Project Foundations for Data Science: FoodHub Data Analysis

Marks: 60

Context

The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.

The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.

Objective

The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.

Data Description

The data contains the different data related to a food order. The detailed data dictionary is given below.

Data Dictionary

• order_id: Unique ID of the order
• customer_id: ID of the customer who ordered the food
• restaurant_name: Name of the restaurant
• cuisine_type: Cuisine ordered by the customer
• cost: Cost of the order
• day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)
• rating: Rating given by the customer out of 5
• food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.
• delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information

Let us start by importing the required libraries

# import libraries for data manipulation
import numpy as np
import pandas as pd

# import libraries for data visualization
import matplotlib.pyplot as plt
import seaborn as sns

import warnings

# Filter out DeprecationWarning
warnings.filterwarnings("ignore", category=DeprecationWarning)

Understanding the structure of the data

# read the data
df = pd.read_csv('foodhub_order.csv')
# returns the first 5 rows
df.head()

	order_id	customer_id	restaurant_name	cuisine_type	cost_of_the_order	day_of_the_week	rating	food_preparation_time	delivery_time
0	1477147	337525	Hangawi	Korean	30.75	Weekend	Not given	25	20
1	1477685	358141	Blue Ribbon Sushi Izakaya	Japanese	12.08	Weekend	Not given	25	23
2	1477070	66393	Cafe Habana	Mexican	12.23	Weekday	5	23	28
3	1477334	106968	Blue Ribbon Fried Chicken	American	29.20	Weekend	3	25	15
4	1478249	76942	Dirty Bird to Go	American	11.59	Weekday	4	25	24

Observations:

The DataFrame has 9 columns as mentioned in the Data Dictionary. Data in each row corresponds to the order placed by a customer.

Question 1: How many rows and columns are present in the data? [0.5 mark]

# use the shape method to determine the the number of rows and columns of the data
df.shape

(1898, 9)

Observations:

• There are 1898 rows and 9 columns in the DataFrame.
• This corresponds to a possible 1898 orders.

Question 2: What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]

# Use info() to print a concise summary of the DataFrame
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1898 entries, 0 to 1897
Data columns (total 9 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   order_id               1898 non-null   int64  
 1   customer_id            1898 non-null   int64  
 2   restaurant_name        1898 non-null   object 
 3   cuisine_type           1898 non-null   object 
 4   cost_of_the_order      1898 non-null   float64
 5   day_of_the_week        1898 non-null   object 
 6   rating                 1898 non-null   object 
 7   food_preparation_time  1898 non-null   int64  
 8   delivery_time          1898 non-null   int64  
dtypes: float64(1), int64(4), object(4)
memory usage: 133.6+ KB

Observations:

• There are 4 features (columns) which are int64
• There is 1 feature (column), cost_of_the_order, which is float64
• There are 3 features (columns) which are objects, however, the feature 'rating' should be a int64 or float64, so this should be investigated further.
• Some 'ratings' were not given, therefore the data type is labeled an object.
• The ratings which were not given either have to be discarded or the average of the rating for that cuisine or restaurant should be given.

Question 3: Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]

# determine if there are missing values
print('Missing Values')
df.isna().sum()

Missing Values

order_id                 0
customer_id              0
restaurant_name          0
cuisine_type             0
cost_of_the_order        0
day_of_the_week          0
rating                   0
food_preparation_time    0
delivery_time            0
dtype: int64

Observations:

• There are no missing vaules

Question 4: Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed? [2 marks]

# use the describe method to determine the statistical summary of the data
df.describe()

	order_id	customer_id	cost_of_the_order	food_preparation_time	delivery_time
count	1.898000e+03	1898.000000	1898.000000	1898.000000	1898.000000
mean	1.477496e+06	171168.478398	16.498851	27.371970	24.161749
std	5.480497e+02	113698.139743	7.483812	4.632481	4.972637
min	1.476547e+06	1311.000000	4.470000	20.000000	15.000000
25%	1.477021e+06	77787.750000	12.080000	23.000000	20.000000
50%	1.477496e+06	128600.000000	14.140000	27.000000	25.000000
75%	1.477970e+06	270525.000000	22.297500	31.000000	28.000000
max	1.478444e+06	405334.000000	35.410000	35.000000	33.000000

