• 1 Introduction, Research Goal & Objective
• 2 Related works
• 3 Dataset Preparation
  • 3.1 R Libraries Used
  • 3.2 Import Datasets
  • 3.3 Data Dictionary
  • 3.4 Understanding Datasets
    • 3.4.1 Aisles
    • 3.4.2 Departments
    • 3.4.3 Products
    • 3.4.4 Departments And Its Relevant Products
    • 3.4.5 Aisles And Its Relevant Products
    • 3.4.6 Orders
    • 3.4.7 Order_Product_Train/Prior
    • 3.4.8 Users
• 4 Exploratory Data Analysis
  • 4.1 Orders_Products
    • 4.1.1 Most Popular Products Sold
    • 4.1.2 Most Popular Department Sold
    • 4.1.3 Most Popular Aisles Sold
    • 4.1.4 Products Ordered Day Pattern
    • 4.1.5 Products Ordered Hour Pattern
  • 4.2 Reordering Analysis
• 5 Predictive Analysis
  • 5.1 Type of Prediction
  • 5.2 Model Evaluation & Optimization
  • 5.3 Model 1 : Naive Prediction
    • 5.3.1 Build The Model
    • 5.3.2 Sample Output
    • 5.3.3 Confusion Matrix
    • 5.3.4 Model Performance
  • 5.4 Model 2 : Smarter Naive Prediction (Baseline)
    • 5.4.1 Build The Model
    • 5.4.2 Sample Output
    • 5.4.3 Optimize Cutoff
    • 5.4.4 Confusion Matrix
    • 5.4.5 Model Performance
  • 5.5 Model 3 : Machine Learning Classification
    • 5.5.1 Feature Engineering
      • 5.5.1.1 User Features
      • 5.5.1.2 Product Features
    • 5.5.2 Construct Training Data with Label
  • 5.6 Selection of Model
  • 5.7 Data Recoding
  • 5.8 Prediction
• 6 Analysis & Recommendations
• 7 Citations

library(knitr)      # web widget
library(tidyverse)  # data manipulation
library(data.table) # fast file reading
library(treemap)    # tree visualization
library(caret)      # rocr analysis
library(ROCR)       # rocr analysis
library(kableExtra) # nice table html formating 
library(gridExtra)  # arranging ggplot in grid
#library(corrgram)   # correlation graphics

setwd('./datasets')
aisles      = fread('aisles.csv',      stringsAsFactors = TRUE)
departments = fread('departments.csv', stringsAsFactors = TRUE)
products    = fread('products.csv',    stringsAsFactors = TRUE)
orders      = fread('orders.csv',      stringsAsFactors = TRUE)
order_products_train = fread('order_products__train.csv')
order_products_prior = fread('order_products__prior.csv')

paste(sort(head(aisles$aisle)), collapse=', ')

[1] "energy granola bars, instant foods, marinades meat preparation, other, prepared soups salads, specialty cheeses"

paste(sort(departments$department), collapse = ', ')

[1] "alcohol, babies, bakery, beverages, breakfast, bulk, canned goods, dairy eggs, deli, dry goods pasta, frozen, household, international, meat seafood, missing, other, pantry, personal care, pets, produce, snacks"

products %>% head %>% kable

left_join(departments, products) %>% select(department, product_name ) %>%
  group_by(department) %>%
  sample_n(3) %>%
  summarise(three_examples_product=paste(product_name, collapse=' || ')) %>% sample_n(5) %>% kable

left_join(aisles, products) %>%
  select(aisle, product_name ) %>%  group_by(aisle) %>%
  sample_n(3) %>% 
  summarise(three_examples_product=paste(product_name, collapse=' || ')) %>% sample_n(5) %>% kable

orders %>% filter (user_id==1) %>% kable

orders %>% filter (user_id==3) %>% kable

order_products_train %>% filter (order_id == 1187899) %>% kable

table(orders$eval_set) %>% kable(col.names = c('eval_set','Frequency')) %>%   kable_styling(bootstrap_options = c("striped", "hover"), position = "float_right")

tmp = order_products_train %>%
  left_join(products) %>%
  group_by(product_name) %>%
  summarize(count=n()) %>%
  top_n(n=30, wt=count) %>%  mutate(percentage=count/sum(count))
p1 = ggplot (tmp, aes(x=reorder(product_name,count), y=percentage)) +  
  geom_col() + ggtitle('Products Top 30') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) 
p2 = ggplot (data = tmp, aes( x= '', y=percentage )) + 
  ggtitle('Products Top 30') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Products')
grid.arrange(p1, p2, ncol = 2)

tmp = order_products_train %>%
  left_join(products) %>%
  left_join(departments) %>%
  group_by(department) %>%
  summarize(count=n()) %>%
  mutate(percentage=count/sum(count))
p1 = ggplot (tmp, aes(x=reorder(department,count), y=percentage)) +  
  geom_col() + ggtitle('Departments') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) 
p2 = ggplot (data = tmp, aes( x= '', y=percentage )) + 
  ggtitle('Departments') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Departments')
grid.arrange(p1, p2, ncol = 2)

