Social Media Data Analysis project using Python in Jupyter Notebook to process JSON profile data and identify users with maximum posts, followers, following, and category trends. The insights are later visualized through a Flask dashboard for easy and interactive data exploration.
This project analyzes social media profile data stored in JSON format using *Python in Jupyter Notebook. The main goal is to identify users with the highest number of posts, followers, following, and categories, and then present these insights through a *Flask-based dashboard for easy visualization.
The notebook contains analytical questions such as:
- Who has the maximum followers?
- Who has the maximum following?
- Who has the maximum posts?
- Which categories appear in the dataset most often?
After performing the analysis in Jupyter Notebook, the results can be integrated into a Flask web app to create a clean and interactive dashboard.
The dataset is based on social media profile information stored in JSON format. From the sample provided, the data includes fields such as:
- Username
- Number of posts
- Followers count
- Following count
- Name
- Category / profession
- Bio / profile description
- External links or profile references
This structured data makes it possible to perform user-level analysis and extract meaningful insights from social media accounts.
Using Python, the project focuses on extracting important insights from the dataset, including:
- User with the maximum followers
- User with the maximum following
- User with the maximum posts
- Most common profile categories
- General comparison of social media account activity
These findings help understand which accounts are the most active, most followed, and how profiles are distributed across different categories.
The next step of the project is to build a Flask dashboard that visualizes the analysis results in a simple and user-friendly format. The dashboard can include:
- Summary cards for max followers, max following, and max posts
- Bar charts for top users
- Category distribution charts
- Search or filter options for profile analysis
This will make the project more interactive and suitable for demonstration, presentation, and GitHub showcasing.
- Python
- Jupyter Notebook
- JSON
- Pandas
- Matplotlib / Seaborn
- Flask
- HTML, CSS, Bootstrap
- Load JSON data in Jupyter Notebook.
- Clean and organize the dataset.
- Analyze key metrics such as followers, following, posts, and categories.
- Generate insights from the notebook.
- Build a Flask dashboard to visualize the results.
- Add interactive charts to the Flask app
- Deploy the project online
- Add profile filtering and sorting
- Improve dashboard UI for better presentation