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https://github.com/gino-freud-hobayan/instacart-analysis__sql-capstone-project
Capstone Project: Analyzing the Instacart dataset (Microsoft SQL) with a 10-minute live video presentation on YouTube
https://github.com/gino-freud-hobayan/instacart-analysis__sql-capstone-project
adhoc-analysis aggregate-functions datadefinitionlanguage datamanipulationlanguage dataquerylanguage joins sql
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Capstone Project: Analyzing the Instacart dataset (Microsoft SQL) with a 10-minute live video presentation on YouTube
- Host: GitHub
- URL: https://github.com/gino-freud-hobayan/instacart-analysis__sql-capstone-project
- Owner: Gino-Freud-Hobayan
- Created: 2023-08-24T05:44:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-08T08:10:39.000Z (9 months ago)
- Last Synced: 2024-02-08T09:25:35.268Z (9 months ago)
- Topics: adhoc-analysis, aggregate-functions, datadefinitionlanguage, datamanipulationlanguage, dataquerylanguage, joins, sql
- Homepage: https://www.youtube.com/@ginohobayan001
- Size: 169 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Instacart Dataset Analysis
### (Scroll down for the Instacart Capstone Project ⬇️⬇️)
This also serves as the Repository of all the major projects I made
during the 2 week SQL Bootcamp by [Data Vanguard](https://datavanguard.ph/)
1.) Aggregate functions and Ad Hoc Analysis on covid19_italy dataset
2.) Assignment # 2 - More Ad Hoc Analysis on covid19_italy dataset
3.) Capstone Project: Analyzing the Instacart dataset using Microsoft SQL
## 📊 SQL Capstone Project: [10-minute Presentation on YouTube](https://youtu.be/5TJ5p7ZGM2U)
## 📊 Tableau link for the Dashboard: [Instacart dataset analysis Dashboard](https://public.tableau.com/app/profile/gino.freud.hobayan/viz/InstacartdatasetanalysisbyGINOFREUDD_HOBAYAN/Dashboard1)
### 👉 [Click here for the Slideshow on Google Slides](https://docs.google.com/presentation/d/1noOMPc8odMdqlKVYHrEb0HAb7qxFdL1T1e0-G1OFej4/edit?usp=sharing)
### Analyzing the Instacart Dataset using Microsoft SQL
### Source : https://www.kaggle.com/datasets/yasserh/instacart-online-grocery-basket-analysis-dataset?select=order_products__train.csv
## Awards and Certificate of Completion:
## (ERD) Entity Relationship Diagram
## Data Dictionary
# DATA DEFINITION LANGUAGE (DDL)
```sql
-----------------------------------------------------------
-----------------------------------------------------------
-- SQL BOOTCAMP CAPSTONE PROJECT (Instacart Dataset) using Microsoft SQL
-- BY: GINO FREUD D. HOBAYAN
-----------------------------------------------------------
-----------------------------------------------------------
-----------------------------------------------------------
-----------------------------------------------------------
-- Data Definition Language (DDL)
-----------------------------------------------------------
-----------------------------------------------------------
CREATE DATABASE Instacart_DATABASE;
-----------------------------------------------------------
-- products TABLE (shape: 49.688, 4)
-----------------------------------------------------------
CREATE TABLE products
(
product_id VARCHAR(50) PRIMARY KEY NOT NULL,
product_name VARCHAR(250),
aisle_id INTEGER,
department_id INTEGER
);
--- Insert all the data from the CSV file into our table (COMMA DELIMITER)
BULK INSERT products
FROM "C:\Users\GINO\Desktop\SQL Capstone Project\InstaCart Online Grocery Basket Analysis Dataset\products.csv"
WITH (
FIELDTERMINATOR = ',', -- Use COMMA as the delimiter
ROWTERMINATOR = '\n',
FIRSTROW = 2 -- Skip the header row if present
);
-- Check the TABLE
SELECT TOP 600
*
FROM
products
ORDER BY
CAST(product_id AS INT);
-----------------------------------------------------------
-- orders TABLE (shape: 3.421.083, 7)
-----------------------------------------------------------
CREATE TABLE orders
(
order_id INTEGER PRIMARY KEY NOT NULL,
user_id INTEGER,
eval_set VARCHAR(100),
order_number INTEGER,
order_dow INTEGER,
order_hour_of_day INTEGER,
days_since_prior_order FLOAT
);
BULK INSERT orders
FROM "C:\Users\GINO\Desktop\SQL Capstone Project\InstaCart Online Grocery Basket Analysis Dataset\orders.csv"
WITH (
FIELDTERMINATOR = ',', -- Use COMMA as the delimiter
ROWTERMINATOR = '0x0a', -- Hexadecimal representation of line feed
FIRSTROW = 2 -- Skip the header row if present
);
-- Check the TABLE
SELECT TOP 1000
*
FROM
orders
ORDER BY
user_id
-- days_since_prior_order COLUMN HAS NULL VALUES.