Observations:

• The statistical summary was completed for all of the quantitative variables
• The order_id and the customer_id are not relevant for the statistical summary as they are identifiers
• The food_preparation_time statistical information:
• average: 27.371970 minutes
• minimum: 20 minutes
• maximum: 35 minutes

## how many unique restaurant_names, cuisine_types and looking at other qualitative variables
df.describe(include = 'all')

	order_id	customer_id	restaurant_name	cuisine_type	cost_of_the_order	day_of_the_week	rating	food_preparation_time	delivery_time
count	1.898000e+03	1898.000000	1898	1898	1898.000000	1898	1898	1898.000000	1898.000000
unique	NaN	NaN	178	14	NaN	2	4	NaN	NaN
top	NaN	NaN	Shake Shack	American	NaN	Weekend	Not given	NaN	NaN
freq	NaN	NaN	219	584	NaN	1351	736	NaN	NaN
mean	1.477496e+06	171168.478398	NaN	NaN	16.498851	NaN	NaN	27.371970	24.161749
std	5.480497e+02	113698.139743	NaN	NaN	7.483812	NaN	NaN	4.632481	4.972637
min	1.476547e+06	1311.000000	NaN	NaN	4.470000	NaN	NaN	20.000000	15.000000
25%	1.477021e+06	77787.750000	NaN	NaN	12.080000	NaN	NaN	23.000000	20.000000
50%	1.477496e+06	128600.000000	NaN	NaN	14.140000	NaN	NaN	27.000000	25.000000
75%	1.477970e+06	270525.000000	NaN	NaN	22.297500	NaN	NaN	31.000000	28.000000
max	1.478444e+06	405334.000000	NaN	NaN	35.410000	NaN	NaN	35.000000	33.000000

Observations:

• There are 178 unique restaurant_names, with Shake Shack (219 orders) being the top restaurant_name
• There are 14 unique cuisine_types, with American (584 orders) being the top cuisine_type
• The most orders are made on the Weekend with 1351 orders
• The rating should be an integer, however because 736 orders have a rating of 'Not given' it is an object. And it shows that the majority of customers do not leave a rating.

Question 5: How many orders are not rated? [1 mark]

# determine the number of orders which are not rated
df.groupby('rating').count()

	order_id	customer_id	restaurant_name	cuisine_type	cost_of_the_order	day_of_the_week	food_preparation_time	delivery_time
rating
3	188	188	188	188	188	188	188	188
4	386	386	386	386	386	386	386	386
5	588	588	588	588	588	588	588	588
Not given	736	736	736	736	736	736	736	736

Observations:

• There are 736 orders which are not rated.

Total_orders = df["cost_of_the_order"].sum()
print('The sum of the orders is: $',  Total_orders)

The sum of the orders is: $ 31314.82

order_rev_by_cuisine = df.groupby('cuisine_type').sum('cost_of_the_order')
print(order_rev_by_cuisine)

                 order_id  customer_id  cost_of_the_order  \
cuisine_type                                                
American        862853485    107555095            9530.78   
Chinese         317668929     38803384            3505.62   
French           26593888      2846497             356.29   
Indian          107857010     11833767            1235.14   
Italian         440303276     47551355            4892.77   
Japanese        694417840     75902159            7663.13   
Korean           19206541      2947126             182.02   
Mediterranean    67964240      5331774             711.84   
Mexican         113763379     13971923            1303.85   
Middle Eastern   72393190      8273157             922.21   
Southern         25118057      2385782             328.11   
Spanish          17729967      2164002             227.93   
Thai             28074933      3853678             364.95   
Vietnamese       10341724      1458073              90.18   

