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
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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).