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🚀 SQL Project Series

Food & Grocery Delivery Analytics 🛒

Analyze customers, stores, products, orders, deliveries, and payments to understand sales performance, customer behavior, delivery efficiency, and operational costs.

🎯 Business Objectives

✅ Analyze order and revenue trends

✅ Identify top-selling products

✅ Measure customer retention

✅ Analyze store performance

✅ Track delivery efficiency

✅ Identify peak ordering periods

✅ Monitor cancellations and refunds

✅ Optimize product and store performance

📂 Database Setup

CREATE DATABASE grocery_delivery_db;
USE grocery_delivery_db;

Tables Created:

customers → customer_id, customer_name, city, signup_date

stores → store_id, store_name, city, store_type

products → product_id, product_name, category, price

orders → order_id, customer_id, store_id, order_date, order_status, delivery_time_minutes, delivery_fee

order_items → order_item_id, order_id, product_id, quantity, unit_price

Sample data for 5 customers, 4 stores, 5 products, 5 orders already included.

🧠 SQL Concepts You'll Practice

✔ INNER JOIN, LEFT JOIN

✔ Aggregate Functions, GROUP BY, HAVING

✔ CASE WHEN, CTEs, Subqueries

✔ Window Functions

✔ Date & Time Functions

✔ Conditional Aggregation

📊 Business KPIs You Can Build

📈 Total Orders, Completed Orders, Cancelled Orders, Cancellation Rate

📈 Total Revenue, AOV, Average Basket Size, Items Sold

📈 Revenue by Category, Store, City

📈 Top-Selling / Low-Selling Products

📈 Customer Lifetime Value, Repeat Purchase Rate, Retention Rate

📈 Average Delivery Time, On-Time Delivery Rate

📈 Peak Ordering Hour, Peak Ordering Day, Monthly Revenue Growth

📈 Delivery Fee Revenue, Customer Acquisition Trend

📈 Executive Grocery Delivery Dashboard

💡 Example Queries

1. Total Revenue

SELECT SUM(oi.quantity * oi.unit_price) AS total_revenue
FROM orders o
JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_status = 'Delivered';

2. Top-Selling Products

SELECT p.product_name, SUM(oi.quantity) AS units_sold
FROM products p
JOIN order_items oi ON p.product_id = oi.product_id
JOIN orders o ON oi.order_id = o.order_id
WHERE o.order_status = 'Delivered'
GROUP BY p.product_name
ORDER BY units_sold DESC
LIMIT 10;

3. Average Order Value

WITH order_values AS (
    SELECT o.order_id, SUM(oi.quantity * oi.unit_price) AS order_value
    FROM orders o
    JOIN order_items oi ON o.order_id = oi.order_id
    WHERE o.order_status = 'Delivered'
    GROUP BY o.order_id
)
SELECT ROUND(AVG(order_value), 2) AS average_order_value FROM order_values;

4. Repeat Customers

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
WHERE order_status = 'Delivered'
GROUP BY customer_id
HAVING COUNT(order_id) > 1;

5. Revenue by Store

SELECT s.store_name, SUM(oi.quantity * oi.unit_price) AS revenue
FROM stores s
JOIN orders o ON s.store_id = o.store_id
JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_status = 'Delivered'
GROUP BY s.store_name
ORDER BY revenue DESC;

6. Cancellation Rate

SELECT ROUND(100.0 * SUM(CASE WHEN order_status = 'Cancelled' THEN 1 ELSE 0 END) / COUNT(*), 2) AS cancellation_rate
FROM orders;

7. Peak Ordering Hours

SELECT EXTRACT(HOUR FROM order_date) AS order_hour, COUNT(*) AS total_orders
FROM orders
WHERE order_status = 'Delivered'
GROUP BY EXTRACT(HOUR FROM order_date)
ORDER BY total_orders DESC;

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💻 HOW TO DEBUG YOUR CODE 🐛🔍

1️⃣ READ THE ERROR MESSAGE

Don't ignore the error.

An error message usually tells you:

👉 What went wrong

👉 Where it happened

👉 Sometimes why it happened

Example:

"NameError: name 'total' is not defined"

This tells you that Python cannot find a variable called "total".