-----------------------------------------------------------
-- order_product_prior TABLE (shape: 32.434.489, 4)
-----------------------------------------------------------
CREATE TABLE order_products__prior
(
order_id INT,
product_id VARCHAR(50),
add_to_cart_order INT,
reordered INT,
);
BULK INSERT order_products__prior
FROM "C:\Users\GINO\Desktop\SQL Capstone Project\InstaCart Online Grocery Basket Analysis Dataset\order_products__prior.csv"
WITH (
FIELDTERMINATOR = ',',
ROWTERMINATOR = '0x0a', -- Hexadecimal representation of line feed
FIRSTROW = 2
);
----------------------------------------------------------------------------------
-- SUCCESSFULLY CREATED A TABLE WITH 32,400,000 ROWS
-- QUERIES TAKE QUITE A WHILE
-- Check the TABLE
SELECT TOP 1000
*
FROM
order_products__prior
ORDER BY
order_id,
add_to_cart_order
-----------------------------------------------------------
-- order_product_train TABLE (shape: 1.384.617, 4)
-----------------------------------------------------------
CREATE TABLE order_products__train
(
order_id INT,
product_id VARCHAR(50),
add_to_cart_order INT,
reordered INT,
);
BULK INSERT order_products__train
FROM "C:\Users\GINO\Desktop\SQL Capstone Project\InstaCart Online Grocery Basket Analysis Dataset\order_products__train.csv"
WITH (
FIELDTERMINATOR = ',',
ROWTERMINATOR = '0x0a',
FIRSTROW = 2
);
-- Check the TABLE
SELECT TOP 1000
*
FROM
order_products__train
ORDER BY
order_id,
add_to_cart_order
-----------------------------------------------------------
-- aisles TABLE (shape: 134,2)
-----------------------------------------------------------
CREATE TABLE aisles
(
aisle_id INTEGER PRIMARY KEY,
aisle_name VARCHAR(255)
);
INSERT INTO aisles
(aisle_id, aisle_name)
VALUES
(1, 'prepared soups salads'),
(2, 'specialty cheeses'),
(3, 'energy granola bars'),
(4, 'instant foods'),
(5, 'marinades meat preparation'),
(6, 'other'),
(7, 'packaged meat'),
(8, 'bakery desserts'),
(9, 'pasta sauce'),
(10, 'kitchen supplies'),
(11, 'cold flu allergy'),
(12, 'fresh pasta'),
(13, 'prepared meals'),
(14, 'tofu meat alternatives'),
(15, 'packaged seafood'),
(16, 'fresh herbs'),
(17, 'baking ingredients'),
(18, 'bulk dried fruits vegetables'),
(19, 'oils vinegars'),
(20, 'oral hygiene'),
(21, 'packaged cheese'),
(22, 'hair care'),
(23, 'popcorn jerky'),
(24, 'fresh fruits'),
(25, 'soap'),
(26, 'coffee'),
(27, 'beers coolers'),
(28, 'red wines'),
(29, 'honeys syrups nectars'),
(30, 'latino foods'),
(31, 'refrigerated'),
(32, 'packaged produce'),
(33, 'kosher foods'),
(34, 'frozen meat seafood'),
(35, 'poultry counter'),
(36, 'butter'),
(37, 'ice cream ice'),
(38, 'frozen meals'),
(39, 'seafood counter'),
(40, 'dog food care'),
(41, 'cat food care'),
(42, 'frozen vegan vegetarian'),
(43, 'buns rolls'),
(44, 'eye ear care'),
(45, 'candy chocolate'),
(46, 'mint gum'),
(47, 'vitamins supplements'),
(48, 'breakfast bars pastries'),
(49, 'packaged poultry'),
(50, 'fruit vegetable snacks'),
(51, 'preserved dips spreads'),
(52, 'frozen breakfast'),
(53, 'cream'),
(54, 'paper goods'),
(55, 'shave needs'),
(56, 'diapers wipes'),
(57, 'granola'),
(58, 'frozen breads doughs'),
(59, 'canned meals beans'),
(60, 'trash bags liners'),
(61, 'cookies cakes'),
(62, 'white wines'),
(63, 'grains rice dried goods'),
(64, 'energy sports drinks'),
(65, 'protein meal replacements'),
(66, 'asian foods'),
(67, 'fresh dips tapenades'),
(68, 'bulk grains rice dried goods'),
(69, 'soup broth bouillon'),
(70, 'digestion'),
(71, 'refrigerated pudding desserts'),
