Phonepe-Pulse-Data-Visualization-and-Exploration

To check website in My Project Link: phonepe-plus-project.onrender.com

Problem Statement

Retrieve data from the Phonepe Pulse GitHub repository, perform data transformation and cleansing,insert it into a MySQL database, and develop a live geo-visualization dashboard using Streamlit and Plotly in Python.The dashboard will present the data interactively and aesthetically, featuring a minimum of 10 diverse dropdown optionsfor users to select various facts and figures. The solution aims to be secure, efficient, and user-friendly, offering valuable insights and information about the data within the Phonepe Pulse GitHub repository.

Technology Stack Used:

1. Python
2. MySQL
3. Streamlit
4. colab
5. Github Cloning
6. Geo Visualisation

Installation:

pip install pandas
pip install numpy
pip install os
pip install mysql.connector
pip install git_clone
pip install stramlit

Import Libraries:

import pandas as pd
import numpy as np
import os
import json
import mysql.connector 
import streamlit as st
import plotly.express as px

Approach:

1. Data Extraction: The data is obtained from the PhonePe Pulse GitHub repository using scripting techniques and cloned for further processing (link).

2. Data Transformation: Process the cloned data using Python algorithms to transform it into DataFrame format, ensuring it is clean and ready for analysis.

3. Database Integration: The transformed data is inserted into a MySQL database, providing efficient storage and retrieval capabilities.

4. Live Geo Visualization Dashboard: Utilizing Python's Streamlit and Plotly libraries, create an interactive and visually appealing dashboard. This real-time dashboard enables users to explore insights effectively.

5. Database Integration with the Dashboard: Fetch relevant data from the MySQL database and seamlessly integrate it into the dashboard, ensuring that the displayed information is up-to-date and accurate.

6. Visualization: Finally, create a dashboard using Streamlit, incorporating selection and dropdown options. Showcase the output through Geo visualization, bar charts, and a DataFrame table.

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