                food_preparation_time  delivery_time  
cuisine_type                                          
American                        16025          14129  
Chinese                          5915           5129  
French                            484            456  
Indian                           1979           1758  
Italian                          8190           7321  
Japanese                        12930          11342  
Korean                            331            272  
Mediterranean                    1242           1085  
Mexican                          2058           1878  
Middle Eastern                   1307           1180  
Southern                          469            405  
Spanish                           323            281  
Thai                              519            440  
Vietnamese                        180            183  

plt.figure(figsize = (25,5))
sns.barplot(order_rev_by_cuisine.cost_of_the_order, order = order_rev_by_cuisine.sum(), index(cuisine_type))
plt.title('Revenue by Cuisine in USD $')
plt.xticks(rotation = 90)
plt.show()

  Input In [26]
    sns.barplot(order_rev_by_cuisine.cost_of_the_order, order = order_rev_by_cuisine.sum(), index(cuisine_type))
                                                                                                               ^
SyntaxError: positional argument follows keyword argument

Exploratory Data Analysis (EDA)

Univariate Analysis

Question 6: Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]

# Write the code here
#Countplots for the qualitative variables
# displaying the 'restaurant_name' using a countplot
plt.figure(figsize = (25, 5))
sns.countplot(df.restaurant_name, order = df.restaurant_name.value_counts().index)

#add a title to the countplot
plt.title('Countplot of Restaurants by Name')

#rotate the xtick labels for readability
plt.xticks(rotation = 90)

# displaying the 'cuisine_type' using a countplot
plt.figure(figsize = (25, 5))
sns.countplot(df.cuisine_type, order = df.cuisine_type.value_counts().index)

#add a title to the countplot
plt.title('Countplot of cuisine_type')

#rotate the xtick labels for readability
plt.xticks(rotation = 45)

# displaying the 'day_of_the_week' using a countplot
plt.figure(figsize = (25, 5))
sns.countplot(df.day_of_the_week, order = df.day_of_the_week.value_counts().index)

#add a title to the countplot
plt.title('Countplot of day_of_the_week')

#set of boxplots of the quantitative variables
fig, axs = plt.subplots(1, 3)
#Boxplot for the cost_of_the_order
axs[0].boxplot(df['cost_of_the_order'])
axs[0].set_title('cost_of_the_order')

#Boxplot for the food_preparation_time
axs[1].boxplot(df['food_preparation_time'])
axs[1].set_title('food_preparation_time')

#Boxplot for the delivery_time
axs[2].boxplot(df['delivery_time'])
axs[2].set_title('delivery_time')

#adjust spacing for readability
fig.subplots_adjust(left=0.08, right=2, bottom=0.05, top=2,
                    hspace=0.5, wspace=0.5)

# set of distribution plots for the quantitative variables
# distribution plot for the cost_of_the_order
plt.figure(figsize = (15, 5))
sns.displot(df.cost_of_the_order, kde=True)
plt.title('Distribution of cost_of_the_order')
plt.xlabel('cost_of_the_order')
plt.ylabel('count')
          
# distribution plot for the food_preparation_time
plt.figure(figsize = (15, 5))
sns.displot(df.food_preparation_time, kde=True)
plt.title('Distribution of food_preparation_time')
plt.xlabel('food_preparation_time')
plt.ylabel('count')
          
# distribution plot for the delivery_time
plt.figure(figsize = (15, 5))
sns.displot(df.delivery_time, kde=True)
plt.title('Distribution of delivery_time')
plt.xlabel('delivery_time')
plt.ylabel('count')

#display plot
plt.show()

/opt/conda/envs/anaconda-2022.05-py39/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
/opt/conda/envs/anaconda-2022.05-py39/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
/opt/conda/envs/anaconda-2022.05-py39/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
/opt/conda/envs/anaconda-2022.05-py39/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 142 (\x8e) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)
/opt/conda/envs/anaconda-2022.05-py39/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 140 (\x8c) missing from current font.
  fig.canvas.print_figure(bytes_io, **kw)

