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https://github.com/yash22222/data-analysis-on-real-time-social-media-comments
EngageInsight analyzes user interactions in comment data. It provides insights through visualizations created using Python libraries like Pandas and Matplotlib. The project aims to uncover patterns and trends in user engagement. The visualizations provide an overview of comment lengths, the frequency of different types of replies.
https://github.com/yash22222/data-analysis-on-real-time-social-media-comments
data-analysis data-cleaning-and-preprocessing data-visualization matplotlib pandas pattern-recognition real-time-social-media-data seaborn trend-analysis
Last synced: 10 days ago
JSON representation
EngageInsight analyzes user interactions in comment data. It provides insights through visualizations created using Python libraries like Pandas and Matplotlib. The project aims to uncover patterns and trends in user engagement. The visualizations provide an overview of comment lengths, the frequency of different types of replies.
- Host: GitHub
- URL: https://github.com/yash22222/data-analysis-on-real-time-social-media-comments
- Owner: Yash22222
- Created: 2024-03-17T04:14:59.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-17T08:07:52.000Z (10 months ago)
- Last Synced: 2024-11-09T23:22:15.119Z (2 months ago)
- Topics: data-analysis, data-cleaning-and-preprocessing, data-visualization, matplotlib, pandas, pattern-recognition, real-time-social-media-data, seaborn, trend-analysis
- Language: Jupyter Notebook
- Homepage: https://yashashokshirsath.netlify.app/
- Size: 10.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data-Analysis-on-Real-Time-Social-Media-Comments
## Overview
EngageInsight analyzes user interactions in comment data. It provides insights through visualizations created using Python libraries like Pandas and Matplotlib. The project aims to uncover patterns and trends in user engagement. The visualizations provide an overview of comment lengths and the frequency of different types of replies.
## Key Features
- Data cleaning and preprocessing
- Creation of informative visualizations using Python libraries
- Analysis of user engagement and interaction patterns
## Installation
To run this project, you need to have Python installed. You can install the required libraries using the following command:
```bash
pip install -r requirements.txt
```
## Usage
1. Clone the repository:
```bash
git clone https://github.com/Yash22222/Data-Analysis-on-Real-Time-Social-Media-Comments.git
```
2. Navigate to the project directory:
```bash
cd Data-Analysis-on-Real-Time-Social-Media-Comments
```
3. Run the Jupyter Notebook or Python script to analyze the dataset and generate visualizations:
```bash
jupyter notebook DA_RT_SMD.ipynb
```
## Contributing
Contributions are welcome! Please open an issue or create a pull request if you have suggestions or improvements.
## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
```
This README provides clear instructions for installation, usage, contributing, and licensing, with properly formatted command code blocks. Feel free to use this as a template for your project!