Introduction to data science Naive Bayes

This is a data science course I am taking in University and I have the excel homework based on Naive Bayes and I cannot answer the questions. The questions are in the uploaded document with the instructions and the data sheet with all the relevant data is in the link provided in the uploaded document.

Get Help With a similar task to - Introduction to data science Naive Bayes

Login to view and/or buy answers.. or post an answer
Additional Instructions:

Introduction to Data Science in the Internet Era Home Assignment #2 Subject: Naïve Bayes Classifier - Personal Loan Acceptance. In this assignment you should use the following google sheet Note: In order to use the google sheet you should create your own copy of the google sheet. You can use the option “Make a copy” under File. In the google sheet there is a sheet named “UniversalBankData”. This is the labeled data for this assignment. This sheet contains data on 800 customers of Universal Bank. The data include customer demographic information (age, income, etc.), the customer’s relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan – Column J in the sheet). The outcome of the classification exercise is to assign a class label to a customer that indicates if the customer will accept a load offer or will reject the offer. For the customers in the training data this is indicated by the Personal Loan column in the sheet (column J) (It will be abbreviated Loan below). ● 1 indicates “Accept the Loan Offer” ● 0 indicates “Reject the Load Offer” In this exercise we focus on several predictors: ● Online - whether or not the customer is an active user of online banking services (Online=1) – Column M ● Credit Card (abbreviated CC below) - does the customer hold a credit card issued by the bank (CreditCard=1) – Column N ● EducGard – whether the customer has Grad education (EducGrad=1) – Column O 1. Create a pivot table in the google sheet as we have done in class for computing the following conditional probabilities as use it to compute the following probabilities (14 points - 6 points for the pivot table and 2 points for https://docs.google.com/spreadsheets/d/1u8f0w5kb0FGzV4ilY2QcmTttxefcu7KlOZUtyOGZXsA/edit?usp=sharing each conditional probability) ● a bank customer does not have a credit card (CreditCard=0) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer has a credit card (CreditCard=1) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer does not have a credit card (CreditCard=0) given he/she accepted a loan offer (Personal Loan=1) ● a bank customer has a credit card (CreditCard=1) given he/she accepted a loan offer (Personal Loan=1) 2. Create a pivot table in the google sheet as we have done in class for computing the following conditional probabilities as use it to compute the following probabilities (14 points - 6 points for the pivot table and 2 points for each conditional probability) ● a bank customer does not use online banking (Online=0) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer uses online banking (Online=1) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer does not use online banking (Online=0) given he/she accepted a loan offer (Personal Loan=1) ● a bank customer uses online banking (Online=1) given he/she accepted a loan offer (Personal Loan=1) 3. Create a pivot table in the google sheet as we have done in class for computing the following conditional probabilities as use it to compute the following probabilities (14 points - 6 points for the pivot table and 2 points for each conditional probability) ● a bank customer does not have Grad Education (EducGrad=0) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer has Grad Education (EducGrad=1) given he/she rejected a loan offer (Personal Loan=0) ● a bank customer does not have Grad Education (EducGrad=0) given he/she accepted a loan offer (Personal Loan=1) ● a bank customer has Grad Education (EducGrad=1) given he/she accepted a loan offer (Personal Loan=1) 4. Using the pivot tables you have prepared , you should compute the following conditional probabilities that a customer will receive a load offer in given the following predictors values: (10 points for each computation) ● The customer owns a bank credit card and is actively using online banking services ● The customer has Grad education and is actively using online banking services. ● The customer has Grad education and owns a bank credit card ● The customer has Grad education , owns a credit card but does not use online banking 5. Given the maximal rule assign the class label to each of the above 4 cases (in question 4). (2 points for each case) 6. Given a threshold of 33% for accepting the loan offer and the cut-off rule assign the class label to each of the above 4 cases (in question 4) and explain why you assign this class label. (2.5 points for each case) Instructions: ● All excel work should be done and submitted in google sheet ● All computations should be done in google sheet (as much as possible) ● Add to the sheet explanations or provide them in a separate document in a clear way ● The assignment should be done in pairs. ● The assignment should be submitted as a google sheet with the raw data in the relevant pivot tables plus a word/pdf document with explanations as required ● List your name and ID at the beginning of the assignment. ● Submit the assignment via moodle. ● The assignment is due on Sunday June 11th 2021. ● If you have any questions please email ylevi@post.idc.ac.il Good Luck Dr. David Movshovitz

Related Questions

Similar orders to Introduction to data science Naive Bayes
10
Views
0
Answers
Exam for data Analysis for managers 9 questions to do
I need my midterm exam done it is 9 exam questions you have 120 mins to start and finish the test start from finish .You can’t log out of the exam or is not a proctor exam also...
74
Views
0
Answers
Create a BAyesian Network using python.
all the details are in the pdf I have uploaded please go through it. you need to create a Bayesian network and create the functions using python as provided in the pdf. you need to get the output as provided in the pdf file those are the example output of ...
48
Views
0
Answers
Apply cumulative forecasting model Identify and describe the basic components of a time series and implement time series
Identify and describe the basic components of a time series and implement time series models in Excel. Follow the instructions below and upload both your Word and Excel file....
73
Views
0
Answers
course work for legal, Ethical and Security aspects of Data Science
course work for Legal, Ethical and Security aspects of Data Science Coursework 1 - Legal Aspects course work for Legal, Ethical and Security aspects of Data Science Coursework 1 - Legal Aspects...