(72, 'condiments'),
(73, 'facial care'),
(74, 'dish detergents'),
(75, 'laundry'),
(76, 'indian foods'),
(77, 'soft drinks'),
(78, 'crackers'),
(79, 'frozen pizza'),
(80, 'deodorants'),
(81, 'canned jarred vegetables'),
(82, 'baby accessories'),
(83, 'fresh vegetables'),
(84, 'milk'),
(85, 'food storage'),
(86, 'eggs'),
(87, 'more household'),
(88, 'spreads'),
(89, 'salad dressing toppings'),
(90, 'cocoa drink mixes'),
(91, 'soy lactosefree'),
(92, 'baby food formula'),
(93, 'breakfast bakery'),
(94, 'tea'),
(95, 'canned meat seafood'),
(96, 'lunch meat'),
(97, 'baking supplies decor'),
(98, 'juice nectars'),
(99, 'canned fruit applesauce'),
(100, 'missing'),
(101, 'air fresheners candles'),
(102, 'baby bath body care'),
(103, 'ice cream toppings'),
(104, 'spices seasonings'),
(105, 'doughs gelatins bake mixes'),
(106, 'hot dogs bacon sausage'),
(107, 'chips pretzels'),
(108, 'other creams cheeses'),
(109, 'skin care'),
(110, 'pickled goods olives'),
(111, 'plates bowls cups flatware'),
(112, 'bread'),
(113, 'frozen juice'),
(114, 'cleaning products'),
(115, 'water seltzer sparkling water'),
(116, 'frozen produce'),
(117, 'nuts seeds dried fruit'),
(118, 'first aid'),
(119, 'frozen dessert'),
(120, 'yogurt'),
(121, 'cereal'),
(122, 'meat counter'),
(123, 'packaged vegetables fruits'),
(124, 'spirits'),
(125, 'trail mix snack mix'),
(126, 'feminine care'),
(127, 'body lotions soap'),
(128, 'tortillas flat bread'),
(129, 'frozen appetizers sides'),
(130, 'hot cereal pancake mixes'),
(131, 'dry pasta'),
(132, 'beauty'),
(133, 'muscles joints pain relief'),
(134, 'specialty wines champagnes');
-- Check the TABLE
SELECT
*
FROM
aisles
-----------------------------------------------------------
-- departments TABLE (shape: 21,2)
-----------------------------------------------------------
CREATE TABLE departments
(
department_id INT PRIMARY KEY,
department VARCHAR(255) NOT NULL
);
INSERT INTO departments
(department_id, department)
VALUES
('1', 'frozen'),
('2', 'other'),
('3', 'bakery'),
('4', 'produce'),
('5', 'alcohol'),
('6', 'international'),
('7', 'beverages'),
('8', 'pets'),
('9', 'dry goods pasta'),
('10', 'bulk'),
('11', 'personal care'),
('12', 'meat seafood'),
('13', 'pantry'),
('14', 'breakfast'),
('15', 'canned goods'),
('16', 'dairy eggs'),
('17', 'household'),
('18', 'babies'),
('19', 'snacks'),
('20', 'deli'),
('21', 'missing');
-- Check the TABLE
SELECT
*
FROM
departments
-- TRIAL ------------------------------------------------------------------
-- MERGE THE IDENTICAL TABLES INTO ONE
CREATE TABLE all_order_products_combined
(
order_id INT,
product_id VARCHAR(50),
add_to_cart_order INT,
reordered INT
);
-- Insert data from order_products__prior
INSERT INTO all_order_products_combined (order_id, product_id, add_to_cart_order, reordered)
SELECT order_id, product_id, add_to_cart_order, reordered FROM order_products__prior;
-- Insert data from order_products__train
INSERT INTO all_order_products_combined (order_id, product_id, add_to_cart_order, reordered)
SELECT order_id, product_id, add_to_cart_order, reordered FROM order_products__train;
-----------------------------------------------
SELECT TOP 1000
*
FROM
all_order_products_combined
------------------------------------------------
SELECT
COUNT(*) AS total_num_of_rows
FROM
all_order_products_combined
-- 33,819,106 rows
-- TRIAL ------------------------------------------------------------------
```
# DATA MANIPULATION LANGUAGE (DML)
```sql
-----------------------------------------------------------
