Uncover Viewer Insights and Trends: Twitch Gaming Data Analysis

Overview

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Uncover Viewer Insights and Trends: Twitch Gaming Data Analysis

Overview

Dive into the world's leading gaming live streaming platform! This project analyzes Twitch user behavior and preferences by exploring two datasets: stream viewing data and chat room usage data. Discover key insights into popular games, viewer locations, streaming sources, game genres, hourly viewership patterns, and more.

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Empowering the Analysis :

• SQL: Interact with databases for efficient data retrieval and manipulation.
• SQLite: Manage and query the datasets locally.
• pandas: Create and manipulate DataFrames for seamless data analysis.
• numpy: Perform numerical computations and array operations.
• matplotlib.pyplot: Generate compelling visualizations to communicate insights.

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Methods Employed :

1. Data manipulation:

range, FOR loop, GROUP BY, DISTINCT, ORDER BY, COUNT, WHERE(), WHEN(), strftime, JOIN

2. Data visualization:

fill_between, set_yticks, y_lower, y_upper, tight_layout, explode, shadow, startangle, autopct, pctdistance, gca()

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Graphs :

• Bar chart
• plt.hist (histogram plot)
• Line Chart with error

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Key Findings :

• Identify unique games and channels in streams.
• Uncover the most popular games captivating viewers.
• Pinpoint the geographical hotspots for LoL stream viewership.
• Discover preferred streaming sources among users.
• Categorize games into genres for deeper insights.
• Explore hourly viewership patterns to uncover peak engagement times.

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Getting Started: Join the Exploration

1. Clone the Repository:

   • Bring the project to your local machine.

2. Install Essential Libraries:

   • pip install pandas numpy matplotlib

3. Activate the SQLite Environment:

   • Using a terminal:
     • Open a terminal window and navigate to the project directory.
   • Using a Python IDE:
     • Open the project in your preferred Python IDE.

4. Connect to the SQLite Database:

   • Execute the following command in your terminal or IDE:

     sqlite3 twitch_gaming_data.db  # Replace with your actual database file name

5. Explore Data and Run Queries:

   • Use the SQLite command prompt to interact with the database:

     .tables  # View available tables
     .schema table_name  # View table structure
     SELECT * FROM table_name;  # Retrieve all data from a table
     -- Execute other SQL queries as needed
     
     
     

6. Ignite the Analysis:

   • Run the main Python script: Visualize Twitch Gaming Data.ipynb

Additional Tips:

• If using a Python IDE, consider installing a plugin for SQLite integration, often providing a user-friendly interface for running commands.
• Provide specific examples of SQL queries relevant to your analysis to guide users further.

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Usage

• Explore Visualizations: Gain valuable insights from the generated charts and plots.
• Experiment and Customize: Modify the code to tailor analyses and visualizations to your specific interests.

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Contributing

Feel free to submit issues or pull requests for improvements or additions.

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Author

Reza Sadeghi