Column Value Description customer_id string ID of the customer - super duper hashed days_since_first_order integer Days since the first order was made days_since_last_order integer Days since the last...

Have attached the assignment below along with data set link in the assignment and pdf explaining data columns


Column Value Description customer_id string ID of the customer - super duper hashed days_since_first_order integer Days since the first order was made days_since_last_order integer Days since the last order was made is_newsletter_subscriber string Flag for a newsletter subscriber orders integer Number of orders items integer Number of items cancels integer Number of cancellations - when the order is cancelled after being placed returns integer Number of returned orders different_addresses integer Number of times a different billing and shipping address was used shipping_addresses integer Number of different shipping addresses used devices integer Number of unique devices used vouchers integer Number of times a voucher was applied cc_payments integer Number of times a credit card was used for payment paypal_payments integer Number of times PayPal was used for payment afterpay_payments integer Number of times AfterPay was used for payment apple_payments integer Number of times Apple Pay was used for payment female_items integer Number of female items purchased male_items integer Number of male items purchased unisex_items integer Number of unisex items purchased wapp_items integer Number of Women Apparel items purchased wftw_items integer Number of Women Footwear items purchased mapp_items integer Number of Men Apparel items purchased wacc_items integer Number of Women Accessories items purchased macc_items integer Number of Men Accessories items purchased mftw_items integer Number of Men Footwear items purchased wspt_items integer Number of Women Sport items purchased mspt_items integer Number of Men Sport items purchased curvy_items integer Number of Curvy items purchased sacc_items integer Number of Sport Accessories items purchased msite_orders integer Number of Mobile Site orders desktop_orders integer Number of Desktop orders android_orders integer Number of Android app orders ios_orders integer Number of iOS app orders other_device_orders integer Number of Other device orders work_orders integer Number of orders shipped to work home_orders integer Number of orders shipped to home parcelpoint_orders integer Number of orders shipped to a parcelpoint other_collection_ordersinteger Number of orders shipped to other collection points average_discount_onoffer float Average discount rate of items typically purchased average_discount_used float Average discount finally used on top of existing discount revenue float $ Dollar spent overall per person What to Submit? Task2.ipynbThe completed notebook (one for each group) with all the run-able code on all requirements. In general, you need to complete, save the results of running, download and submit your notebook from Python platform such as Google Colab. You need to clearly list the answer for each question, with sufficient coding comments, and the expected format from your notebook will be like in Figure 1. Task2Report.pdf You (group) are also required to put your answer (code) and running results from SIT742Task2.ipynb into a pdf as the report for your task2 assignment (copy the code and paste into the report, the code format such as Indentation should be same in the ipynb notebook). In this report (one for each group), you will need to include the questions for the assignment for both Part 1 and Part 2. Also you will need to provide a clear explanation on your logic for solving each question. In the explanation, you will need to cover below parts: 1). why you decide to choose your solution; 2). are there any other solutions that could solve the question; 3). whether your solution is the optimal or not? why? The length of the explanation part for each question is limited below 100 words. Link to data- https://raw.githubusercontent.com/tuliplab/sit742/develop/Assessment/2022/data/assignment2data.json Question 1 Open the assignment2data.json file and convert it to csv format as dataframe in pandas. Removing the duplicated rows from dataframe and save as the new dataframe. The meaning of the column is in assignment2data.pdf Create some new features for the dataframe by using below code: df [ ’ female_item_rate ’ ] = df [ ’ female_items ’ ] / df [ ’ items ’ ] df [ ’ male_item_rate ’ ] = df [ ’ male_items ’ ] / df [ ’ items ’ ] df [ ’ unisex_items_rate ’ ] = df [ ’ unisex_items ’ ] / df [ ’ items ’ ] • Write a code find out how many rows (customers) could have the value female_item_rate == 1 and the value male_item_rate == 1 and the value orders > 4:11 Question 2 Open the assignment2data.json file and convert it to csv format as dataframe in pandas. Removing the duplicated rows from dataframe and save as the new dataframe. The meaning of the column is in assignment2data.pdf In this question, you will use the original format of the data to group data on the value of column is_newsletter_subscriber to show the average order value, the max order value, the median order value. Question 3 Transaction Data Analysis In this part, we will do the analysis on the customer transaction data. The data is from customer transaction.(link to data set- https://github.com/tulip-lab/sit742/blob/develop/Assessment/2022/data/customer_transaction.csv) The row of the data represents the item transaction from customer (one item from a transaction for that customer). The product is represented as the product_id and the commodity. There is also a column basket_id to help group the transaction together into basket level (check out basket). Question 3.1 You will need to group the customer_id and basket_id to find out the product commodity in each basket. Then you will need to answer: • How many transactions based on basket level? what is the average basket size? • What is the most popular product commodity (based on the frequency of the purchase)? • What is the average of the total transaction price (average basket total price) for each customer? • You will need to transform the data into a format of: the row represent the basket, the column will be all product commodity, the value of the column should indicate whether the basket contains particular product commodity. Name this new dataframe as transaction_product • You will need to transform the data into a format of: the row represent the unique customer, the column will be all product commodity, the value of the column should be the frequency of the purchase on the particular commodity cross entire data. Name this new dataframe as customer_product_freq • Using the customer_product_freq to find the top 5 similar customers for each customer. (Check out the KNN) Question 3.2 Using the dataframe transaction_product to conduct association rule analysis (you are recommended to use mlxtend package). You will need to find out: • The itemsets(basket) having length more than 1 and minimum support of 5% • The association rules with minimum support of 2% and having lift more than 1. The definition of the support and lift is in M05E, lecture slides and also Association rule learning.