Project Title :Phonepe-Pulse-with-streamlit

Phonepe Pulse Data Visualization and Exploration: A User-Friendly Tool Using Streamlit and Plotly

Overview

The Phonepe pulse Github repository contains a large amount of data information related to Tarnsaction and Users in the states of India. ...

Approach

1. Data Extraction:

   • Employ scripting to clone the Phonepe pulse Github repository, extracting data related to transactions and user activities.

2. Data Transformation:

   • Using Python libraries such as Pandas, to manipulate and preprocess the extracted data. This step involves cleaning, handling missing values, and transforming the data into a format conducive to analysis.

3. Database Insertion:

   • Utilize the "mysql-connector-python" library in Python to establish a connection with a MySQL database. Execute SQL commands to seamlessly insert the transformed data, ensuring efficient storage and retrieval.

4. Dashboard Creation:

   • Harness the capabilities of Streamlit and Plotly in Python to craft an interactive and visually captivating dashboard. This platform will serve as the gateway for users to explore and understand the insights derived from the data.

5. Data Retrieval:

   • Employ the "mysql-connector-python" library to connect to the MySQL database. Retrieve the data into a Pandas dataframe, enabling dynamic updates to the dashboard and ensuring users always have access to the latest information.

6. Data Analysis

   • Develop a comprehensive dashboard that facilitates an effective and insightful analysis of the data.

Pyhton packages

pip install pandas

pip install streamlit

Pip install sqlalchemy

pip install PyMySQL

pip isstall git

pip install plotly

Dowload the source files from repo and use the bellow commandas to run

Script Execution

Data Extraction and Transformation

Run the following commands to extract and transform data related to users:

python .\Transaction_database.py
python .\users_datase.py

This script employs scripting to clone the Phonepe Pulse GitHub repository, then extracts users and transaction data and store in database.

Run the application using the following command

streamlit run ./home.py

This will launch the Streamlit application.