<Figure size 1080x360 with 0 Axes>

<Figure size 1080x360 with 0 Axes>

<Figure size 1080x360 with 0 Axes>

Question 7: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]

# Write the code here
#list the top 5 restaurants by the number of orders received
num_orders_per_restaurant = df.groupby('restaurant_name').count()
top_5_restaurants = num_orders_per_restaurant.sort_values(by = ['order_id'], ascending = False).head(5)
top_5_restaurants

	order_id	customer_id	cuisine_type	cost_of_the_order	day_of_the_week	rating	food_preparation_time	delivery_time
restaurant_name
Shake Shack	219	219	219	219	219	219	219	219
The Meatball Shop	132	132	132	132	132	132	132	132
Blue Ribbon Sushi	119	119	119	119	119	119	119	119
Blue Ribbon Fried Chicken	96	96	96	96	96	96	96	96
Parm	68	68	68	68	68	68	68	68

Observations:

• The top 5 restaurants, ranked by the number of orders received are:
• Shake Shack - 219 orders
• The Meatball Shop - 132 orders
• Blue Ribbon Sushi - 119 orders
• Blue Ribbon Fried Chicken - 96 orders
• Parm - 68 orders

Question 8: Which is the most popular cuisine on weekends? [1 mark]

# Write the code here
# find the most popular cuisines on the weekends
cuisine_by_day_of_week = pd.crosstab(df['cuisine_type'],df['day_of_the_week'])
popular_cuisine_weekend = cuisine_by_day_of_week.sort_values(by = ['Weekend'] , ascending = False)
popular_cuisine_weekend

day_of_the_week	Weekday	Weekend
cuisine_type
American	169	415
Japanese	135	335
Italian	91	207
Chinese	52	163
Mexican	24	53
Indian	24	49
Mediterranean	14	32
Middle Eastern	17	32
Thai	4	15
French	5	13
Korean	2	11
Southern	6	11
Spanish	1	11
Vietnamese	3	4

Observations:

• The most popular, top 5, cuisines on the weekend are:
• American - 415 orders
• Japanese - 335 orders
• Italian - 207 orders
• Chinese - 163 orders
• Mexican - 53 orders

Question 9: What percentage of the orders cost more than 20 dollars? [2 marks]

# Write the code here
#find the number of orders greater than 20usd
greater_than_20usd = df['cost_of_the_order'] > 20
num_greater_than_20usd = greater_than_20usd.sum()

# to find the percentage of orders greater than 20usd, you will take the (number of orders greater than 20usd / total number of orders)*100
total_orders = df['cost_of_the_order'].count()

percent_orders_greater_20usd = (num_greater_than_20usd/total_orders)*100

print('The number of orders greater than 20usd is:', num_greater_than_20usd)
print('The number of total number of orders:', total_orders)
print('The percent of orders greater than 20usd is:', percent_orders_greater_20usd)

The number of orders greater than 20usd is: 555
The number of total number of orders: 1898
The percent of orders greater than 20usd is: 29.24130663856691

Observations:

• The percent of orders that cost more than 20 USD is 29.24%

Question 10: What is the mean order delivery time? [1 mark]

# Write the code here
# find the mean order delivery time
mean_delivery_time = df['delivery_time'].mean()

print ('The mean order delivery time is:', mean_delivery_time)

The mean order delivery time is: 24.161749209694417

Observations:

• The mean order delivery time is approximately 24 minutes

Question 11: The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed. [1 mark]

# Write the code here
#list the top 3 most frequent customers
freq_customers = df.groupby('customer_id').count()
top_3_customers = freq_customers.sort_values(by = ['order_id'], ascending = False).head(3)
top_3_customers

	order_id	restaurant_name	cuisine_type	cost_of_the_order	day_of_the_week	rating	food_preparation_time	delivery_time
customer_id
52832	13	13	13	13	13	13	13	13
47440	10	10	10	10	10	10	10	10
83287	9	9	9	9	9	9	9	9

