代做DTS301TC、R编程设计代写

项目预算：开发周期：发布时间：要求地区：

XJTLU Entrepreneur College (Taicang) Cover Sheet
Module code and Title DTS301TC Data Mining
School Title School of AI and Advanced Computing
Assignment Title Group Assignment
Submission Deadline Sunday, October 15th 23:59 (Beijing Time), 2023
Final Word Count
If you agree to let the university use your work anonymously for teaching
and learning purposes, please type “yes” here.
I certify that I have read and understood the University’s Policy for dealing with Plagiarism,
Collusion and the Fabrication of Data (available on Learning Mall Online). With reference to this
policy I certify that:
• My work does not contain any instances of plagiarism and/or collusion.
My work does not contain any fabricated data.
By uploading my assignment onto Learning Mall Online, I formally declare
that all of the above information is true to the best of my knowledge and
belief.
Scoring – For Tutor Use
Student ID
Stage of
Marking
Marker
Code
Learning Outcomes Achieved （F/P/M/D）
(please modify as appropriate)
Final
Score
A B C
1
st Marker – red
pen
Moderation
– green pen
IM
Initials
The original mark has been accepted by the moderator
(please circle as appropriate):
Y / N
Data entry and score calculation have been checked by
another tutor (please circle):
Y
2
nd Marker if
needed – green
pen
For Academic Office Use Possible Academic Infringement (please tick as appropriate)
Date
Received
Days
late
Late
Penalty
☐ Category A
Total Academic Infringement Penalty
(A,B, C, D, E, Please modify where
necessary) _____________________
☐ Category B
☐ Category C
☐ Category D
☐ Category E
DTS301TC Data Mining
Group Assignment
Deadline: Sunday, October 15th 23:59 (Beijing Time), 2023.
Percentage in final mark: 40% (20% Group work + 20% Individual work)
Learning outcomes assessed:
A. Introduce students to the basic concepts and techniques of Data Mining
B. Demonstrate knowledge of statistical data analysis techniques used in decision making
C. Apply principles of Data Mining to the analysis of large-scale problems
Late policy: 5% of the total marks available for the assessment shall be deducted from the
assessment mark for each working day after the submission date, up to a maximum of five working
days
Risks:
• Please read the coursework instructions and requirements carefully. Not following these
instructions and requirements may result in loss of marks.
• The assignment must be submitted via Learning Mall to the correct drop box. Only electronic
submission is accepted and no hard copy submission.
• All students must download their file and check that it is viewable after submission.
Documents may become corrupted during the uploading process (e.g. due to slow internet
connections). However, students themselves are responsible for submitting a functional and
correct file for assessments.
• Academic Integrity Policy is strictly followed.
Overview
The purpose of this assignment is to get familiar with the basic concepts and techniques of data
mining and gain experience in R and data mining applications. In this group project, you are
expected to apply data mining techniques to predict hotel reservation cancellations using the R
programming language.
Dataset
Online hotel booking platforms have made it easier for guests to cancel hotel reservations for free
or at low cost, but this can lead to lost revenue for hotels. In this project, we will use data mining
techniques to analyze hotel reservation data and help hotel owners better predict whether a
customer will accept or cancel a reservation. The dataset used in this assignment contains
information on around 18,000 hotel reservation records. In the hotel_reservation.csv file, each row
contains information about one reservation record. The columns are explained in a separate file
named Variables description.txt.
Requirements and Tasks
Given the datasets, you are expected to finish the following tasks using R programming language.
You are allowed to use existing R libraries to solve the following tasks. Tasks 1 and 2 are group
work; Tasks 3 and 4 are individual work. Please include all the source code and results for T1 and
T2 in a group pdf file; include all the source code, results and evaluation report for T3 and T4 in
an individual pdf file. Please also explain anything that is not obvious in the pdf files. Mark
breakdown for each task can be found from the DTS301TC Group Assignment Marking Criteria
at the end of this document.
T1. Exploratory Data Analysis – Group (25 marks)
T1-1: Load the CSV file; show the dimensionality, structure and summary of the dataset.
T1-2: Calculate and visualize the number of guests from different countries.