tmp = order_products_train %>%
  left_join(products) %>%
  left_join(aisles) %>%
  group_by(aisle) %>%
  summarize(count=n()) %>%
  top_n(n=30, wt=count) %>%  mutate(percentage=count/sum(count))
p1 = ggplot (tmp, aes(x=reorder(aisle,count), y=percentage)) +  
  geom_col() + ggtitle('Aisles Top 30') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) +  ylab('Percentage of Orders') + xlab('Aisles')
p2 = ggplot (tmp, aes( x= '', y=percentage )) + 
  ggtitle('Aisles Top 30') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Aisles')
grid.arrange(p1, p2, ncol = 2)

order_products_prior %>%
  left_join(orders) %>%
  group_by(order_dow) %>%
  summarize(count = n()) %>%
  mutate(percentage=count/sum(count)) %>%
  ggplot (aes(x=as.factor(order_dow), y=percentage)) + 
    geom_col()+ ylab('Percentage of Orders') + ggtitle('Daily Orders')

order_products_prior %>% 
  left_join(orders) %>% left_join(products) %>%
  group_by(order_dow, product_name) %>%
  summarize(n=n()) %>%
  mutate(percentage=n/sum(n)) %>%
  top_n(10, wt=n) %>%
  ggplot (aes(x=as.factor(order_dow), y=percentage, fill=product_name)) + 
    geom_col() + ylab('Proprtion of Orders') + ggtitle('Daily Top 10 Products Ordered') +
    theme(legend.position="bottom",legend.direction="horizontal")

order_products_prior %>%
  left_join(orders) %>%
  group_by(order_hour_of_day) %>%
  summarize(count = n()) %>%
  mutate(percentage=count/sum(count)) %>%
  ggplot (aes(x=as.factor(order_hour_of_day), y=percentage)) + 
    geom_col()+ ylab('Percentage of Orders') + ggtitle('Hourly Orders')

order_products_prior %>% 
  left_join(orders) %>% left_join(products) %>%
  group_by(order_hour_of_day, product_name) %>%
  summarize(n=n()) %>%
  mutate(percentage=n/sum(n)) %>%
  top_n(10, wt=n) %>%
  ggplot (aes(x=as.factor(order_hour_of_day), y=percentage, fill=product_name)) + 
    geom_col() + ylab('Proprtion of Orders In A Hour') + ggtitle('Hourly Top 10 Products Ordered') +
    theme(legend.position="bottom",legend.direction="horizontal")

Joining, by = "order_id"
Joining, by = "product_id"

## Training Label
train.label = orders %>% 
  filter(eval_set=='train') %>% 
  left_join(order_products_train) %>%
  left_join(products) %>%
  mutate(actual=1) %>%   #this is training label
  select(user_id, order_id, product_id, product_name, actual)

train.label %>% filter(user_id==1) %>% kable %>%  kable_styling(bootstrap_options = c("striped"))

## Custom Function For Binary Class Performance Evaluation
binclass_eval = function (actual, predict) {
  cm = table(as.integer(actual), as.integer(predict), dnn=c('Actual','Predicted'))
  ac = (cm['1','1']+cm['0','0'])/(cm['0','1'] + cm['1','0'] + cm['1','1'] + cm['0','0'])
  pr = cm['1','1']/(cm['0','1'] + cm['1','1'])
  rc = cm['1','1']/(cm['1','0'] + cm['1','1'])
  fs = 2* pr*rc/(pr+rc)
  list(cm=cm, recall=rc, precision=pr, fscore=fs, accuracy=ac)
}

### Cutoff Threshold Optimization
optimize_cutoff = function (actual, probability) {
  rocr.pred = prediction(predictions = probability, labels = actual)
  rocr.metrics = data.frame(
      cutoff   = rocr.pred@cutoffs[[1]],
      accuracy = (rocr.pred@tp[[1]] + rocr.pred@tn[[1]]) / 
                   (rocr.pred@tp[[1]] + rocr.pred@tn[[1]] + rocr.pred@fp[[1]] + rocr.pred@fn[[1]]),
      tpr = rocr.pred@tp[[1]] / (rocr.pred@tp[[1]] + rocr.pred@fn[[1]]),
      fpr = rocr.pred@fp[[1]] / (rocr.pred@fp[[1]] + rocr.pred@tn[[1]]),
      ppv = rocr.pred@tp[[1]] / (rocr.pred@tp[[1]] + rocr.pred@fp[[1]])
  )
  rocr.metrics$fscore = 2 * (rocr.metrics$tpr * rocr.metrics$ppv) / (rocr.metrics$tpr + rocr.metrics$ppv)
  rocr.metrics$tpr_fpr = rocr.metrics$tpr / rocr.metrics$fpr
  
