Capstone Project guidelines released
Project summaries released    29th April 2020
1st May 2020
Selection of projects by the learner    3rd May 2020
Projects Data Release date   
4th May 2020

Project Notes I submission deadline    17th May 2020
First milestone session    23th/24th May 2020
Project Notes II submission deadline    1st June 2020
Second milestone session    6th/7th June 2020
Project Notes III submission deadline    22th June 2020
Third milestone session    27th/28th June 2020

Final Report Submission
Final Presentation Submission
    6th July 2020
9th July 2020
Final Capstone Presentation     11th July 2020

Capstone Project guidelines released
Project summaries released    29th April 2020
1st May 2020
Selection of projects by the learner    3rd May 2020
Projects Data Release date   
4th May 2020

Project Notes I submission deadline    17th May 2020
First milestone session    23th/24th May 2020
Project Notes II submission deadline    1st June 2020
Second milestone session    6th/7th June 2020
Project Notes III submission deadline    22th June 2020
Third milestone session    27th/28th June 2020

Final Report Submission
Final Presentation Submission
    6th July 2020
9th July 2020
Final Capstone Presentation     11th July 2020

I need the R code, interpretation of results and variables and reasoning for choosing certain parameters and such for Naive Bayes classifier, J48, logistic regression and SVM. I'm having issues with some functions, it would help me greatly to dedicate time on more important areas of my paper.
Data set: first 24 features are predictors, last one is the target variable. 0 are true news, 1 are fake news.
If it has any importance, I would prefer Caret package.

#One sided confidence intervals

#For p
library(regclass)
data("CUSTREACQUIRE")
summary(CUSTREACQUIRE)


summary(CUSTREACQUIRE$Reacquire)
mean( CUSTREACQUIRE$Reacquire == "Yes" )





#Old reacquire policy got 60% of churned customers. New one is cheaper, and may not be as effective
#Ho: p=0.6 vs. HA: p < 0.6
binom.test(295,500,alternative = "less", p=0.6)
# 95 percent confidence interval:
# 0.0000000 0.6267122
#60% is still a plausible value for p, retain Ho


#Is the average lifetimevalue 2 larger than lifetime value 1?
#Ho: mu2 = mu1 vs. HA: mu2 > mu1
#Ho: mu2 - mu1 = 0 vs HA: mu2 - mu1 > 0
SUB <- subset(CUSTREACQUIRE,Reacquire=="Yes")
summary(SUB)
t.test(SUB$Lifetime2,SUB$Lifetime1,paired=TRUE,alternative="greater")
# 95 percent confidence interval:
# 110.8438 Inf




#Median Age < 53?
median(CUSTREACQUIRE$Age)
# < alternative wants (-Inf, quantile(,.95)

Description
Thera Bank - Loan Purchase Modeling
This case is about a bank (Thera Bank) which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget. The department wants to build a model that will help them identify the potential customers who have a higher probability

Description
Thera Bank - Loan Purchase Modeling
This case is about a bank (Thera Bank) which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget. The department wants to build a model that will help them identify the potential customers who have a higher probability