-----------------------------------------------------------
-- DATA CLEANING
-- Data Manipulation Language (DML)
-----------------------------------------------------------
-----------------------------------------------------------
---------------------------------------------------------
-- CHECKING FOR DUPLICATE VALUES
---------------------------------------------------------
-- products TABLE
SELECT
product_name,
COUNT(*) AS total_num
FROM
products
GROUP BY
product_name
HAVING COUNT(product_name) > 1
/*
ANSWER: 101 DUPLICATE VALUES FOUND
Business Logic: Consider the business logic behind the data.
They look like duplicate values on paper,
but they have their own unique primary keys, they might be a different variation of the product
and might have orders connected with them and their unique primary key and product_id
I did not delete these, as it will affect the order counts, item popularity and other metrics
Deleting these duplicates could potentially disrupt the integrity of the data and impact the analysis.
*/
/*
Example: Bag of Oranges
1.) product_id = 4377
2.) product_id = 45231
*/
SELECT
*
FROM
products AS p
LEFT JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
LEFT JOIN orders AS o
ON o.order_id = AOP.order_id
WHERE
p.product_name = 'Bag of Oranges'
---------------------------------------------------------
-- CHECKING FOR NULL VALUES
---------------------------------------------------------
-- orders TABLE
SELECT
COUNT(*) AS TOTAL_NUM_OF_RECORDS,
COUNT(order_id) AS non_null_order_id,
COUNT(user_id) AS non_null_user_id,
COUNT(eval_set) AS non_null_eval_set,
COUNT(order_number) AS non_null_order_number,
COUNT(order_dow) AS non_null_order_dow,
COUNT(order_hour_of_day) AS non_null_order_hour_of_day,
COUNT(days_since_prior_order) AS non_null_days_since_prior_order
FROM
orders;
-- 206,209 NULL VALUES FOUND on "orders" TABLE and "days_since_prior_order" COLUMN
SELECT
*
FROM
orders
WHERE
days_since_prior_order IS NULL
-- 206,209 null values CONFIRMED
-- orders TABLE
SELECT
COUNT(*) AS TOTAL_NUM_OF_RECORDS
FROM
orders
WHERE
order_number = 1
/*
Business Logic: Consider the business logic behind the data.
This is normal
It can be seen that for every users 1st order (order_number = 1)
the days_since_prior_order is NULL, which makes sense, since it's the very first order.
Therefore the NULL VALUES are valid.
Business Logic: Consider the business logic behind the data.
The NULL VALUES are valid.
Because they correspond to the first orders for each user,
where there is no prior order to calculate the time since.
In this case, it's not necessary to replace these null values with "NA" or any other value,
as they already hold a meaningful interpretation.
Keeping them as null values preserves the distinction between the first orders and subsequent orders.
We can also infer that there are 206,209 users/customers.
*/
---------------------------------------------------------
-- Leading and Trailing spaces using TRIM()?
---------------------------------------------------------
-------- aisles table - aisle_name
UPDATE aisles
SET aisle_name = TRIM(aisle_name);
-------- departments table - department
UPDATE departments
SET department = TRIM(department);
-------- products table - product_name
UPDATE products
SET product_name = TRIM(product_name);
--------- orders table - eval_set
UPDATE orders
SET eval_set = TRIM(eval_set);
/*
Find all string columns for TRIM()
I made sure that all string columns have no excessive spaces using TRIM() function.