  ## Discovery the optimal threshold for various metrics
  rocr.best = rbind(
    best.accuracy = c(max = max(rocr.metrics$accuracy, na.rm = TRUE), cutoff=rocr.metrics$cutoff[which.max(rocr.metrics$accuracy)]),
    best.ppv = c(max = max(rocr.metrics$ppv, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$ppv)]),
    best.recall = c(max = max(rocr.metrics$tpr, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$tpr)]),
    best.fscore = c(max = max(rocr.metrics$fscore, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$fscore)]),
    best.tpr_fpr = c(max = max(rocr.metrics$tpr_fpr, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$tpr_fpr)])
  )
  
  list(metrics = rocr.metrics, best = rocr.best)
}

m1.predict = orders %>% 
  filter(eval_set=='prior') %>% 
  group_by(user_id) %>%
  top_n(n=1, wt=order_number) %>%       #last order has the higher order_number
  left_join(order_products_prior) %>%
  mutate (
    predicted=1) %>%              #predict based on last ordered, therefore 1
  select(user_id, product_id, predicted) %>% 
  full_join(train.label) %>%  # join with train.label for items not predicted but in train.label
  select(user_id, product_id, actual, predicted) %>%
  replace(., is.na(.), 0) %>% # treat not predicted as 0
  arrange(user_id, product_id)

m2.predict %>% filter(user_id ==1) %>% kable %>% kable_styling(bootstrap_options = c("striped"))

m1.eval = binclass_eval(m1.predict$actual, m1.predict$predicted)
m1.eval$cm

      Predicted
Actual       0       1
     0       0 1760406
     1 1005235  379382

cat("Accuracy:  ", m1.eval$accuracy,
    "\nPrecision: ", m1.eval$precision,
    "\nRecall:    ", m1.eval$recall,
    "\nFScore:    ", m1.eval$fscore)

Accuracy:   0.1206293 
Precision:  0.1772989 
Recall:     0.2739978 
FScore:     0.2152885

## Build Model
m2.predict = orders %>% 
  filter(eval_set=='prior') %>% 
  group_by(user_id) %>%
  mutate(total_orders = max(order_number)) %>%  # total number of orders made previously
  ungroup %>% select(user_id, order_id, total_orders) %>%
  left_join(order_products_prior) %>%
  group_by(user_id, product_id) %>%
  summarize(rate=n()/max(total_orders)) %>%
  select(user_id, product_id, rate) %>%
  full_join(train.label) %>%  # join with train.label for items not predicted but in train.label
  select(user_id, product_id, actual, rate) %>%
  replace(., is.na(.), 0) %>% # treat not predicted as 0
  arrange(user_id, product_id)

m2.predict %>% filter(user_id==1) %>% kable %>% kable_styling(bootstrap_options = c("striped"))

m2.eval = binclass_eval(m2.predict$actual, m2.predict$rate>0.3368)
m2.eval$cm

      Predicted
Actual        0        1
     0 11548605   930524
     1  1081948   302669

cat("Accuracy:  ", m2.eval$accuracy,
    "\nPrecision: ", m2.eval$precision,
    "\nRecall:    ", m2.eval$recall,
    "\nFScore:    ", m2.eval$fscore)

Accuracy:   0.8548392 
Precision:  0.2454352 
Recall:     0.218594 
FScore:     0.2312383

#m3.predict = orders %>% 
#  filter(eval_set=='prior') %>% 
#  left_join(order_products_prior) %>%
#  group_by(user_id, product_id) %>%
#  summarize(rate=n()/max(total_orders))

users.train.avg_items = orders %>% 
  filter(eval_set=='train') %>%
  select(user_id) %>%
  left_join(orders) %>%
  left_join(order_products_prior) %>%
  count(user_id, order_id) %>%
  group_by(user_id) %>%
  summarize(avg_user_items = mean(n))
users.train.avg_dowhod = orders %>% 
  filter(eval_set=='train') %>%
  select(user_id) %>%
  left_join(orders) %>%
  group_by(user_id) %>%
  summarize( 
    avg_user_order_dow = mean(order_dow),
    avg_user_order_hod = mean(order_hour_of_day))
users.train = users.train.avg_items %>% left_join(users.train.avg_dowhod)
  
users.test.avg_items = orders %>% 
  filter(eval_set=='test') %>%
  select(user_id) %>%
  left_join(orders) %>%
  left_join(order_products_prior) %>%
  count(user_id, order_id) %>%
  group_by(user_id) %>%
  summarize(avg_user_items = mean(n))
users.test.avg_dowhod = orders %>% 
  filter(eval_set=='test') %>%
  select(user_id) %>%
  left_join(orders) %>%
  group_by(user_id) %>%
  summarize( 
    avg_user_order_dow = mean(order_dow),
    avg_user_order_hod = mean(order_hour_of_day))
users.test = users.test.avg_items %>% left_join(users.test.avg_dowhod)
rm(list=c('users.test.avg_items','users.test.avg_dowhod','users.train.avg_items','users.train.avg_dowhod'))

head(users.test) %>% kable %>% kable_styling(bootstrap_options = c("striped"))

products_ = orders %>% 
  left_join(order_products_prior) %>%
  group_by(product_id) %>%
  summarize( 
    avg_product_order_dow = mean(order_dow),
    avg_product_order_hod = mean(order_hour_of_day)
  ) %>%
  left_join(products)
head(products_) %>% kable %>% kable_styling(bootstrap_options = c("striped"))

m3.train.label = train.label %>% 
  left_join(users.train) %>%   #combine with new user features
  left_join(products_)          #combine with new product features

