📹Project 15 : Q-Commerce Data Analysis with Python, on YouTube - https://youtu.be/Szvc57nPTkk

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This dataset is a synthetic yet realistic simulation of Quick Commerce (Q-Commerce) business data having 1 Million Records, inspired by popular platforms such as Blinkit, Zepto, Swiggy Instamart, Dunzo, JioMart, BigBasket, Amazon Now, and Flipkart Minutes.

It is designed for learners, analysts, and data science enthusiasts who want to practice real-world data analytics workflows using Python, Pandas, and data visualization tools.

🎯 Purpose of the Dataset

The primary goal of this dataset is to help users practice: Data Cleaning, Exploratory Data Analysis (EDA), Data Visualization, Feature Engineering, Business Problem Solving, Dashboard Building, Portfolio Project Creation.

This is not a “perfect” dataset. It intentionally contains: Missing values, Outliers, Realistic distributions to simulate real-world analytical challenges.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analysis will be helpful for those working in Jobs & Career domain.

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Using this dataset, we answered multiple business questions with Python in our Project.

Q.1) Which quick commerce platform has the highest total revenue?

Q.2) Which platform has the highest average order value (AOV)?

Q.3) How does Customer Rating vary across platforms?

Q.4) Does 'Delivery Time' affects the 'Delivery Partner Ratings'?

Q.5) What is the most popular Product Category on Swiggy Instamart, for the people of age between 30-40, in Mumbai?

Q.6) Which cities should these company expand into based on performance?

Q.7) Are discounts increasing order volume or just reducing revenue?

Q.8) Which company has the Best Operational Efficiency (Delivery Time vs Order Volume)?

At Last - Mini Dashboard with KPI

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These are the main Features/Columns available in the dataset :

1. Order_ID: Unique identifier for each order.

2. Company: Name of the quick commerce platform (e.g., Blinkit, Zepto, Swiggy Instamart, etc.).

3. City: City where the order was placed.

4. Customer_Age: Age of the customer who placed the order.

5. Order_Value: Total monetary value of the order.

6. Delivery_Time_Min: Time taken to deliver the order (in minutes).

7. Distance_KM: Distance between the store and the customer (in kilometers).

8. Items_Count: Number of items included in the order.

9. Product_Category: Product category of the ordered item.

10. Payment Method : Payment done by Card, Wallet, Cash etc

11. Customer_Rating: Rating given by the customer on a scale of 1 to 5.

12. Discount_Applied: Discount applied to the order or not.

13. Delivery_Partner_Rating: Rating given to the delivery partner on a scale of 1 to 5.