代写FIT5196-S2-2024 Assessment 2调试Python程序

FIT5196-S2-2024 Assessment 2

This is a group assessment and is worth 40% of your total mark for FIT5196.

Due date: Friday 18 October 2024, 11:55pm

Task 1. Data Cleansing (50%)

For this assessment, you are required to write Python code to analyse 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 task 1

Input files	Submission
	Output files	Other Deliverables
Group<group_id>_dirty_data	Group<group_id>_dirty_data_sol	Group<group_id>_ass2_task
.csv	ution.csv	1.ipynb
Group<group_id>_outlier_da	Group<group_id>_outlier_data_s	Group<group_id>_ass2_task
ta.csv	olution.csv	1.py
Group<group_id>_missing_d	Group<group_id>_missing_data_
ata.csv	solution.csv
warehouse.csv

Note1: All files must be zipped into a file named Group<group_id>_ass2.zip (please use zip not rar, 7z, tar, etc.)

Note2: Replace <group_id> with your group id (do not include <>)

Note3: You can find your three input files from the folder with your group numberhere. Using the wrong files will result in zero marks.

Note4:  Please strictly follow the instructions in the appendix to generate the .ipynb and .py files.

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 the data problems. In this assessment, you have been provided with three  data  inputs  along  with  the  additional  file:  warehouse.csv  here.  Due  to  an  unexpected scenario, a portion of the data is missing or contains anomalous values. Thus, before moving to the nextstep in data analysis, you are required to perform. the following tasks:

1.   Detect and fix errors in <group_id>_dirty_data.csv

2.   Impute the missing values in <group_id>_missing_data.csv

3.   Detect and remove outlier rows in <group_id>_outlier_data.csv

○    (w.r.t. the  delivery_charges attribute only)

Project Background

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 around Melbourne from which goods are delivered to customers.

Each instance of the data represents a single order from DigiCO 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     that 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.
customer_lat	Latitude of the customer’s location
customer_long	Longitude of the customer’s location
coupon_discount	An integer denoting the percentage discount to be applied to the order_price.
distance_to_nearest_wa rehouse	A float representing the arc distance, in kilometres, between the customer and the nearest warehouse to him/her. (radius of earth: 6378 KM)
delivery_charges	A float representing the delivery charges of the order

Notes:

1.   The output csv files must have the exact same columns as the respective input files. Any misspelling or mismatch will lead to a malfunction of the auto-marker which will in turn lead to losing marks.

2.   In the file  Group<group_id>_dirty_data.csv, any  row can carry no more than one anomaly. (i.e. there can only be up to one issue in a single row.)

3.  All anomalies in dirty data have one and only one possible fix.

4.   There are no data anomalies in the file Group<group_id>_outlier_data.csv except for outliers. Similarly,   there  are  only  coverage  data  anomalies  (i.e.  no  other  data  anomalies)  in Group<group_id>_missing_data.csv.

5.   The  retail  store  has three different warehouses in Melbourne (see warehouse.csv for their locations)

6.   The retail store focuses only on 10 branded items and sells them at competitive prices7.   In order to get the item unit price, a useful python package to solve multivariable equations is

numpy.linalg

8.   The distance is calculated as Haversine Distance (with radius of earth = 6378 KM) like here  .

9.   The  store  has  different  business  rules  depending  on  the  seasons  to  match  the  different demands of each season. For example, delivery charge is calculated using a linear model which differs depending on the season. The model depends linearly (but in different ways for each season) on:

○    Distance between customer and nearest warehouse

○   Whether the customer wants an expedited delivery

○   Whether the customer was happy with his/her last purchase (if no previous purchase, it is assumed that the customer is happy)

10. It is recommended to use sklearn.linear_model.LinearRegression for solving the linear model as demonstrated in the tutorials.

11.  Using proper data for model training is crucial to have a good linear model (i.e. R2  score over 0.97 and very close to 1) to validate the delivery charges. The better your model is, the more   accurate your result will be.

12. To check whether a customer is happy with their last order, the customer's latest review is classified    using    a    sentiment    analysis    classifier.    SentimentIntensityAnalyzer   from nltk.sentiment.vader is used to obtain the polarity score. A sentiment is considered positive if it has a 'compound' polarity score of 0.05 or higher and is considered negative otherwise.Refer to this link for more details on how to use this module.

13. If the customer provided a coupon during purchase, the coupon discount percentage will be applied to the order price before adding the delivery charges (i.e. the delivery charges will    never be discounted).

