Naive_bayes_titanic.md

library('e1071')
data('Titanic')

Titanic_df = as.data.frame(Titanic)

head(Titanic_df)

Class	Sex	Age	Survived	Freq
1st	Male	Child	No	0
2nd	Male	Child	No	0
3rd	Male	Child	No	35
Crew	Male	Child	No	0
1st	Female	Child	No	0
2nd	Female	Child	No	0

repeating_sequence=rep.int(seq_len(nrow(Titanic_df)), Titanic_df$Freq)
Titanic_dataset=Titanic_df[repeating_sequence,]
row.names(Titanic_dataset) = NULL
Titanic_dataset$Freq=NULL
head(Titanic_dataset)

Class	Sex	Age	Survived
3rd	Male	Child	No
3rd	Male	Child	No
3rd	Male	Child	No
3rd	Male	Child	No
3rd	Male	Child	No
3rd	Male	Child	No

Naive_Bayes_Model=naiveBayes(Survived ~., data=Titanic_dataset)

Naive_Bayes_Model

Naive Bayes Classifier for Discrete Predictors

Call:
naiveBayes.default(x = X, y = Y, laplace = laplace)

A-priori probabilities:
Y
      No      Yes 
0.676965 0.323035 

Conditional probabilities:
     Class
Y            1st        2nd        3rd       Crew
  No  0.08187919 0.11208054 0.35436242 0.45167785
  Yes 0.28551336 0.16596343 0.25035162 0.29817159

     Sex
Y           Male     Female
  No  0.91543624 0.08456376
  Yes 0.51617440 0.48382560

     Age
Y          Child      Adult
  No  0.03489933 0.96510067
  Yes 0.08016878 0.91983122

NB_Predictions=predict(Naive_Bayes_Model,Titanic_dataset) 
Titanic_dataset$NB_Pred = NB_Predictions

summary(Titanic_dataset)

  Class         Sex          Age       Survived   NB_Pred   
 1st :325   Male  :1731   Child: 109   No :1490   No :1726  
 2nd :285   Female: 470   Adult:2092   Yes: 711   Yes: 475  
 3rd :706                                                   
 Crew:885                                                   

table(Titanic_dataset$Age , Titanic_dataset$NB_Pred , Titanic_dataset$Sex)

, ,  = Male

       
          No  Yes
  Child   59    5
  Adult 1667    0

, ,  = Female

       
          No  Yes
  Child    0   45
  Adult    0  425