Joining, by = "user_id"
Joining, by = c("product_id", "product_name")

head(m3.train.label) %>% kable %>% kable_styling(bootstrap_options = c("striped"))

---
title: "Instacart Market Basket Analysis"
author: "Yong Keh Soon"
date: "18 November 2018"
output:
  html_notebook:
    #code_folding: hide
    number_sections: yes
    theme: cerulean
    toc: yes
    toc_depth: 4
  html_document:
    df_print: paged
    toc: yes
    toc_depth: '4'
---


<style type="text/css">

body{ /* Normal  */
      font-size: 12px;
  }
td {  /* Table  */
  font-size: 12px;
}
h1.title {
  font-size: 38px;
  color: DarkRed;
}
h1 { /* Header 1 */
  font-size: 24px;
  color: DarkBlue;
}
h2 { /* Header 2 */
  font-size: 20px;
  color: DarkBlue;
}
h3 { /* Header 3 */
  font-size: 16px;
#  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
}
h4 { /* Header 4 */
  font-size: 14px;
  color: DarkBlue;
}
code.r{ /* Code block */
    font-size: 12px;
}
pre { /* Code block - determines code spacing between lines */
    font-size: 12px;
}
</style>


```{r setup, include=FALSE}
#knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(echo=TRUE, fig.height=3.5, fig.width=9.2, results='hold', warning=FALSE, fig.show='hold', message=FALSE) 
```

# Introduction, Research Goal & Objective


# Related works


# Dataset Preparation

## R Libraries Used

Here are the R libraries used in this analysis. 

```{r, message=FALSE}
library(knitr)      # web widget
library(tidyverse)  # data manipulation
library(data.table) # fast file reading
library(treemap)    # tree visualization
library(caret)      # rocr analysis
library(ROCR)       # rocr analysis
library(kableExtra) # nice table html formating 
library(gridExtra)  # arranging ggplot in grid
#library(corrgram)   # correlation graphics
```

## Import Datasets

```{r, results='hide', message=FALSE, warning=FALSE}
setwd('./datasets')
aisles      = fread('aisles.csv',      stringsAsFactors = TRUE)
departments = fread('departments.csv', stringsAsFactors = TRUE)
products    = fread('products.csv',    stringsAsFactors = TRUE)
orders      = fread('orders.csv',      stringsAsFactors = TRUE)
order_products_train = fread('order_products__train.csv')
order_products_prior = fread('order_products__prior.csv')
```

## Data Dictionary

The dataset for this competition is a relational set of files describing customers' orders over time. They are anonymized and contains a sample of over **3 million grocery orders** from more than **200,000 Instacart users**. For each user, Instacart provided **between 4 and 100** of their orders, with the sequence of products purchased in each order, the **week and hour of day** the order was placed, and a **relative measure of time between orders**.

Total six datasets were imported. Follwing section will explore each datasets in further detail. These datasets were sourced from an existing Kaggle competiotion (https://www.kaggle.com/c/instacart-market-basket-analysis/data)

`orders` (3.4m rows, 206k users):  

* `order_id`: order identifier  
* `user_id`: customer identifier  
* `eval_set`: which evaluation set this order belongs in (see `SET` described below)  
* `order_number`: the order sequence number for this user (1 = first, n = nth)  
* `order_dow`: the day of the week the order was placed on  
* `order_hour_of_day`: the hour of the day the order was placed on  
* `days_since_prior`: days since the last order, capped at 30 (with NAs for `order_number` = 1)  

`products` (50k rows):  

* `product_id`: product identifier  
* `product_name`: name of the product  
* `aisle_id`: foreign key  
* `department_id`: foreign key  

`aisles` (134 rows):  

* `aisle_id`: aisle identifier  
* `aisle`: the name of the aisle  

`deptartments` (21 rows):  

* `department_id`: department identifier  
* `department`: the name of the department  

`order_products__SET` (30m+ rows):  

* `order_id`: foreign key  
* `product_id`: foreign key  
* `add_to_cart_order`: order in which each product was added to cart  
* `reordered`: 1 if this product has been ordered by this user in the past, 0 otherwise  

where `SET` is one of the four following evaluation sets (`eval_set` in `orders`):  

* `"prior"`: orders prior to that users most recent order (~3.2m orders)  
* `"train"`: training data supplied to participants (~131k orders)  
* `"test"`: test data reserved for machine learning competitions (~75k orders)  

## Understanding Datasets

### Aisles

There are **134 ailes** in this dataset. Here are few sample names of the ailes.  

```{r, results='hold'}
paste(sort(head(aisles$aisle)), collapse=', ')
```

### Departments

There are **21 departments** in this dataset.Names of all deparments are listed below in aphabetically ordered.

```{r, results='hold'}
paste(sort(departments$department), collapse = ', ')
```

### Products

There are **49,688 products** in the catalogue within **134 aisles and 21 departments**. Sample products are as below.

```{r, results='hold'}
products %>% head %>% kable
```

