OLA Dashboard

Overview This project is a Streamlit-based dashboard for visualizing OLA ride data. It connects to a SQL Server database and executes various queries to display insights on ride data, vehicle types, cancellations, revenue, customer behavior, and ratings.

Features

• Interactive sidebar with category selection
• Displays various metrics and insights for each category
• Uses icons for vehicle types
• Customizable styling using Streamlit's markdown feature

Requirements

• Python 3.x
• Streamlit
• pandas
• pyodbc
• SQL Server database with OLA ride data

Installation

1. Clone the repository: git clone https://github.com/subassri/OLAride.git
2. Install required packages: pip install streamlit pandas pyodbc
3. Update the database connection string in the DatabaseConnector class in olaapps.py

Usage

1. Run the application: streamlit run olaapps.py
2. Open a web browser and navigate to http://localhost:8501
3. Select a category from the sidebar to view insights

Database Schema The project assumes a SQL Server database with a table named ola_data containing columns such as:

• Booking_Status
• Vehicle_Type
• Ride_Distance
• Customer_Rating
• Payment_Method
• Booking_Value