*/
```
# DATA QUERY LANGUAGE (DQL)
```sql
-- 1.) What are the most frequently ordered products?
SELECT TOP 10
p.product_name,
COUNT(o.order_number) AS ORDER_COUNT
FROM
products AS p
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders AS o
ON o.order_id = AOP.order_id
GROUP BY
p.product_name
ORDER BY
ORDER_COUNT DESC
/*
ANSWER:
product_name, ORDER_COUNT
Banana, 491291
Bag of Organic Bananas, 394930
Organic Strawberries, 275577
Organic Baby Spinach, 251705
Organic Hass Avocado, 220877
Organic Avocado, 184224
Large Lemon, 160792
Strawberries, 149445
Limes, 146660
Organic Whole Milk, 142813
*/
-- 2.) What are the least frequently ordered products?
SELECT TOP 10
p.product_name,
COUNT(o.order_number) AS ORDER_COUNT
FROM
products AS p
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders AS o
ON o.order_id = AOP.order_id
GROUP BY
product_name
ORDER BY
ORDER_COUNT ASC
/*
ANSWER:
(product_name, ORDER_COUNT)
'Swingtop' Premium Lager, 1
1 000 Mg Vitamin C Tangerine Grapefruit Effervescent Powdered Drink Mix, 1
11.3 Oz. Oreo Fudge Creme Double Chocolate, 1
12 Inch Taper Candle White, 1
7.04 Oz. Grahamfuls Banana Vanilla 8ct, 1
Aged Parmesan Cheese Sticks, 1
All Natural Stevia Liquid Extract Sweetener, 1
Anarchy For Her Daily Fragrance, 1
Anjou Pear Hand Soap, 1
Berry Sprouted Blend Cereal, 1
*/
-- 3.) Are there specific days of the week when orders are more frequent?
-- The days are anonymized, Day 0 could mean Sunday (or Monday if we go by ISO 8601)
SELECT
order_dow,
COUNT(order_number) AS ORDER_COUNT
FROM
orders
GROUP BY
order_dow
ORDER BY
order_dow ASC
/*
ANSWER:
(order_dow, ORDER_COUNT)
0 600905
1 587478
2 467260
3 436972
4 426339
5 453368
6 448761
*/
-- 4.) Are there specific hours of the day when orders are more frequent?
SELECT
order_hour_of_day,
COUNT(order_number) AS ORDER_COUNT
FROM
orders
GROUP BY
order_hour_of_day
ORDER BY
order_hour_of_day ASC
/*
ANSWER:
(order_hour_of_day, ORDER_COUNT)
0 22758
1 12398
2 7539
3 5474
4 5527
5 9569
6 30529
7 91868
8 178201
9 257812
10 288418
11 284728
12 272841
13 277999
14 283042
15 283639
16 272553
17 228795
18 182912
19 140569
20 104292
21 78109
22 61468
23 40043
*/
-- 5.) What are the most frequently REORDERED products?
SELECT TOP 10
p.product_name,
COUNT(AOP.product_id) AS REORDER_COUNT
FROM
products AS p
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders AS o
ON o.order_id = AOP.order_id
WHERE
AOP.reordered = 1
GROUP BY
p.product_name
ORDER BY
REORDER_COUNT DESC
/*
ANSWER:
(product_name, REORDER_COUNT)
Banana, 415166
Bag of Organic Bananas, 329275
Organic Strawberries, 214448
Organic Baby Spinach, 194939
Organic Hass Avocado, 176173
Organic Avocado, 140270
Organic Whole Milk, 118684
Large Lemon, 112178
Organic Raspberries, 109688
Strawberries, 104588
Organic fruits like Banana, Strawberries, and Avocados are frequently reordered
Milk is also frequently reordered.
These are the products that customer tend to stick with over time.
*/
-- 6.) Which products were never ordered / reordered?
SELECT
p.product_name,
COUNT(o.order_id) AS ORDER_COUNT
FROM
products AS p
LEFT JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
LEFT JOIN orders AS o
ON o.order_id = AOP.order_id
GROUP BY
p.product_name
HAVING
COUNT(o.order_id) = 0
/*
ANSWER:
(product_name, ORDER_COUNT)
Protein Granola Apple Crisp 0
Single Barrel Kentucky Straight Bourbon Whiskey 0
Unpeeled Apricot Halves in Heavy Syrup 0
We used LEFT JOIN to include all products even if there are no corresponding orders.
