Phonepe Pulse Data Visualization and Exploration: A User-Friendly Tool Using Streamlit and Plotly
- GitHub Cloning: For extracting data from the Phonepe Pulse GitHub repository.
- Python: The primary scripting language for data manipulation and dashboard creation.
- Pandas: To handle data manipulation and preprocessing.
- PostgreSQL: The relational database management system used to store data.
- Streamlit: To create a user-friendly and interactive dashboard.
- Plotly: For building visually appealing data visualizations.
The Phonepe Pulse GitHub repository is a valuable resource containing a vast dataset of financial metrics and statistics. The objective of this project is to extract, transform, and visualize this data in a user-friendly manner, providing valuable insights into the fintech domain. The solution will encompass the following steps:
1. Data Extraction: Utilize scripting to clone the GitHub repository, enabling the retrieval of data from Phonepe Pulse.
2. Data Transformation: Python, along with Pandas and other libraries, to manipulate and preprocess the data. This may involve data cleaning, handling missing values.
3. Database Insertion: Establish a connection to a MySQL database using the "mysql-connector-python" library in Python. Insert the transformed data into the database using SQL commands.
4. Dashboard Creation: Develop an interactive and visually appealing dashboard using Streamlit and Plotly in Python.Utilize Plotly's built-in geo map functions to display data on a map, while Streamlit will facilitate the creation of a user-friendly interface.Incorporate multiple dropdown options for users to select various facts and figures for display.
5. Data Retrieval: Employ the "mysql-connector-python" library to connect to the MySQL database, fetching data into a Pandas dataframe.
6. Deployment: Prioritize security, efficiency, and user-friendliness during testing and deployment. Deploy the dashboard publicly, making it easily accessible to users.