3DA3 C02 Predictive Data Analytics
Assignment 1, Commerce 3DA3 C02 -
Predictive Data AnalyticsTo complete this assignment, please create a Jupyter notebook. The code in your jupyternotebook should provide answers to questions asked in the assignment. Please submitthe assignment by uploading the file(s) into the "Assignment 1" folder onAvenuetoLearn. You can find this folder under "Assessments>Assignments" on the course page.The deadline for submission is 11:59PM on Monday Oct. 21.
BackgroundIn the past decade, we witnessed the rise of online grocery shopping. With the
convenience of ordering groceries from the comfort of home, more people are turning todigital platforms for their everyday needs. This shift has been further fueled by factorssuch as busy lifestyles, the increasing use of mobile devices, and the covid-19pandemic, which underscored the importance of contactless shopping.For online grocery platforms, conducting data analysis on sales records is critical forunderstanding customer behavior, enhancing the overall shopping experience, and makedata-driven decisions that lead to higher customer satisfaction and profitability.
ata: We will make use of two datasets from the transaction records of an onlinegrocery delivery platform, stored in the files orders.csv (click to download) and
order_products.csv (click to download).The dataset in orders.csv includes the following columns:
order_id: This is the unique identifier of every customer ordercustomer_id: This is the unique identifier of every customer who placed the order
order_dow: This indicates the day of the week, on which the order took place. 0
stands for Sunday, 1-5 indiates Monday-Friday, and 6 indicates a Saturday.
order_hour_of_day: This indicates during which hour the order took place; for
example, 14 indicates that the order was placed between 14:00 and 14:59.
days_since_prior_order: This indicates how many days have passed since the
customer's last order
coupon_use: This shows if the customer used a coupon to (partially) pay for theorderThe dataset in order_products.csv records which products are purchased in an order. ItImagine that you are a data analyst at the grocery delivery platform. Based on thedatasets, please answer the following questions/tasks.
Questions 0.In the first cell of your Jupyter notebook, please create the following as markdown. Addyour first and last name, and your Student ID.includes the following columns:order_id: This is the order idenfitier (same as in order.csv).product_id: This is the identifier of a product that is purchased in the corresponding
orderquantity: This is the quantity of the product purchased in the corresponding order.unit_price: This is the unit price (in dollars) of the product purchased in thecorresponding ordercustomer_id: This is the identifier of the customer who purchased the product.Please note that order_id in order_products.csv does not need to be unique. If tworows in order_products.csv share the same order_id, it means that in the same order,the products in those two rows are both purchased.For example, suppose that the following row exists in order.csv:order_id customer_id order_dow order_hour_of_day days_since_prior_order coupon_useand the following two rows exist in order_products.csv:order_id product_id quantity unit price customer_idthen we know that in the same order (order_id O1234), 1 unit of product P0217 and 2units of product P0219 are purchased. And this order O1234 is the same order as theorder O1234 in order.csv.Important: For the remaining questions, please make sure to create a markdown cellbefore you answer each question and in it indicate the question number, e.g.,
Question 1, Question 2, etc.
For each question, you should use one or more code cells to present your codes. Pleasemake sure that you run each cell and display all the requested results. Please alsoensure that you will use markdown cells to provide necessary explanations of yourcodes and results.The Jupyter notebook should be a easy-to-read report that presents your analysis andresults. The grading will be based on both the correctness of your coding and thereadability of your notebook.
Question 1.
Import the two .csv files and assign them to a dataframe called df_orders anddf_order_products , respectively. Then,use a line of codes to review the first few rows of the dataframes. The result shouldbe clearly displayed 代 写3DA3 C02 Predictive Data Analytics in the notebook after you run the code cells.get the structures of the dataframes (number of rows, column types, etc.) using theinfo() function. Review the first few rows of the dataframe.In a markdown cell,explain the results returned by this function as comprehensive as youcan..
Question 2.For the DataFrame df_orders loaded from orders.csv, perform the following steps in
the given order.
- Find how many missing value each column contains.
- For any missing value in the column order_id , replace it with 'unknown_order'
- For any missing value in the column customer_id , replace it with
'unknown_customer'4. For any missing value in the column days_since_prior_order , replace it withthe mean value of the column
- After completing the above steps, repeat the codes in Step 1 to check again thenumber of missing values in each column
- For any remaining missing values, drop all rows containing a missing value
Question 3.
The grocery delivery platform is interested in assessing if offering coupons will increasecustomers' purchase frequency. To that end, let us again make use of the DataFramedf_orders (loaded from orders.csv) to perform the following tasks.
- Select all rows in df_orders where use of a coupon is yes , and assign thoserows as a new DataFrame named df_orders_coupon .
- Calculate the mean value of 'days_since_prior_order' in df_orders_coupon .
- Select all rows in df_orders where use of a coupon is no , and assign those rowsas a new DataFrame named df_orders_no_coupon .
- Calculate the mean value of 'days_since_prior_order' in df_orders_no_coupon .Based on your findings of the above steps, answer the following question in amarkdown cell:Is the use of coupon associated with higher/lower order frequency?Please briefly explain your answer in the markdown cell.
Questions 4.The platform is also interested in measuring the total number of orders received on each
day of the week. To do this, they would like you to complete the following tasks.Divide the order id's in the 'order_id' column of the DataFrame df_orders (loadedfromorders.csv) into groups, based on the day of the week ('order_dow') when theorder is placed. The result should be a Groupby object.Construct and display the content of a pandas Series, which should show the totalnumber of orders for each day of the week.
Question 5.As observed, each row of the data in order_products.csv is the sales information of a
product in a certain order. The information includes the per-unit price and number ofunits ordered, but it does not directly provide the revenue.Let us now create anewcolumn named 'revenue' in the DataFramedf_order_products constructed from order_products.csv. For each row, thecolumn should contain the corresponding revenue, calcuated as 'quantity' 'unit price'.
See the following two-row example for a demonstration.order_id product_id quantity unit_price customer_id revenue
C621739.98After you have added the new column, further complete the following tasks:Display the first few rows of the updated df_order_products DataFrame.Calculate the total revenue by summing up revenues in each row.
Question 6
From time to time, there will be customers who would like to review their purchaserecord. To do that, they will need to supply their customer id.Suppose a customer with the id '0421MWMT' just contacted Customer Service andwould like to see all their purchases. Perform the following tasks for the customer.Select all rows related to this customer's purchases in the DataFramedf_order_products (loaded from order_products.csv), and assign them to anew DataFrame named 'df_cust_inquiry'. Display the content of this DataFrame.Calculate the customer's total purchase in dollar amount.×
