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FIT5196 assessment
创建日期：2024-08-03 16:49:10
标签： FIT5196
assessment
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FIT5196 assessment 

This is an individual assessment and worth 35% of your total mark for

FIT5196.

Data Cleansing (%60)

For this assessment, you are required to write Python (Python 2/3) code to analyze your dataset,

find and fix the problems in the data. The input and output of this task are shown below:

Table 1. The input and output of the task

Input Output Output Notebook

<student_id>_dirty_data.csv

<student_id>_outlier_data.csv

<student_id>_missing_data.csv

<student_id>_dirty_data_solution.csv

<student_id>_outlier_data_solution.csv

<student_id>_missing_data_solution.csv

<student_id>_ass2.ipynb

<student_id>_ass2.pdf

Note1: You should submit a zip file and a pdf file which will be used for plagiarism check.

1. The csv files and the ipynb file must be zipped into a file named

<student_id>_ass2.zip.

2. The pdf file should be exported from the <student_id>_ass2.ipynb without

any cell output (please only keep the markdown notes and scripts in the

pdf file). The pdf file is named <student_id>_ass2.pdf.

Note2: <student_id> is to be replaced with your student id

Note3: Students can find their three input files here based on their student_id

Note 4: An interview is required for this assessment. You will need to explain your

solution and answer questions from a teaching team member.

Exploring and understanding the data is one of the most important parts of the data wrangling

process. You are required to perform graphical and/or non-graphical EDA methods to understand

the data first and then find and fix the data problems. You are required to:

● Detect and fix errors in <student_id>_dirty_data.csv

● Detect and remove outlier rows in <student_id>_outlier_data.csv

(outliers are to be found w.r.t. delivery_charges attribute only)

● Impute the missing values in <student_id>_missing_data.csv

As a starting point, here is what we know about the dataset in hand:

The dataset contains transactional retail data from an online electronics store (DigiCO) located in

Melbourne, Australia . The store operation is exclusively online, and it has three warehouses 1

around Melbourne from which goods are delivered to customers.

Each instance of the data represents a single order from said store. The description of each data

column is shown in Table 2.

Table 2. Description of the columns

COLUMN DESCRIPTION

order_id A unique id for each order

customer_id A unique id for each customer

date The date the order was made, given in YYYY-MM-DD format

nearest_warehouse A string denoting the name of the nearest warehouse to the

customer

shopping_cart A list of tuples representing the order items: first element of

the tuple is the item ordered, and the second element is the

quantity ordered for such item.

order_price A float denoting the order price in AUD. The order price is the

price of items before any discounts and/or delivery charges

are applied.

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