代做 program、java/Python 程序语言代写
- 首页 >> Python编程 In this assignment, you will assume the role of a data scientist that has just received an email from a potential
client who owns a new online bank. The clients email reads:
“
Dear all,
CSCU9S2 Assignment: Data Analysis
12th May 2024
As you may be aware, we opened our new online (mobile) bank a few months ago and,
since then, we have been collecting data from our customers. One thing that is particularly
interesting (and intriguing at the same time) to our team is prediction of customer churn and we
would like your help to better understand this. An anonymized dataset of our customers churn is
attached containing information such as age, country, estimated salary, credit score, whether the
customer has exited/left the bank, etc. Could you please create a report on this data? Furthermore,
if you could provide us with any insights that might help us with this matter, it would be very much
appreciated. We believe that certain attributes may influence customer churn, but we are not sure
if there are any noticeable patterns. If you could offer us a solution which could help us, it would
be great!
Kind regards,
”
So, now you need to analyse this data and describe each step you would need to carry out in order to answer the questions raised in the email above. Precisely, you will need to describe all steps according to the CRISP-DM project methodology, i.e., Data Cleaning, Exploratory Data Analysis, and Modelling (Descriptive Analytics, and Predictive Analytics). PLEASE USE THE REPORT TEMPLATE BELOW (penalties will be applied for those who do not use the template provided).
The dataset
Please, download the dataset on VLE. This dataset is composed of three files:
main_personalinfo.csv - this csv file contains personal information regarding the customers, such as the id,
surname (anonymized), gender, age, and geography (i.e., country).
main_financialinfo.csv – this file provides financial information related to the customers, including credit score and estimated annual salary.
main_bankinfo.csv - this csv file provides some banking information, including tenure (how many years the customer has been with the current bank), current balance, current number of products contracted from the bank (for example, credit card, debit card, plus mortgage loan = 3 products), whether the customer has
credit card, whether they are an active member of the bank, whether they have premium account, and whether they have exited/left the bank.
Submission
The submission will be on VLE. Please, make sure to submit your assignment before Sunday the 12th of May.
Plagiarism
You will need to submit a report explaining, in detail, the steps that you would take in order to analyse and
answer the enquiries raised by your client. The template of this report is on the last page of this
document. The word limit of this report is 2000 words.
Work which is submitted for assessment must be your own work. All students should note that the
University has a formal policy on plagiarism which can be found at:
https://www.stir.ac.uk/about/professional-services/student-academic-and-corporate-services/academic-
registry/academic-policy-and-practice/quality-handbook/assessment-and-academic-misconduct/#eight
Plagiarism means presenting the work of others as though it were your own. The University takes a very
serious view of plagiarism, and the penalties can be severe (ranging from a reduced grade in the assessment,
through a fail for the module, to expulsion from the University for more serious or repeated offences).
Specific guidance in relation to Computing Science assignments may be found in the Computing Science
Student Handbook. We check submissions carefully for evidence of plagiarism, and pursue those cases we
find.
Generative AI
For this assignment, the ethical and intentional use of Generative Artificial Intelligence Tools (AI), such as ChatGPT, is permitted with the exception of the use of AI for the specific purpose of programming, data preparation/analysis, critical reflection, and writing, which is NOT permitted as this assessment tests your ability to understand, reflect, and describe the problem and solution effectively.
Whenever AI tools are used you should:
• Cite as a source, any AI tool used in completing your assignment. The library referencing guide should be followed.
• Acknowledge how you have used AI in your work.
Using AI without citation or against assessment guidelines falls within the definition of plagiarism or cheating, depending on the circumstances, under the current Academic Integrity Policy, and will be treated accordingly. Making false or misleading statements as to the extent, and how AI was used, is also an example of “dishonest practice” under the policy. More details below.
Note on Avoiding Academic Misconduct
Work which is submitted for assessment must be your own work. All students should note that the
University has a formal policy on Academic Integrity and Academic Misconduct (including plagiarism)
which can be found here.
Plagiarism: We are aware that assignment solutions by previous students can sometimes be found posted
on GitHub or other public repositories. Do not be tempted to include any such code in your submission.
Using code that is not your own will be treated as “poor academic practice” or “plagiarism” and will be
penalized.
