CSV Files: Datasets with information extracted for our analysis.
  • Data Extraction: This is the script we used to obtain our data.
  • Initial Analysis: A document where we perform our initial analysis of the data.
  • Data Processing: Here we process all our data and transform it as needed.
  • Exploratory Data Analysis (EDA): A deeper analysis where we create more advanced graphs and observe stronger relationships. We also represent the Steam symbol through data mining.
  • Time Series Analysis: We use the ARIMA model for our time series analysis and make future predictions about video games.
  • Machine Learning: We employ a regression model (GradientBoosterRegressor) to make future price predictions for potential video games being released to the market, and use a classification model (RandomForestClassifier) to predict the future success of a video game set to be launched.
  • Power BI Dashboard: A dashboard on the top 4 companies that have launched the most video games on Steam.
  • Script.py: Script containing the code for our Streamlit webapp where everything is integrated. You can access it by clicking on the following link.

LIBRARIES

To run the code, first, ensure you have all the dependencies installed:

  • streamlit
  • matplotlib
  • numpy
  • pandas
  • seaborn
  • plotly
  • plotly_express
  • Pillow # Para PIL
  • streamlit_option_menu
  • requests
  • beautifulsoup4 # Para bs4
  • statsmodels
  • scikit-learn==1.2.2

ICONS

https://icons.getbootstrap.com/

https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/

NOTICES

https://vandal.elespanol.com/noticias/videojuegos

Contributions

Contributions are encouraged, with a reminder to adhere to best practices and thorough documentation.

Acknowledgments

I would like to express my gratitude to Upgradehub for providing me the opportunity to learn and develop my skills in Data Analytics. Special thanks to Andrés, Demetrio and Rocío, who are fantastic teachers, and to my classmates who supported me throughout this journey.

Contact Information

Should you have any questions or inquiries, feel free to reach out to me via LinkedIn.