koushik-l / phonepe-pulse-data-visualization

Phonepe Pulse Data Visualization and Exploration

Phonepe-Pulse-Data-Visualization-and-Exploration

Problem Statement: The Phonepe pulse Github repository contains a large amount of data related to various metrics and statistics. The goal is to extract this data and process it to obtain insights and information that can be visualized in a user-friendly manner.

Project Work Flow

1. Data Extraction: The Github repository for Phonepe Pulse has been cloned using python to fetch the data, and it was stored into local in suitable JSON format.

2. Data Transformation: Python, along with libraries such as Pandas, has been employed to manipulate and pre-process the data. This has included cleaning the data, handling missing values, and transforming the data into a suitable format for analysis and visualization.

3. Database Creation and Data Insertion: The transformed data has been inserted into a MySQL database by connecting through the "mysql-connector-python" library in Python and by using SQL commands.

4. Streamlit Dashboard Creation: An interactive and visually appealing dashboard was created using Python's Streamlit and Plotly libraries. Plotly's built-in geo map functions have been used to display the data on a map, and Streamlit has been utilized to create a user-friendly interface with multiple dropdown options for users to select different facts and figures to display.

5. Data Retrival: The "mysql-connector-python" library has been used to connect to the MySQL database and fetch the data into a Pandas dataframe. The data in the dataframe has been used to update the dashboard dynamically.

6. Deployment: The application was made user-friendly for gaining insights and to know information about their Phone Pe app usage and money transactions.

Tools Used and Skills Gained

1. GitHub Cloning

• Used Git to clone repositories from GitHub.

2. Programming Language

• Python

3. Data Manipulation and Analysis

• Pandas Library

4. Database and Connection

• MySQL
• mysql-connector-python

5. Web Application

• Streamlit

6. Data Visualization

• Plotly Library