Observations:

• The three most frequent customers are:

| Customer_id | number of orders |

| 52832 | 13 |
| 47440 | 10 |
| 83287 | 9 |

Multivariate Analysis

Question 12: Perform a multivariate analysis to explore relationships between the important variables in the dataset. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]

# Write the code here
# create a pair plot to see if there are any relationships 
sns.pairplot(df)


# identify relationships between the cuisine type and the cost of the order
df.hist(by='cuisine_type',column = 'cost_of_the_order', figsize=(20,30))

# identify relationships between the cuisine type and the day_of_the_week
df.hist(by='cuisine_type',column = 'day_of_the_week', figsize=(20,30))

# identify relationships between the cuisine type and the rating
df.hist(by='cuisine_type',column = 'rating', figsize=(20,30))

# identify relationships between the cuisine type and the food_preparation_time
df.hist(by='cuisine_type',column = 'food_preparation_time', figsize=(20,30))

# identify relationships between the cuisine type and the delivery_time
df.hist(by='cuisine_type',column = 'delivery_time', figsize=(20,30))

# identify relationships between the cost_of_the_order and the day_of_the_week
df.hist(by='day_of_the_week',column = 'cost_of_the_order', figsize=(10,15))

# identify relationships between the food_preparation_time and the day_of_the_week
df.hist(by='day_of_the_week',column = 'food_preparation_time', figsize=(10,15))

# identify relationships between the food_preparation_time and the day_of_the_week
df.hist(by='day_of_the_week',column = 'delivery_time', figsize=(10,15))

# identify relationships between the cost_of_the_order and the rating
df.hist(by='rating',column = 'cost_of_the_order', figsize=(10,15))



# find the correlations between the features
corr = df.corr()
corr

# create the heatmap of the correlation between the features
sns.heatmap(corr, annot=True)

plt.show()

Question 13: The company wants to provide a promotional offer in the advertisement of the restaurants. The condition to get the offer is that the restaurants must have a rating count of more than 50 and the average rating should be greater than 4. Find the restaurants fulfilling the criteria to get the promotional offer. [3 marks]

# Write the code here
# create a dataframe with the specific ratings for each restaurant 
all_ratings_by_restaurant = pd.crosstab(df['restaurant_name'],df['rating'] )

# need to drop the 'not given' responses in order to sum the total ratings for each restaurant
ratings_by_restaurant = all_ratings_by_restaurant.drop('Not given', axis=1)

# find the total number of reveiws each restaurant received
total_reviews = ratings_by_restaurant['total_reviews'] = ratings_by_restaurant.sum(axis=1)

# find the average rating for each restaurant
column = list(ratings_by_restaurant.columns)
temp_lst = ['3', '4', '5']

for column in temp_lst:
    ratings_by_restaurant['average_rating'] = (ratings_by_restaurant['3'] * 3 + ratings_by_restaurant['4'] * 4 + ratings_by_restaurant['5'] * 5)/ratings_by_restaurant['total_reviews']
    
# sort the total_reviews in descending order
ratings_by_restaurant.sort_values(by = ['total_reviews'], ascending = False)

temp_lst2 = ['total_reviews', 'average_rating']

# use a loop to go through all of the objects to determine if the restaurant has 50 or more reviews and an average of greater than 4.0
for column in temp_lst2:
    ratings_by_restaurant['More_50'] = ratings_by_restaurant['total_reviews'] >= 50
    ratings_by_restaurant['More_4'] = ratings_by_restaurant['average_rating'] >= 4

# determine which restaurants meet the criteria 
promotion_restaurants = ratings_by_restaurant[(ratings_by_restaurant['More_50'] == True) & (ratings_by_restaurant['More_4'] == True)]