2️⃣ CHECK THE LINE NUMBER

Most programming errors tell you where the problem occurred.

Go directly to that line.

Then check:

• Variable names

• Syntax

• Data types

• Function calls

• Missing brackets

• Incorrect indentation

3️⃣ UNDERSTAND THE ERROR TYPE

Common errors beginners encounter:

"SyntaxError" → Code doesn't follow the language syntax.

"NameError" → You used a name that hasn't been defined.

"TypeError" → An operation was performed on an incompatible data type.

"IndexError" → You tried to access an invalid index.

"KeyError" → A dictionary key doesn't exist.

"ValueError" → A value has the wrong format or isn't acceptable.

👉 Learn what common errors mean instead of simply searching for fixes.

4️⃣ CHECK YOUR ASSUMPTIONS

Sometimes your code runs without an error but produces the wrong result.

Example:

"age = "25""

You might think "age" contains a number.

But it actually contains a string.

Always ask:

👉 What type is this variable?

👉 What value does it currently contain?

5️⃣ PRINT INTERMEDIATE VALUES

When you're unsure what is happening, inspect your variables.

Example:

"print(total)"

"print(count)"

"print(average)"

This helps you understand how the values change while your program runs.

6️⃣ BREAK THE PROBLEM INTO SMALL PARTS

Don't debug 200 lines of code at once.

Separate the problem.

Instead of asking:

❌ "Why doesn't my program work?"

Ask:

✅ "Is my input correct?"

Then:

✅ "Is my calculation correct?"

Then:

✅ "Is my loop working?"

Then:

✅ "Is my output correct?"

Small questions are easier to solve.

7️⃣ CHECK YOUR LOOP

Loops are a common source of bugs.

Check:

👉 Where does the loop start?

👉 When does it stop?

👉 Is the condition correct?

👉 Is the variable being updated?

👉 Could this become an infinite loop?

Example:

"while count < 10:"

" print(count)"

" count += 1"

If "count" never changes, the loop may never end.

8️⃣ CHECK YOUR DATA TYPES

Many bugs happen because developers expect one type but receive another.

Example:

""10" + "20""

Result:

""1020""

But:

10 + 20

Result:

30

The values look similar, but their data types are different.

9️⃣ TEST WITH SIMPLE INPUT

If your program fails with complicated data, simplify it.

Instead of:

[15, 82, 43, 91, 27, 64, 10]

Try:

[1, 2, 3]

Then:

[1]

Then:

[]

Simple inputs make problems easier to identify.

🔟 TEST EDGE CASES

Always test unusual situations.

Examples:

• Empty input

• One element

• Duplicate values

• Negative numbers

• Very large numbers

• Missing values

• Invalid input

A solution isn't truly reliable until you understand how it behaves in these situations.

1️⃣1️⃣ USE A DEBUGGER

As your programs become larger, use debugging tools.

A debugger allows you to:

🔹 Pause execution

🔹 Inspect variables

🔹 Execute code step by step

🔹 Set breakpoints

🔹 Find where the logic goes wrong

This is much more powerful than adding "print()" everywhere.


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⚡ Variables & Data Types in Java ⭐

After understanding Java basics, the next important concept is Variables and Data Types. Every Java program stores and manipulates data, and this is done using variables. Let’s understand everything step by step.

✅ 1️⃣ What is a Variable?

A variable is a container that stores data. Think of it like a box that holds values.

Example: int age = 25;

Here:
- int → data type
- age → variable name
- 25 → value stored in variable

Simple Structure:
data_type variable_name = value;

Example:

int number = 10;
double salary = 50000.50;
char grade = 'A';

✅ 2️⃣ Rules for Naming Variables

Java has some rules for variable names.

✔ Must start with letter, _ or $
✔ Cannot start with a number
✔ Cannot use Java keywords

Valid examples:
- int age;
- double salary;
- String studentName;

Invalid examples:
- int 1age;
- double student-name;

✅ 3️⃣ Data Types in Java

Java has two main types of data types.