To avoid the risk of your own work being plagiarised by others, do not share copies of your solution, and
keep your work secure both during and after the assignment period.
Collusion: This is an individual assignment: working together with other students is not permitted. If
students submit the same, or very similar work, this will be treated as "collusion" and all students involved
will be penalized.
Contract cheating: Asking or paying someone else to do assignment work for you (contract cheating) is
considered gross academic misconduct, and will result in termination of your studies with no award.
Report Template
1. Introduction/Business Understanding (10 marks)
2. Data Cleaning (20 marks)
3. Exploratory Data Analysis (25 marks)
Note that a penalty will be applied based on the word limit. This penalty will be proportional to how
many words over the limit you are - e.g. 10% over the word limit will incur a 10% penalty.
Summarise the problem the company is asking you to solve. Demonstrate that you can connect it to the data
by explicitly mentioning and explaining the variables that are most likely to be relevant to the problem.
Clean and prepare the dataset. What data cleaning was required for this dataset? What techniques did you
employ to correct them? Create a table reporting the data column with
and explaining how it was identified and fixed. Additionally, report, at least, one example of dirty data,
problem
, describing the problem,
explain how you cleaned it, and then report the cleaned data.
Explore the dataset and report the TWO most interesting observations that you have learned from the data
– you may make more observations/analyses but should report only the 2 most interesting ones. Use
appropriate visualisations/tables to support your findings. Discuss the outcome of those findings. Were any
variables removed/dropped because of this analysis? Why?
4. Descriptive Analytics (25 marks)
5. Machine Learning (20 marks)
Think about TWO questions that might be useful for your client and that can be answered using
descriptive analytics. Answer such questions using this type of analysis. Report: (1) the questions, (2) why
they are important for your client, and (3) the answers.
Now that you understand the business’s needs and concerns, and the data that they have access to, try to
answer the enquiries of your client using machine learning. You do not need to implement this – but
feel free to implement it if you want. Instead, you have to specify: (i) what question(s) you could answer
with machine learning, (ii) what type of problem it is, (iii) what data would be used as input (specify input
and output variables!), and (iv) what kind of model you would use. Justify your choices in model.
client who owns a new online bank. The clients email reads:
“
Dear all,
CSCU9S2 Assignment: Data Analysis
12th May 2024
As you may be aware, we opened our new online (mobile) bank a few months ago and,
since then, we have been collecting data from our customers. One thing that is particularly
interesting (and intriguing at the same time) to our team is prediction of customer churn and we
would like your help to better understand this. An anonymized dataset of our customers churn is
attached containing information such as age, country, estimated salary, credit score, whether the
customer has exited/left the bank, etc. Could you please create a report on this data? Furthermore,
if you could provide us with any insights that might help us with this matter, it would be very much
appreciated. We believe that certain attributes may influence customer churn, but we are not sure
if there are any noticeable patterns. If you could offer us a solution which could help us, it would
be great!
Kind regards,
”
So, now you need to analyse this data and describe each step you would need to carry out in order to answer the questions raised in the email above. Precisely, you will need to describe all steps according to the CRISP-DM project methodology, i.e., Data Cleaning, Exploratory Data Analysis, and Modelling (Descriptive Analytics, and Predictive Analytics). PLEASE USE THE REPORT TEMPLATE BELOW (penalties will be applied for those who do not use the template provided).
The dataset
Please, download the dataset on VLE. This dataset is composed of three files:
main_personalinfo.csv - this csv file contains personal information regarding the customers, such as the id,
surname (anonymized), gender, age, and geography (i.e., country).
main_financialinfo.csv – this file provides financial information related to the customers, including credit score and estimated annual salary.
main_bankinfo.csv - this csv file provides some banking information, including tenure (how many years the customer has been with the current bank), current balance, current number of products contracted from the bank (for example, credit card, debit card, plus mortgage loan = 3 products), whether the customer has
credit card, whether they are an active member of the bank, whether they have premium account, and whether they have exited/left the bank.
Submission
The submission will be on VLE. Please, make sure to submit your assignment before Sunday the 12th of May.
