Qudyan Delivery Order Analytics

This project analyzes real-world order data from Qudyan Delivery using SQL and Python.

Objective

The goal of this project is to explore order trends, revenue patterns, and delivery performance.

Dataset Overview

• Orders dataset: 1,152 rows
• Customers dataset: 1,968 rows (anonymized for portfolio use)

Dataset

The project uses two datasets derived from Qudyan Delivery operations.

Orders Dataset

Contains information about delivery orders including:

• order_id
• order_amount
• payment_status
• order_status
• payment_method
• created_at
• delivery_date
• delivery_time

Customers Dataset

Contains anonymized customer information used for analysis:

• customer_id
• first_name
• last_name

Example Insights

From the analysis we can identify:

• Total number of orders
• Average order value
• Distribution of order status (delivered vs cancelled)
• Payment behavior of customers
• Daily order trends

These insights help understand operational performance and customer behavior in the delivery platform.

Key Questions

• How many orders were placed?
• What is the average order value?
• What percentage of orders were delivered or canceled?
• How does payment status vary across orders?
• What trends can be observed over time?

Tools Used

• SQL
• Python
• Pandas
• Matplotlib

Files

• qudyan_orders_portfolio.csv
• qudyan_customers_portfolio.csv
• qudyan_sql_queries.sql
• qudyan_analysis.py
• insights.md

Orders over time