We used GROUP BY p.product_name to count the orders for each product.
We used HAVING clause to filter out products that have a count of orders equal to 0, meaning they were never ordered or reordered.
*/
-- TRIAL -------------------------------------------------------------------
SELECT
--*
p.product_name,
COUNT(o.order_number) AS ORDER_COUNT
FROM
products AS p
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders AS o
ON o.order_id = AOP.order_id
GROUP BY
p.product_name
HAVING
o.order_number = 0
-- TRIAL -------------------------------------------------------------------
-- 7.) What aisles are the most popular? (by order count)
SELECT TOP 20
a.aisle_name,
COUNT(o.order_number) AS ORDER_COUNT
FROM
aisles AS a
JOIN products AS p
ON a.aisle_id = p.aisle_id
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders as o
ON o.order_id = AOP.order_id
GROUP BY
a.aisle_name
ORDER BY
ORDER_COUNT DESC
/*
ANSWER:
(aisle_name, ORDER_COUNT)
fresh fruits, 3792661
fresh vegetables, 3568630
packaged vegetables fruits, 1843806
yogurt, 1507583
packaged cheese, 1021462
milk, 923659
water seltzer sparkling water, 878150
chips pretzels, 753739
soy lactosefree, 664493
bread, 608469
refrigerated, 599109
frozen produce, 545107
ice cream ice, 521101
crackers, 478430
energy granola bars, 473835
eggs, 472009
lunch meat, 412087
frozen meals, 408520
baby food formula, 395654
fresh herbs, 393793
*/
-- Using the ERD makes it easier to visualize what tables we need and what PATH we need to take
-- TRIAL AND ERROR -----------------------------------------------------
SELECT TOP 20
--*
--DISTINCT a.aisle_name AS DISTINCT_aisle_name
a.aisle_name,
--o.order_number,
COUNT(o.order_number) AS COUNT_of_order_number
FROM
aisles AS a
JOIN products AS p
ON a.aisle_id = p.aisle_id
JOIN order_products__prior as op_p
ON op_p.product_id = p.product_id
JOIN orders as o
ON o.order_id = op_p.order_id
GROUP BY
a.aisle_name
--o.order_number
ORDER BY
count_of_order_number DESC
-- TRIAL AND ERROR -----------------------------------------------------
-- 8.) What departments are the most popular? (by order count)
SELECT
d.department,
COUNT(o.order_number) AS ORDER_COUNT
FROM
departments AS d
JOIN products AS p
ON d.department_id = p.department_id
JOIN all_order_products_combined AS AOP
ON AOP.product_id = p.product_id
JOIN orders as o
ON o.order_id = AOP.order_id
GROUP BY
d.department
ORDER BY
ORDER_COUNT DESC
/*
ANSWER:
(department, ORDER_COUNT)
produce, 9888378
dairy eggs, 5631067
snacks, 3006412
beverages, 2804175
frozen, 2336858
pantry, 1956819
bakery, 1225181
canned goods, 1114857
deli, 1095540
dry goods pasta, 905340
household, 774652
meat seafood, 739238
breakfast, 739069
personal care, 468693
babies 438743
international 281155
alcohol 159294
pets 102221
missing 77396
other 38086
bulk 35932
*/
-- 9.) When do customers usually reorder?
SELECT
days_since_prior_order,
COUNT(order_number) AS ORDER_COUNT
FROM
orders
GROUP BY
days_since_prior_order
ORDER BY
days_since_prior_order ASC
/*
ANSWERS:
(days_since_prior_order, ORDER_COUNT)
NULL 206209
0 67755
1 145247
2 193206
3 217005
4 221696
5 214503
6 240013
7 320608
8 181717
9 118188
10 95186
11 80970
12 76146
13 83214
14 100230
15 66579
16 46941
17 39245
18 35881
19 34384
20 38527
21 45470
22 32012
23 23885
24 20712
25 19234
26 19016
27 22013
28 26777
29 19191
30 369323
(LIMITATIONS: days_since_prior_order is capped at 30)
Users/customers tend to order the most after 7 days or 30 days.
Most customers buy groceries either Weekly or Monthly.
*/
-- END
```