### Departments And Its Relevant Products

Products dataframe is related to Deparments. We shall see below a glimpse of 3 products for each deparment, giving an idea the how the products being departmentized. Only sample five departments are shown.

```{r, results='hold', message=FALSE}
left_join(departments, products) %>% select(department, product_name ) %>%
  group_by(department) %>%
  sample_n(3) %>%
  summarise(three_examples_product=paste(product_name, collapse=' || ')) %>% sample_n(5) %>% kable

```

### Aisles And Its Relevant Products

Products dataframe is also related to aisles. Each aisle relates to multiple prodcuts. By joining both aisles and products dataframe, we have an idea what type of prodcuts for each ailes. Example below shows sample of aisles and few of its related products.

```{r, results='hold', message=FALSE}
left_join(aisles, products) %>%
  select(aisle, product_name ) %>%  group_by(aisle) %>%
  sample_n(3) %>% 
  summarise(three_examples_product=paste(product_name, collapse=' || ')) %>% sample_n(5) %>% kable
```

### Orders

There are over **3 millions** observations in orders dataset. Each row represent an **unique order**.  Each variable potentially can be used as predictors. Let's analyse the construct of one user. For example, **user_id 1** had made **10 prior orders** (order number from 1 to 10), last order is a **train** (eval_set).

```{r}
orders %>% filter (user_id==1) %>% kable
```

Let's analyse another construct of orders. **User_id 3** had made **12 orders** before the final order labeled as **test** (eval_set) order. From the data we know that order_number is being recycled for each user.  Instacart did not provide us the basket content for test order. This is in fact the **target for prediction**. 

This also means **`<user_id, product_id>`** made up the **key** for prediction.  

```{r}
orders %>% filter (user_id==3) %>% kable
```

### Order_Product_Train/Prior

**order_product_train/prior** dataframe tells us which products were purchased in each order, for both train order and prior order. For example, we can know **user_id 1** in the last order (order_id 1187899) purchased **10 unique products** by **quering order_product_train** with the relevant order_id.  

```{r}
order_products_train %>% filter (order_id == 1187899) %>% kable
```

### Users

Ther is no dedicated dataframe for users. However, we can derive number of unique users from **order** dataframe. By grouping the user_id and eval_set column, we found that there are **75,000 test** users, **131,209 train** users.  

We shall use **train users** for training, and **test users** for submission to Kaggle.We want to keep this large number of train records in mind for **trimming** during quick initial model build and verification.

```{r}
table(orders$eval_set) %>% kable(col.names = c('eval_set','Frequency')) %>%   
  kable_styling(bootstrap_options = c("striped", "hover"))
```

# Exploratory Data Analysis

In this section, we shall try to understand the buying behaviour by asking some interesting quesitons.  

- What usually does people buy, and which one they usually reorder
- When do they buy (day and time)? Is there a buying trend and does it influence what they buy ?  

## Orders_Products

### Most Popular Products Sold

We know that **banana** are the most popular products. The number of orders varies greatly for different products. Illustration below uses shows sample of only **30 top products**.  Notice however the varience is not obvious after top 10 products.

```{r, fig.width=9.5, fig.height=3.5}
tmp = order_products_train %>%
  left_join(products) %>%
  group_by(product_name) %>%
  summarize(count=n()) %>%
  top_n(n=30, wt=count) %>%  mutate(percentage=count/sum(count))

p1 = ggplot (tmp, aes(x=reorder(product_name,count), y=percentage)) +  
  geom_col() + ggtitle('Products Top 30') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) 

p2 = ggplot (data = tmp, aes( x= '', y=percentage )) + 
  ggtitle('Products Top 30') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Products')

grid.arrange(p1, p2, ncol = 2)
```
```{r echo=FALSE}
rm(list=c('tmp','p1','p2'))
```

### Most Popular Department Sold

Certain departmens are clearly more popular, like **produce and dairy eggs**. Both deparments combined contributed to **more than 40%** of total orders.

```{r, fig.width=9.5, fig.height=3.5}
tmp = order_products_train %>%
  left_join(products) %>%
  left_join(departments) %>%
  group_by(department) %>%
  summarize(count=n()) %>%
  mutate(percentage=count/sum(count))

p1 = ggplot (tmp, aes(x=reorder(department,count), y=percentage)) +  
  geom_col() + ggtitle('Departments') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) 

p2 = ggplot (data = tmp, aes( x= '', y=percentage )) + 
  ggtitle('Departments') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Departments')

grid.arrange(p1, p2, ncol = 2)
```

```{r echo=FALSE}
rm(list=c('tmp','p1','p2'))
```