T1-3: Calculate and visualize the average number of nights the guests stayed per month.
T1-4: Calculate and visualize the number of guests per month for both Resort Hotel and City Hotel.
T1-5: Calculate and visualize the average hotel price (adr) of each month for both Resort Hotel
and City Hotel.
T1-6: Analyze data visualization results and summarize your findings in the pdf file.
T2. Data Pre-processing – Group (25 marks)
In task 2, you need to perform the following data pre-processing tasks on the given dataset. Each
pre-processing task may be handled with different methods, e.g., fill or drop missing values. Please
discuss with your team members and select a suitable method for those tasks.
T2-1: Check for missing values and handle them if they exist.
T2-2: Check for duplicates and remove them if they exist.
T2-3: Plot data distribution, check for outliers and remove them if they exist.
T2-4: Apply data normalization.
T2-5: Encode categorical values.
T2-6: Store the preprocessed dataset into a new CSV file.
T3. Modelling – Individual (30 marks)
In Task 3, you need to build one data mining model based on the pre-processed dataset in Task 2.
If you made further pre-processing steps for better model performance, please explain the steps in
your individual pdf file.
T3-1: Each team member applies one different data mining model (e.g., kNN, logistic regression,
decision tree, random forest, SVM, etc.) to predict if a hotel reservation will be cancelled (attribute
in the second column of the dataset) using the remaining attributes.
T3-2: Use k-fold cross validation with k = 5 folds to evaluate performance.
T3-3: Select features and/or tune model parameters to achieve the optimal performance. Show (or
plot) model performance under different feature selection and/or parameter tuning settings.
T3-4: Report the best prediction results (i.e., Accuracy, Precision, Recall, F1-score) and the
corresponding running time.
T4. Evaluation – Individual (20 marks)
T4-1: Use one example from the given dataset and draw plots or figures to explain how the input
is processed by you model to generate prediction results.
T4-2: Discuss the performance of your model with your team members, i.e., Accuracy, Precision,
Recall, F1-score and running time (Run the models under the same setting if necessary). Analyze
the performance of your model.
T4-3: Discuss the advantages and disadvantages of the model you choose and point out some
future directions to further improve model performance.
Group Submission
One group member must submit a zip file (named DTS301TC_ GroupID.zip) containing the
following documents.
1. Cover sheet with student IDs of all group members.
2. Source code files for Tasks 1 and 2.
3. A preprocessed dataset (in CSV format) generated in T2.
4. A pdf file containing all the source code and results for T1 and T2.
Individual Submission
Each student must submit a zip file (named DTS301TC_GroupID_IDNumber.zip) containing the
following documents.
1. Cover sheet with student ID.
2. Source code files for Tasks 3 and 4. Please name your source code file:
IDnumber_YourName_ModelName.R (e.g.:1900000_ZhangSan_KNN.R).
3. A pdf file containing source code, results and evaluation report for T3 and T4. Please
name your pdf file: IDnumber_YourName_ModelName.pdf (e.g.:
1900000_ZhangSan_KNN.pdf).
Generic Marking Criteria
Grade Point
Scale
Criteria to be satisfied
A 81+ First ➢ Outstanding work that is at the upper limit of
performance.
➢ Work would be worthy of dissemination under
appropriate conditions.
➢ Mastery of advanced methods and techniques at a
level beyond that explicitly taught.
➢ Ability to synthesise and employ in an original way
ideas from across the subject.
➢ In group work, there is evidence of an outstanding
individual contribution.
➢ Excellent presentation.
➢ Outstanding command of critical analysis and
judgment.
B 70 - 80 First ➢ Excellent range and depth of attainment of intended
learning outcomes.
➢ Mastery of a wide range of methods and techniques.
➢ Evidence of study and originality clearly beyond the
bounds of what has been taught.
➢ In group work, there is evidence of an excellent
individual contribution.
➢ Excellent presentation.
➢ Able to display a command of critical thinking,
analysis and judgment.
C 60 - 69 Upper
Second
➢ Attained all the intended learning outcomes for a
module or assessment.
➢ Able to use well a range of methods and techniques
to come to conclusions.
➢ Evidence of study, comprehension, and synthesis
beyond the bounds of what has been explicitly
taught.