# display the restaurants which meet the criteria
promotion_restaurants

rating	3	4	5	total_reviews	average_rating	More_50	More_4
restaurant_name
Blue Ribbon Fried Chicken	11	21	32	64	4.328125	True	True
Blue Ribbon Sushi	16	25	32	73	4.219178	True	True
Shake Shack	23	50	60	133	4.278195	True	True
The Meatball Shop	10	21	53	84	4.511905	True	True

ratings_by_restaurant.sort_values(by = ['total_reviews'], ascending = False)

rating	3	4	5	total_reviews	average_rating	More_50	More_4
restaurant_name
Shake Shack	23	50	60	133	4.278195	True	True
The Meatball Shop	10	21	53	84	4.511905	True	True
Blue Ribbon Sushi	16	25	32	73	4.219178	True	True
Blue Ribbon Fried Chicken	11	21	32	64	4.328125	True	True
RedFarm Broadway	8	15	18	41	4.243902	False	True
...	...	...	...	...	...	...	...
Market Table	0	0	0	0	NaN	False	False
Paul & Jimmy's	0	0	0	0	NaN	False	False
Room Service	0	0	0	0	NaN	False	False
Rohm Thai	0	0	0	0	NaN	False	False
Lamarca Pasta	0	0	0	0	NaN	False	False

178 rows × 7 columns

Observations:

• There are only 4 restaurants that meet the criteria for the promotional offer.
• Blue Ribbon Fried Chicken
• Blue Ribbon Sushi
• Shake Shack
• The Meatball Shop

Question 14: The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders. [3 marks]

# Write the code here
# find the orders that are over 20 usd and over 15 usd
# create variables for the 15 and 25%
charge_more_than_5 = 0.15
addl_charge_more_than_20 = 0.10

temp_lst3 = ['cost_of_the_order']

# identify which orders cost more than 5 usd
#greater_than_5usd = df['cost_of_the_order'] > 5

# use a loop to go through all of the objects to determine if the cost of the order is more than 5 or 20 USD.
for column in temp_lst3:
    df['greater_than_5usd'] = df['cost_of_the_order'] > 5
    df['greater_than_20usd'] = df['cost_of_the_order'] > 20

# determine the net revenue generated by the company across all orders 


df['charge'] = np.where ((df['greater_than_5usd'] == 'True') & (df['greater_than_20usd'] == 'True'), df['cost_of_the_order'] * charge_more_than_5, df['cost_of_the_order'] * (charge_more_than_5 + addl_charge_more_than_20))

net_revenue = df['charge'].sum()


print('The net revenue generated by the company across all orders is:', net_revenue)

The net revenue generated by the company across all orders is: 7828.705

Observations:

• The total net revenue by the company across all orders is: 7828.71

Question 15: The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed? (The food has to be prepared and then delivered.) [2 marks]

# Write the code here
# find the number of orders which require more than 60 minutes to deliver the food

delivery_time_from_order = df['food_preparation_time'] + df['delivery_time']
delivery_time_from_order.describe()

df['over_60min'] = (delivery_time_from_order > 60).astype(int)

df['over_60min'].sum()

percent_delivery_over_60 = (df['over_60min'].sum() / df['order_id'].count()) * 100

print('The percent of orders that take more than 60 minutes to get delivered from the time the order is placed is:', percent_delivery_over_60)

The percent of orders that take more than 60 minutes to get delivered from the time the order is placed is: 10.537407797681771

Observations:

• About 10.5% of the orders take over 60 minutes to get delivered from the time the order is placed

Question 16: The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]

# Write the code here
# find the mean delivery time grouped by the day_of_the_week 
by_day_of_the_week = df.groupby('day_of_the_week').mean()

mean_delivery_by_day_of_the_week = by_day_of_the_week['delivery_time']
#delivery_time_by_day_of_week = pd.crosstab(df.groupby('day_of_the_week'), df['delivery_time'])

#delivery_time_by_day_of_week = pd.crosstab(df['day_of_the_week'], df['delivery_time'])

by_day_of_the_week 
mean_delivery_by_day_of_the_week

day_of_the_week
Weekday    28.340037
Weekend    22.470022
Name: delivery_time, dtype: float64

Observations:

• The weekend has a shorter delivery time.

