Food Delivery Dataset Integration & Analysis

Dataset Overview and Problem Statement

The goal of this project is to manage three different data files, each in a different format, to simulate real-world data integration. ** Files Provided**

File 1: orders.csv (Transactional Data)

File 2: users.json (User Master Data)

File 3: restaurants.sql (Restaurant Master Data)

Step-by-Step: Combining the Datasets

1. Load CSV Data: Ingest the transactional order records.

2. Load JSON Data: Ingest the user profile information.

3. Load SQL Data: Ingest the restaurant database records.

4. Merge the Data: Perform joins using the following keys:

    orders.user_id → users.user_id
    orders.restaurant_id → restaurants.restaurant_id
    
    Join Type: Left Join (to retain all orders).

5. Create Final Dataset: The resulting dataset includes order details, user information, and restaurant information.

Output File: final_food_delivery_dataset.csv

Final Dataset – Key Analysis Areas The merged dataset serves as the single source of truth for the following analysis:

Order trends over time.

User behavior patterns.

City-wise and cuisine-wise performance.

Membership impact (Gold vs Regular).

Revenue distribution and seasonality.

Repository Information

The datasets used are saved in the Dataset folder. The solution code and analysis are present in the hackathon_code.ipynb file.