### Most Popular Aisles Sold

We looked into the buying trend of product by ailes and notice that certain aisle like **vegetables and fruits** contributes to **almost 30%** of total orders. Chart below shows top 30 aisles.

```{r, fig.width=9.5, fig.height=3.5}
tmp = order_products_train %>%
  left_join(products) %>%
  left_join(aisles) %>%
  group_by(aisle) %>%
  summarize(count=n()) %>%
  top_n(n=30, wt=count) %>%  mutate(percentage=count/sum(count))

p1 = ggplot (tmp, aes(x=reorder(aisle,count), y=percentage)) +  
  geom_col() + ggtitle('Aisles Top 30') + ylab('Percentage of Orders') +
  theme (
    axis.text.x=element_text(angle=90, hjust=1, vjust=0.5),
    axis.title.x = element_blank()) +  ylab('Percentage of Orders') + xlab('Aisles')

p2 = ggplot (tmp, aes( x= '', y=percentage )) + 
  ggtitle('Aisles Top 30') + ylab('percentage.of.orders') + geom_boxplot() + xlab('Aisles')

grid.arrange(p1, p2, ncol = 2)
```

```{r echo=FALSE}
rm(list=c('tmp','p1','p2'))
```

### Products Ordered Day Pattern

We can see that both **Day 0 and Day 1** stands out to be the most busy shopping day for instacart. This means that day of order made may influence the basket size.

```{r,fig.width=9.3, fig.height=3.0}
order_products_prior %>%
  left_join(orders) %>%
  group_by(order_dow) %>%
  summarize(count = n()) %>%
  mutate(percentage=count/sum(count)) %>%
  ggplot (aes(x=as.factor(order_dow), y=percentage)) + 
    geom_col()+ ylab('Percentage of Orders') + ggtitle('Daily Orders')
```

Top ten products ordered daily contributes between 7% to 8%. It is interesting to see that Limes are part of top ten for Day 0 and Day 6, but not other days. Wherease Organic Whole Milk doesn't make it to top ten for Day 0. Organic Respberries does not make it to top 10 of Day 6. This means that there is a chance of predictability based on the day order is made. 

```{r,fig.width=9.3, fig.height=4.5}
order_products_prior %>% 
  left_join(orders) %>% left_join(products) %>%
  group_by(order_dow, product_name) %>%
  summarize(n=n()) %>%
  mutate(percentage=n/sum(n)) %>%
  top_n(10, wt=n) %>%
  ggplot (aes(x=as.factor(order_dow), y=percentage, fill=product_name)) + 
    geom_col() + ylab('Proprtion of Orders In A Day') + ggtitle('Daily Top 10 Products Ordered') +
    theme(legend.position="bottom",legend.direction="horizontal")
```

### Products Ordered Hour Pattern

Morning to afternoon are the peak shopping hours for instacart customers. The hour order made influences basket size.

```{r,fig.width=9.3, fig.height=3.0}
order_products_prior %>%
  left_join(orders) %>%
  group_by(order_hour_of_day) %>%
  summarize(count = n()) %>%
  mutate(percentage=count/sum(count)) %>%
  ggplot (aes(x=as.factor(order_hour_of_day), y=percentage)) + 
    geom_col()+ ylab('Percentage of Orders') + ggtitle('Hourly Orders')
```

In the grocery, there are close to 50,000 products. When we zoom into hourly purchases, we noticed that top 10 products managed to score betwen 6% to 8% of hourly sales. Every hour has slightly diffrent combination of top 10 products (combination out of 12 products). That means certain products are predictable for ordering irregardless of the hour of order.

```{r,fig.width=9.3, fig.height=5}
order_products_prior %>% 
  left_join(orders) %>% left_join(products) %>%
  group_by(order_hour_of_day, product_name) %>%
  summarize(n=n()) %>%
  mutate(percentage=n/sum(n)) %>%
  top_n(10, wt=n) %>%
  ggplot (aes(x=as.factor(order_hour_of_day), y=percentage, fill=product_name)) + 
    geom_col() + ylab('Proprtion of Orders In A Hour') + ggtitle('Hourly Top 10 Products Ordered') +
    theme(legend.position="bottom",legend.direction="horizontal")
```


## Reordering Analysis

# Predictive Analysis

## Type of Prediction

Predict what product will the customer purchase in the next basket means estimation of probably if each product being purchased before will be purchased again. **This is a classification problem**, as well as **a regression of probability** of repurchases. The output of the prediction is to be evaluated against training eval_set. with below table columns:  

- `<user_id product_id>`: as key  
- `label` : 1 or 0 , this is the target label  

For submission to kagggle, output from above table should be transformed into the form below:

- `user_id`  
- `<list of product id seperated by space>`  

First, let's create a **training label**, which is the value or either 1 or 0 to indicate the actual basket content. All prediction will be evaluated against this training label.

```{r message=FALSE}
## Training Label
train.label = orders %>% 
  filter(eval_set=='train') %>% 
  left_join(order_products_train) %>%
  left_join(products) %>%
  mutate(actual=1) %>%   #this is training label
  select(user_id, order_id, product_id, product_name, actual)
```

Let's see an example of **training label** for user_id 1. As this is training eval_set, the single order_id shows that it is the final order. We shall use this as the label (actual) for training. 

```{r message=FALSE}
train.label %>% filter(user_id==1) %>% kable %>%  kable_styling(bootstrap_options = c("striped"))
```

## Model Evaluation & Optimization

Instacart has close to 50k products in their catalogue. As the maximum number of items ordered by a user is just a fraction of the 50k available product. This means by simply predicting nothing is purchased in the next basket, we would yeild **close to 100% accuracy**.  