1️⃣ Primitive Data Types
2️⃣ Non-Primitive Data Types

🔹 4️⃣ Primitive Data Types

Primitive types store simple values directly in memory. Java has 8 primitive data types.

- byte: 1 byte (e.g., byte a = 10;)
- short: 2 bytes (e.g., short b = 100;)
- int: 4 bytes (e.g., int age = 25;)
- long: 8 bytes (e.g., long population = 8000000000L;)
- float: 4 bytes (e.g., float price = 12.5f;)
- double: 8 bytes (e.g., double salary = 50000.75;)
- char: 2 bytes (e.g., char grade = 'A';)
- boolean: 1 bit (e.g., boolean isTrue = true;)

🔹 5️⃣ Non-Primitive Data Types

Non-primitive types store references to objects.
Examples: String, Arrays, Classes, Objects, Interfaces

Example:

String name = "Java";
int[] numbers = {1, 2, 3, 4};

Difference:
- Primitive: Stores value, fixed size, faster.
- Non-Primitive: Stores reference, dynamic size, slightly slower.

🔹 6️⃣ Type Casting

Type casting means converting one data type to another. There are two types.

⭐ 1. Implicit Casting (Automatic): Smaller type → Larger type.
Example:

int number = 10;
double value = number; 

⭐ 2. Explicit Casting (Manual): Larger type → Smaller type.
Example:

double price = 99.99;
int value = (int) price; // Output: 99

🔹 7️⃣ Constants in Java (final keyword)

A constant is a variable whose value cannot change. Java uses the final keyword.

Example:

final double PI = 3.14159;

Constants are usually written in UPPERCASE.

🔥 Example Program (Variables in Java)

class VariablesDemo {
    public static void main(String[] args) {
        int age = 25;
        double salary = 50000.75;
        char grade = 'A';
        boolean isWorking = true;

        System.out.println("Age: " + age);
        System.out.println("Salary: " + salary);
        System.out.println("Grade: " + grade);
        System.out.println("Working: " + isWorking);
    }
}

Output:

Age: 25
Salary: 50000.75
Grade: A
Working: true

⭐ Common Interview Questions

1️⃣ What are the 8 primitive data types in Java?
2️⃣ What is the difference between primitive and non-primitive data types?
3️⃣ What is type casting in Java?
4️⃣ What is the difference between implicit and explicit casting?


🔥 Quick Revision

- Variables → containers for storing data.
- Primitive types: byte, short, int, long, float, double, char, boolean.
- Non-primitive types: String, Arrays, Objects, Classes.
- Type casting: Implicit → automatic; Explicit → manual.
- Constant: Created using the final keyword.


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𝟮. 𝗟𝗘𝗘𝗧𝗖𝗢𝗗𝗘 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/leetcode_cp

𝟯. 𝗖𝗢𝗗𝗘𝗙𝗢𝗥𝗖𝗘𝗦 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/codeforces_cp

𝟰. 𝗖𝗢𝗗𝗘𝗖𝗛𝗘𝗙 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗚𝗿𝗼𝘂𝗽:

https://t.me/codechef_group

𝟱. 𝗖𝗢𝗗𝗜𝗡𝗚 𝗡𝗜𝗡𝗝𝗔𝗦 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗿𝗼𝘂𝗽:

https://t.me/coding_ninjas_discuss

𝟲. 𝗔𝗧𝗖𝗢𝗗𝗘𝗥 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗥𝗢𝗨𝗣:

https://t.me/atcoder_discuss

𝟳. 𝗡𝗘𝗪𝗧𝗢𝗡 𝗦𝗖𝗛𝗢𝗢𝗟 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗥𝗢𝗨𝗣:

https://t.me/Newton_School_Discuss

𝟴. 𝗞𝗔𝗚𝗚𝗟𝗘 𝗗𝗜𝗦𝗖𝗨𝗦𝗦𝗜𝗢𝗡 𝗚𝗥𝗢𝗨𝗣:

https://t.me/kaggle_official

9.Smart India Hackathon:

https://t.me/sih_official

10. ICPC Official:

https://t.me/icpc_Official

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