Description
Problem 1 [10 points]
Background : Consider the attached dataset assign2_ChurnData.csv, which has 20 variables:
“Gender” “SeniorCitizen” “Partner” “Dependents”
“Tenure” “PhoneService” “MultipleLines” “InternetService”
“OnlineSecurity” “OnlineBackup” “DeviceProtection” “TechSupport”
“StreamingTV” “StreamingMovies” “Contract” “PaperlessBilling”
“PaymentMethod” “MonthlyCharges” “TotalCharges” “Churn”
The target is to fit an optimal tree model and a random forest to predict the chance of “Churn”.
Task : Import the dataset, perform exploratory data analysis on the variables, and construct
the optimal cross-validated decision tree to predict the response variable “Churn” in case of the
given dataset assign2_ChurnData.csv. Follow this up by building an optimal random forest
model to predict “Churn” in case of the given dataset. Briefly comment (within the code) on
your observations and on the choices you make in the process of building the optimal models.
Also mention (within the code) which variables you think are the most important in this case.
Problem 2 [10 points]
Background: The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.
On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg,
killing 1502 out of 2224 passengers and crew. In the following online challenge, Kaggle asks you
to analyse what sorts of people were likely to survive, as a case-study of binary classification.
Task : Take part in the Kaggle Competition – “Titanic: Machine Learning from Disaster” –
available at https://www.kaggle.com/c/titanic. You are only allowed to use optimal decision
trees or random forests as your classification model. Submit your code within the submission file
assign2_FullName.R, and if you wish, you may also submit your predictions online at Kaggle.
Briefly comment (within the code) on the choices you make in the process of building the optimal
models, as well as which variables you think are the most important for survival in this case.
This is an individual assignment. Properly acknowledge every source of information that you
refer to, including discussions with your fellow students, if any. Verbatim copy from any source
is strongly discouraged, and plagiarism will be heavily penalized. It is strongly recommended that
you write the codes completely on your own. Feel free to write the codes in Python if you want.