Due to the **highly imbalance** dataset, Instacart require **F1 Score** as the competition scoring, instead of accuracy.  

To evaluate the performance of the model, we had created a custom function to build a **confusion matrix and derive other binary classification metrics**. 

```{r}
## Custom Function For Binary Class Performance Evaluation
binclass_eval = function (actual, predict) {
  cm = table(as.integer(actual), as.integer(predict), dnn=c('Actual','Predicted'))
  ac = (cm['1','1']+cm['0','0'])/(cm['0','1'] + cm['1','0'] + cm['1','1'] + cm['0','0'])
  pr = cm['1','1']/(cm['0','1'] + cm['1','1'])
  rc = cm['1','1']/(cm['1','0'] + cm['1','1'])
  fs = 2* pr*rc/(pr+rc)
  list(cm=cm, recall=rc, precision=pr, fscore=fs, accuracy=ac)
}
```

If the prediction is based on probability, we shall build a function to discover **cutoff that optimize various performance metrics**. 

```{r}
### Cutoff Threshold Optimization
optimize_cutoff = function (actual, probability) {
  rocr.pred = prediction(predictions = probability, labels = actual)
  rocr.metrics = data.frame(
      cutoff   = rocr.pred@cutoffs[[1]],
      accuracy = (rocr.pred@tp[[1]] + rocr.pred@tn[[1]]) / 
                   (rocr.pred@tp[[1]] + rocr.pred@tn[[1]] + rocr.pred@fp[[1]] + rocr.pred@fn[[1]]),
      tpr = rocr.pred@tp[[1]] / (rocr.pred@tp[[1]] + rocr.pred@fn[[1]]),
      fpr = rocr.pred@fp[[1]] / (rocr.pred@fp[[1]] + rocr.pred@tn[[1]]),
      ppv = rocr.pred@tp[[1]] / (rocr.pred@tp[[1]] + rocr.pred@fp[[1]])
  )
  rocr.metrics$fscore = 2 * (rocr.metrics$tpr * rocr.metrics$ppv) / (rocr.metrics$tpr + rocr.metrics$ppv)
  rocr.metrics$tpr_fpr = rocr.metrics$tpr / rocr.metrics$fpr
  
  ## Discovery the optimal threshold for various metrics
  rocr.best = rbind(
    best.accuracy = c(max = max(rocr.metrics$accuracy, na.rm = TRUE), cutoff=rocr.metrics$cutoff[which.max(rocr.metrics$accuracy)]),
    best.ppv = c(max = max(rocr.metrics$ppv, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$ppv)]),
    best.recall = c(max = max(rocr.metrics$tpr, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$tpr)]),
    best.fscore = c(max = max(rocr.metrics$fscore, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$fscore)]),
    best.tpr_fpr = c(max = max(rocr.metrics$tpr_fpr, na.rm = TRUE), cutoff = rocr.metrics$cutoff[which.max(rocr.metrics$tpr_fpr)])
  )
  
  list(metrics = rocr.metrics, best = rocr.best)
}
```



## Model 1 : Naive Prediction

### Build The Model

We can simply predict the basket based on user last order. 

```{r message=FALSE}
m1.predict = orders %>% 
  filter(eval_set=='prior') %>% 
  group_by(user_id) %>%
  top_n(n=1, wt=order_number) %>%       #last order has the higher order_number
  left_join(order_products_prior) %>%
  mutate (
    predicted=1) %>%              #predict based on last ordered, therefore 1
  select(user_id, product_id, predicted) %>% 
  full_join(train.label) %>%  # join with train.label for items not predicted but in train.label
  select(user_id, product_id, actual, predicted) %>%
  replace(., is.na(.), 0) %>% # treat not predicted as 0
  arrange(user_id, product_id)
```
### Sample Output

```{r}
m2.predict %>% filter(user_id ==1) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```


### Confusion Matrix

```{r model1-eval}
m1.eval = binclass_eval(m1.predict$actual, m1.predict$predicted)
m1.eval$cm
```

### Model Performance

The result shows only **0.2153 F1 Score**.

```{r}
cat("Accuracy:  ", m1.eval$accuracy,
    "\nPrecision: ", m1.eval$precision,
    "\nRecall:    ", m1.eval$recall,
    "\nFScore:    ", m1.eval$fscore)
```

```{r echo=FALSE}
rm(list=c('m1.predict'))
```

## Model 2 : Smarter Naive Prediction (Baseline)

In this model, we predict products in the basket by estimating their frequency of repurchased. This way we get a ratio to indicate probability of re-purchases. We use ROCR package to predict the best cutoff point (at which above this cutoff we shall predict for re-order) that give us the **optimum F1 score**. 

### Build The Model