Plagiarism
You will need to submit a report explaining, in detail, the steps that you would take in order to analyse and
answer the enquiries raised by your client. The template of this report is on the last page of this
document. The word limit of this report is 2000 words.
Work which is submitted for assessment must be your own work. All students should note that the
University has a formal policy on plagiarism which can be found at:
https://www.stir.ac.uk/about/professional-services/student-academic-and-corporate-services/academic-
registry/academic-policy-and-practice/quality-handbook/assessment-and-academic-misconduct/#eight
Plagiarism means presenting the work of others as though it were your own. The University takes a very
serious view of plagiarism, and the penalties can be severe (ranging from a reduced grade in the assessment,
through a fail for the module, to expulsion from the University for more serious or repeated offences).
Specific guidance in relation to Computing Science assignments may be found in the Computing Science
Student Handbook. We check submissions carefully for evidence of plagiarism, and pursue those cases we
find.
Generative AI
For this assignment, the ethical and intentional use of Generative Artificial Intelligence Tools (AI), such as ChatGPT, is permitted with the exception of the use of AI for the specific purpose of programming, data preparation/analysis, critical reflection, and writing, which is NOT permitted as this assessment tests your ability to understand, reflect, and describe the problem and solution effectively.
Whenever AI tools are used you should:
• Cite as a source, any AI tool used in completing your assignment. The library referencing guide should be followed.
• Acknowledge how you have used AI in your work.
Using AI without citation or against assessment guidelines falls within the definition of plagiarism or cheating, depending on the circumstances, under the current Academic Integrity Policy, and will be treated accordingly. Making false or misleading statements as to the extent, and how AI was used, is also an example of “dishonest practice” under the policy. More details below.
Note on Avoiding Academic Misconduct
Work which is submitted for assessment must be your own work. All students should note that the
University has a formal policy on Academic Integrity and Academic Misconduct (including plagiarism)
which can be found here.
Plagiarism: We are aware that assignment solutions by previous students can sometimes be found posted
on GitHub or other public repositories. Do not be tempted to include any such code in your submission.
Using code that is not your own will be treated as “poor academic practice” or “plagiarism” and will be
penalized.
To avoid the risk of your own work being plagiarised by others, do not share copies of your solution, and
keep your work secure both during and after the assignment period.
Collusion: This is an individual assignment: working together with other students is not permitted. If
students submit the same, or very similar work, this will be treated as "collusion" and all students involved
will be penalized.
Contract cheating: Asking or paying someone else to do assignment work for you (contract cheating) is
considered gross academic misconduct, and will result in termination of your studies with no award.
Report Template
1. Introduction/Business Understanding (10 marks)
2. Data Cleaning (20 marks)
3. Exploratory Data Analysis (25 marks)
Note that a penalty will be applied based on the word limit. This penalty will be proportional to how
many words over the limit you are - e.g. 10% over the word limit will incur a 10% penalty.
Summarise the problem the company is asking you to solve. Demonstrate that you can connect it to the data
by explicitly mentioning and explaining the variables that are most likely to be relevant to the problem.
Clean and prepare the dataset. What data cleaning was required for this dataset? What techniques did you
employ to correct them? Create a table reporting the data column with
and explaining how it was identified and fixed. Additionally, report, at least, one example of dirty data,
problem
, describing the problem,
explain how you cleaned it, and then report the cleaned data.
Explore the dataset and report the TWO most interesting observations that you have learned from the data
– you may make more observations/analyses but should report only the 2 most interesting ones. Use
appropriate visualisations/tables to support your findings. Discuss the outcome of those findings. Were any
variables removed/dropped because of this analysis? Why?
4. Descriptive Analytics (25 marks)
5. Machine Learning (20 marks)
Think about TWO questions that might be useful for your client and that can be answered using
descriptive analytics. Answer such questions using this type of analysis. Report: (1) the questions, (2) why
they are important for your client, and (3) the answers.
Now that you understand the business’s needs and concerns, and the data that they have access to, try to
answer the enquiries of your client using machine learning. You do not need to implement this – but
feel free to implement it if you want. Instead, you have to specify: (i) what question(s) you could answer
with machine learning, (ii) what type of problem it is, (iii) what data would be used as input (specify input
and output variables!), and (iv) what kind of model you would use. Justify your choices in model.