14. The below columns are error-free (i.e. don’t look for any errors in dirty data for them):

○    coupon_discount

○    delivery_charges

○    The ordered quantity values in the shopping_cart attribute

○    order_id

○    customer_id

○    latest_customer_review

15. For missing data imputation, you are recommended to try all possible methods to impute

missing values and keep the most appropriate one that could provide the best performance.

16. As EDA is part of this assessment, no further information will be given publicly regarding the    data. However, you can brainstorm with the teaching team during tutorials or on the Ed forum.

17. No libraries/packages restriction.

Methodology (10%)

The report <group_id>_ass2_task1.ipynb should demonstrate the methodology (including all steps) to achieve the correct results.

You need to demonstrate your solution using correct steps.

●    Your solution should be presented in a proper way including all required steps.

●    You need to select and use the appropriate Python functions for input, process and output.

●    Your   solution   should   be   an   efficient   one   without   redundant   operations   and unnecessary reading and writing the data.

Task 2: Data Reshaping (15%)

You need to complete task 2 with the suburb_info.xlsx file ONLY. With the given property and suburb related data, you need to study the effect of different normalisation/transformation (e.g. standardisation, min-max normalisation, log, power, box-cox transformation) methods on these columns:        number_of_houses,       number_of_units,        population,        aus_born_perc, median_income, median_house_price. You need to observe and explain their effect assuming we want to develop a linear model to predict the “median_house_price” using the 5 attributes mentioned above.

When reshaping the data, we normally have two main criteria.

●    Second, we want our features to  have as much linear relationship as possible with the target variable (i.e., median_house_price).

You need to first explore the data to see if any scaling or transformation is necessary (if yes why? and  if not, also why?) and then  perform. appropriate actions and document your  results and observations.  Please  note  that the aim for this task  is to  prepare the  data for   a  linear regression model, it’s not building the linear model. That is, you need to record all your steps from load the raw data to complete all the required transformations if any.

Input files	Submission
suburb_info.xlsx	Group<group_id>_ass2_task2.ipynb

You could consider the scenario of task 2 to be an open exploratory project: Jackie and Kiara have got some funding to do an exploratory consulting project on the property market. We wish to understand any interesting insights from the relevant features in different suburbs of Melbourne. Before we step into the final linear regression modelling stage, we wish to hire you to prepare the data for us and tell us if any transformation/normalisation is required? Will those data satisfy the assumptions of linear regression?  How  could we  make  our data  more suitable for the  latter modelling stage.

As  an  exploratory  task,  you  only  need  to  put  your  journey   of  exploration  in  proper documentation in your .ipynb file, no other output file to be submitted for task 2. We will mark based on the .ipynb content for task 2.

Table3. Description of the suburb_info.xlsx file.

suburb	The suburb name, which serves as the index of the data
number_of_houses	The number of houses in the property suburb
number_of_units	The number of units in the property suburb
municipality	The municipality of the property suburb

aus_born_perc	The percentage of the Australian-born population in the property suburb
median_income	The median income of the population in the property suburb
median_house_price	The median ‘house’ price in the property suburb
population	The population in the property suburb

Task 3: Project Reflective Report (15%)

Input files	Submission
N/A	Group<group_id>_report.pdf

3.1 Feedback Session During Week 10 Applied Session

Tasks :Please attend the week 10 applied session and present your working progress to your TA for some feedback. You need to:

1.   Present your current progress

2.  Any future planning you wish to undertake

3.   Record/Noted the TA’s suggestions

4.   Continue your work with tailored solution/follow-ups based on the suggestions

Details:

●   Time/Date: Week 10, during your allocated Applied sessions

●    Duration: Approximate 5-8 minutes per group

●    Location: Normal location of allocated applied sessions in your Allocate+ records

●   Criterion: Please refer to A2 marking rubrics

3.2 Group Reflection Presentation (Hurdle)

There will be a reflective presentation for your A2. The aim for the presentation is to check    your understanding of your A2 project and make sure all submissions are compliant with the academic integrity requirements of Monash.

Details:

●   Time/Date: Week 12, during your allocated Applied sessions

●    Duration: Approximate 5-10 minutes per group

●    Location: Normal location of allocated applied sessions in your Allocate+ records

●   Arrangement: We will provide a time schedule for every group during their

allocated session, please arrive at your allocated time slot. If you arrive earlier, please wait patiently outside the room.

●   Content: Please briefly describe your methodology/ logic of A2 (at least for 80% of A2, detailed subtasks please refer to A2 marking rubrics) and answer questions if any

●   Criterion: Please refer to A2 marking rubrics