➢ Very good presentation of material.
➢ Able to employ critical analysis and judgement.
➢ Where group work is involved there is evidence of a
productive individual contribution
D 50- 59 Lower
Second
➢ Some limitations in attainment of learning
objectives but has managed to grasp most of them.
➢ Able to use most of the methods and techniques
taught.
➢ Evidence of study and comprehension of what has
been taught
➢ Adequate presentation of material.
➢ Some grasp of issues and concepts underlying the
techniques and material taught.
➢ Where group work is involved there is evidence of a
positive individual contribution.
E 40 - 49 Third ➢ Limited attainment of intended learning outcomes.
➢ Able to use a proportion of the basic methods and
techniques taught.
➢ Evidence of study and comprehension of what has
been taught, but grasp insecure.
➢ Poorly presented.
➢ Some grasp of the issues and concepts underlying
the techniques and material taught, but weak and
incomplete.
F 0 - 39 Fail ➢ Attainment of only a minority of the learning
outcomes.
➢ Able to demonstrate a clear but limited use of some
of the basic methods and techniques taught.
➢ Weak and incomplete grasp of what has been
taught.
➢ Deficient understanding of the issues and concepts
underlying the techniques and material taught.
➢ Attainment of nearly all the intended learning
outcomes deficient.
➢ Lack of ability to use at all or the right methods and
techniques taught.
➢ Inadequately and incoherently presented.
➢ Wholly deficient grasp of what has been taught.
➢ Lack of understanding of the issues and concepts
underlying the techniques and material taught.
➢ Incoherence in presentation of information that
hinders understanding.
G 0 Fail ➢ No significant assessable material, absent, or
assessment missing a "must pass" component.
DTS301TC Group Assignment Marking Criteria
Group Components: Tasks 1 and 2.
Weight: 50% (20% of the final module mark)
Tasks 50 Components Description Maximum
Credit Mark
Task 1 25
Task 1-1
CSV file is imported to R
Summary of dataset 3
Task 1-2
Number of guests from different countries is
correctly calculated
Clear data visualization of results
4
Task 1-3
Average number of nights the guests stayed
per month is correctly calculated
Clear data visualization of results
4
Task 1-4
Number of guests per month is correctly
calculated
Clear data visualization of results
4
Task 1-5
Average hotel price of each month is correctly
calculated
Clear data visualization of results
4
Task 1-6
Data visualization results well-analyzed,
findings are correct and meaningful 4
Code Quality Code readability, formatting and comments 2
Task 2 25
Task 2-1
Missing values are correctly checked and
handled 4
Task 2-2
Duplicates are correctly checked and removed
if exist 4
Task 2-3
Data distribution is checked
Potential outliers are removed 4
Task 2-4 Data normalization is correctly applied 4
Task 2-5
Encoding is correctly applied for categorical
values 4
Task 2-6 New dataset is stored in a CSV file 3
Code Quality Code readability, formatting and comments 2
Late Submission?  Yes
 No
Days
late
Group Work Final Marks
Individual Components: Tasks 3 and 4
Weight: 50% (20% of the final module mark)
Tasks 50 Components Description Maximum
Credit Mark
Task 3 30
Task 3-1
1) Model function is correctly used
2) Model parameters are correctly set 4
Task 3-2
1) K-fold cross validation is set correctly
2) Cross validation is correctly used in the data
mining model
4
Task 3-3
1) Select features and/or tune model
parameters to achieve the optimal
performance.
2) Show (or plot) model performance under
different settings.
10
Task 3-4
1) Results (Accuracy, Precision, Recall, F1-
score and running time) are correct and
consistent
2) Optimal performance for the chosen model
is achieved.
10
Code quality Code readability, formatting and comments 2
Task 4 20
Task 4-1
Plots or charts of the model are given
One suitable example is given
Classification model is well-explained
6
Task 4-2 Performance of the model is well-analyzed 6
Task 4-3
Pros and cons of the model is well-discussed
Potential future improvement directions
discussed
6
Report
quality Fluency, readability and conciseness 2
Late Submission?  Yes
 No
Days
late
Individual Work Final Marks