```{r model2-building, message=FALSE}
## Build Model
m2.predict = orders %>% 
  filter(eval_set=='prior') %>% 
  group_by(user_id) %>%
  mutate(total_orders = max(order_number)) %>%  # total number of orders made previously
  ungroup %>% select(user_id, order_id, total_orders) %>%
  left_join(order_products_prior) %>%
  group_by(user_id, product_id) %>%
  summarize(rate=n()/max(total_orders)) %>%
  select(user_id, product_id, rate) %>%
  full_join(train.label) %>%  # join with train.label for items not predicted but in train.label
  select(user_id, product_id, actual, rate) %>%
  replace(., is.na(.), 0) %>% # treat not predicted as 0
  arrange(user_id, product_id)
```

### Sample Output

```{r}
m2.predict %>% filter(user_id==1) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```
### Optimize Cutoff

We see that in order to maximize **F1 Score**, we need to set the cutoff threshold to 0.3368, which is the next step.

```{r model2-optimize}
### Threshold Optimization
m2.rocr = optimize_cutoff(actual = m2.predict$actual, probability = m2.predict$rate)
kable(m2.rocr$best) %>% kable_styling(bootstrap_options = c("striped"))
```

### Confusion Matrix

Let's set the **cutoff to 0.3368** as discovered in previous step.

```{r model2-eval}
m2.eval = binclass_eval(m2.predict$actual, m2.predict$rate>0.3368)
m2.eval$cm
```

### Model Performance

We are getting slightly **better F1 Score (0.2312)** compare to previous model. We shall use this as the **baseline**.

```{r}
cat("Accuracy:  ", m2.eval$accuracy,
    "\nPrecision: ", m2.eval$precision,
    "\nRecall:    ", m2.eval$recall,
    "\nFScore:    ", m2.eval$fscore)
```


```{r echo=FALSE}
rm(list=c('m2.predict','m2.rocr'))
```

## Model 3 : Machine Learning Classification

We construct all the products that users had purchased in the last 3 orders, then use machine learning classification to predict will each of the product be purchased again. We shall use **decision tree and logistic regression** for this prediction.

```{r}
#m3.predict = orders %>% 
#  filter(eval_set=='prior') %>% 
#  left_join(order_products_prior) %>%
#  group_by(user_id, product_id) %>%
#  summarize(rate=n()/max(total_orders))
```


### Feature Engineering

#### User Features

We create three features applied to individual users.

**`users`**  
- `avg_user_order_dow`: Average of order_dow  
- `avg_user_order_hod`: Average of order_hour_of_day  
- `avg_user_items`: Average items for each order  

```{r message=FALSE}
users.train.avg_items = orders %>% 
  filter(eval_set=='train') %>%
  select(user_id) %>%
  left_join(orders) %>%
  left_join(order_products_prior) %>%
  count(user_id, order_id) %>%
  group_by(user_id) %>%
  summarize(avg_user_items = mean(n))

users.train.avg_dowhod = orders %>% 
  filter(eval_set=='train') %>%
  select(user_id) %>%
  left_join(orders) %>%
  group_by(user_id) %>%
  summarize( 
    avg_user_order_dow = mean(order_dow),
    avg_user_order_hod = mean(order_hour_of_day))

users.train = users.train.avg_items %>% left_join(users.train.avg_dowhod)
  
users.test.avg_items = orders %>% 
  filter(eval_set=='test') %>%
  select(user_id) %>%
  left_join(orders) %>%
  left_join(order_products_prior) %>%
  count(user_id, order_id) %>%
  group_by(user_id) %>%
  summarize(avg_user_items = mean(n))

users.test.avg_dowhod = orders %>% 
  filter(eval_set=='test') %>%
  select(user_id) %>%
  left_join(orders) %>%
  group_by(user_id) %>%
  summarize( 
    avg_user_order_dow = mean(order_dow),
    avg_user_order_hod = mean(order_hour_of_day))

users.test = users.test.avg_items %>% left_join(users.test.avg_dowhod)

rm(list=c('users.test.avg_items','users.test.avg_dowhod','users.train.avg_items','users.train.avg_dowhod'))
```


**Training Users**  

```{r}
head(users.train) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```

**Test Users**   

```{r}
head(users.test) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```

#### Product Features

We create two features generally applied across all products.  

**`products`**  

- `avg_product_order_dow`: Average of product order_dow
- `avg_product_order_hod`: Average of product order_hour_of_day  

```{r, message=FALSE}
products_ = orders %>% 
  left_join(order_products_prior) %>%
  group_by(product_id) %>%
  summarize( 
    avg_product_order_dow = mean(order_dow),
    avg_product_order_hod = mean(order_hour_of_day)
  ) %>%
  left_join(products)

head(products_) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```
### Construct Training Data with Label

```{r}
m3.train.label = train.label %>% 
  left_join(users.train) %>%   #combine with new user features
  left_join(products_)          #combine with new product features

head(m3.train.label) %>% kable %>% kable_styling(bootstrap_options = c("striped"))
```



## Selection of Model




## Data Recoding


## Prediction


# Analysis & Recommendations

# Citations

1. The Instacart Online Grocery Shopping Dataset 2017", Accessed from https://www.instacart.com/datasets/grocery-shopping-2017  
2. Data Dictionary: https://gist.github.com/jeremystan/c3b39d947d9b88b3ccff3147dbcf6c6b