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https://github.com/xre22zax/twitch-stream-analyze
Uncover Viewer Insights and Trends: Twitch Gaming Data Analysis
https://github.com/xre22zax/twitch-stream-analyze
python python3 sql sql-query sqlite sqlite3
Last synced: 11 days ago
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Uncover Viewer Insights and Trends: Twitch Gaming Data Analysis
- Host: GitHub
- URL: https://github.com/xre22zax/twitch-stream-analyze
- Owner: xre22zax
- Created: 2024-01-12T10:59:10.000Z (10 months ago)
- Default Branch: master
- Last Pushed: 2024-01-13T09:43:48.000Z (10 months ago)
- Last Synced: 2024-01-13T20:32:20.671Z (10 months ago)
- Topics: python, python3, sql, sql-query, sqlite, sqlite3
- Language: Jupyter Notebook
- Homepage:
- Size: 46 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: ReadMe.md
Awesome Lists containing this project
README
# Uncover Viewer Insights and Trends: Twitch Gaming Data Analysis
## Overview
Here's an enhanced version of your README, incorporating suggestions for improvement:
# 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.
---
## 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.
---
### 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()`
---
## Graphs :
* Bar chart
* plt.hist (histogram plot)
* Line Chart with error
---
## 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.
---
## 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:
```bash
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:
```sqlite
.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.
---
## 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.
---
## Contributing
Feel free to submit issues or pull requests for improvements or additions.
---
## Author
[Reza Sadeghi](https://github.com/xre22zax/)