BUSS6002代写、代写Java/Python程序

日期：2023-04-18 09:10

BUSS6002 Assignment 1

Semester 1, 2023

Due Date

Due: at 12:00pm (noon) on Monday, 17 April 2023 (start of week 8).

A late penalty of 5% per day applies if you submit your assignment late without a suc-

cessful special consideration or simple extension. See the Unit Outline for more details.

You may submit multiple times but only your last submission will be marked.

Instructions

You must submit a Jupyter Notebook (.ipynb) file with the following filename format,

replacing STUDENTID with your own student ID: BUSS6002 A1 STUDENTID.ipynb.

There is a limit of 1000 words for your submission (excluding code, tables, and captions).

Do not include any more Python output than necessary and include only concise discus-

sions.

Each task must be clearly labelled with the corresponding question (and sub-question)

number so that the marker can spot your solution easily.

The submitted .ipynb file must be free of any errors, and the results must be reproducible.

All figures must be appropriately sized (by setting figsize) and have readable axis labels

and legends (where applicable).

Use plt.show() instead of plt.savefig(‘plot.png’) to display each figure.

Rubric

This assignment is worth 20% of the unit’s marks. The assessment is designed to test your

technical ability and statistical knowledge in performing important basic tasks associated with

an exploratory data analysis (or EDA) of a real-world dataset.

Assessment Item Goal Marks

Question 1 Overall summary of the dataset 10

Question 2 Univariate analysis 10

Question 3 Multivariate analysis 19

Jupyter Notebook Logical and clear presentation 1

Total 40

Table 1: Assessment Items and Mark Allocation

1

Overview

The Australian federal government is building a website to provide households with a tool to

determine if installing a rooftop solar panel system is right for them. On the website, users will

be able to enter information about their house and the website will provide an estimate of the

possible solar power generation.

The government collected a random sample of existing households with solar panels, includ-

ing information about the households, solar panel installation and the associated solar power

generation. The generation from the solar panels was collected from 1/1/2022 to 31/12/2022.

The data has been assembled from a multiple sources including the customer energy retailer,

energy distributors and solar installers. Sampling is limited to installations with:

a single solar panel array or multiple arrays that are oriented identically,

rooftop installation only.

As a data-scientist-in-training, you will assist the project by completing a variety of EDA

tasks.

Data Files

The following files are available on Canvas.

File Description

SolarSurvey.csv Data file with 3000 observations.

DataDictionary.txt Data dictionary containing description of each variable

BUSS6002 A1 STUDENTID.ipynb A Jupyter Notebook template for getting you started

Table 2: Files Provided

Question 1 (10 Marks)

a) (4 marks) Write some code to automatically print out the column names of the variables

with missing values, as well as the number of missing observations associated with each

of those variables. The output should be sorted by the number of missing observations

from most to least.

b) (4 marks) Write some code to cross-check the data against the data dictionary and identify

discrepancies.

c) (2 marks) Briefly discuss your findings from a) and b).

Question 2 (10 Marks)

a) (4 marks) Graphically summarise the distributions of the variables Generation and Panel Capacity,

one at a time, and briefly discuss the distributional characteristics of the two variables.

Your discussion should also connect the distributional characteristics to the domain-

specific context of these variables.

b) (2 marks) Graphically summarise the distribution of the variable Roof Azimuth and briefly

discuss the distributional characteristics of the variable. Your discussion should also con-

nect the distributional characteristics to the domain-specific context.

2

c) (3 marks) Given that there may be an association with Roof Azimuth and generation,

transform Roof Azimuth so that equal angles either side of North are treated the same.

d) (1 mark) Visualise your new version of Roof Azimuth.

Question 3 (19 Marks)

a) (2 marks) Generate a visualisation of the correlation coefficient between Generation and

all variables. Briefly discuss your findings from in the context of predicting Generation.

b) (3 marks) Construct an appropriate plot to visualise the relationship between Generation

and Panel Capacity. Briefly discuss your findings.

c) (3 marks) Construct an appropriate plot to visualise the relationship between Generation

and Latitude. Briefly discuss your findings.

d) (5 marks) For each city, compile a table that shows the mean Generation for combinations

of Roof Azimuth and Roof Pitch. Bin values of Roof Azimuth into 45° groups. Briefly

discuss your findings.

e) (4 marks) Pick one city, bin households based on Panel Capacity and Shading. For each

bin display the mean Generation. Display the results using an appropriate visualisation.

Discuss your findings.

f) (4 marks) Pick one city, bin households based on Panel Capacity and Year. For each

bin display the mean Generation. Display the results using an appropriate visualisation.

Discuss your findings.