Conclusion and Recommendations

Question 17: What are your conclusions from the analysis? What recommendations would you like to share to help improve the business? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]

Conclusions:

There were 1898 orders analyzed.

There are 178 unique restaurants. Shake Shack was the restaurant with the most orders, 219.

There are 14 unique cuisine types. American was the most ordered cuisine with 584 orders.

Costs of the Orders

The costs of the orders range from 4.47 USD to 35.41 USD with the mean cost being $16.50. The cost of the food has a positively skewed distribution. Only approximately 29.24% (555 orders) of the orders cost over 20 USD.

The days of the week were separated into two variables, weekday and weekend. 71% (1351/1898) of the orders occur on the weekend.

Reviews

Food hub asks customers to review the food and 4 unique reviews were offered: 3, 4, 5, Not given. 736 (38.7%) of the customers chose not to review their received order.
This was the most common response.

• rating 3 - 188
• rating 4 - 386
• rating 5 - 588
• Not Given - 736

Food Preparation and Delivery

The food_preparation_time ranged 20 minutes to 35 minutes with the mean being 27.37 minutes. The food preparation time was uniformly distributed. The food delivery time ranged from 15 minutes to 33 minutes with the mean being 24.16 minutes. The delivery time was negatively skewed. The mean deliver time

• Weekday 28.340037
• Weekend 22.470022

The summary statistics for the delivery time for orders including food preparation are:

• count 1898.000000
• mean 51.533720
• std 6.833603
• min 35.000000
• 25% 47.000000
• 50% 52.000000
• 75% 56.000000
• max 68.000000

Only '10%' of the orders placed take over 60 minutes for delivery, from the time it was placed.

Relationships

There are no correlations identified between the quantitative variables.

The Cuisine and x

The relationship shown by: The cuisine and cost of the order is positively skewed for all except Southern The cuisine and the day of the week shows the weekend is the most popular for all The cuisine and the ratings given shows Not Given was the highest received for 8 cuisines, however for Indian, 5 was the highest, Korean was a equal between 4 and Not given, Mediterranean has more 5, Middle Eastern and Southern has 4 as the most given review and Vietnamese had a tie between 3, 5 and Not given.

For the restaurants which received reviews the following four received the most reviews

• Shake Shack - 133 reviews average rating - 4.28
• The Meatball Shop - 84 reviews, average rating - 4.51
• Blue Ribbon Fried Chicken - 73 reviews, average rating - 4.32
• Blue Ribbon Sushi - 64 reviews, average rating - 4.22

The top 5 restaurants ordered from are:

• Shake Shack - 219 orders
• The Meatball Shop - 132 orders
• Blue Ribbon Sushi - 119 orders
• Blue Ribbon Fried Chicken - 96 orders
• Parm - 68 orders

The most popular, top 5, cuisines on the weekend are:

• American - 415 orders
• Japanese - 335 orders
• Italian - 207 orders
• Chinese - 163 orders
• Mexican - 53 orders

Net Revenue

Almost all orders are charged a fee: '15%' for orders over '5 USD' and '25%' for orders over '20 USD'. The company received a net revenue of $7828.71 across all orders.

Recommendations:

• As the mean averages are 16.50 USD, look at how the charges are broken down.
  It may be benficial to add a 20% for orders that over $10 USD.

• To gain more reviews from the customers add a 5 USD off coupon on their next order for them leaving a review. This would increase customer engagement and encourage return business.

• The food prep and delivery had reasonable time frames with 75% taking 56 minutes or less.
  Foodhub could have delivery drivers closer to the restaurants which receive the most orders.

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