**Project Title:**🏠 Cozy Gaming Data Analysis

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Description/Background: The "Cozy Gaming Data Analysis" project is an exploration of the emerging field of cozy gaming. Cozy gaming emphasizes relaxation, comfort, and self-care in gaming experiences. As an enthusiast of cozy gaming, I embarked on this project to dive into the data-driven aspects of this genre, aiming to uncover insights into player behavior and engagement factors. This project showcases my passion for gaming and my skills in data analysis using Python libraries.

Project Objectives: The primary objectives of the "Cozy Gaming Data Analysis" project are as follows:

• To gain a comprehensive understanding of player behavior in the cozy gaming genre.
• To explore patterns in playtime and in-game purchases among cozy gamers.
• To identify the most important features that contribute to an engaging gaming experience in cozy games.

Research Question: The central research question driving this project is: "Player Behavior Analysis: How do players engage with cozy games in terms of playtime, in-game purchases, and the most important features while playing cozy video games?"

Methodologies: To achieve the project objectives and answer the research question, the following methodologies will be employed:

1. Data Collection: Develop and administer a bilingual survey (in Spanish and English) tailored to gather detailed information about cozy gamers' preferences and behaviors.
2. Data Analysis: Utilize Python libraries, including Pandas, Numpy, Matplotlib, and Seaborn, to conduct exploratory data analysis (EDA). This will involve data cleaning, statistical analysis, and data visualization to uncover insights.
3. Synthetic Data Augmentation: Augment the original survey data with synthetic data generated using Gretel AI to ensure a robust dataset for analysis.
4. Feature Importance Analysis: Explore and assess the importance of various gaming features (e.g., visuals, gameplay mechanics, customization options) using statistical techniques to understand their impact on player engagement.
5. Demographic Analysis: Analyze demographic factors, such as age and gender, to identify trends and patterns in cozy gaming behavior.
6. Data Visualization: Create meaningful and informative data visualizations to present findings and insights to a wider audience.

Achievements and Highlights:

• Designed and conducted a bilingual survey to collect data from cozy gamers.
• Generated synthetic data using Gretel AI to augment the dataset for analysis.
• Leveraged Python libraries to create data visualizations and explore patterns.