MACHINE LEARNING IN TITANIC

THIS FUNCTION HELPS US TO VIEW THE IMPORTED DATA.

RENAMING THE DATASET.

str(Titanic)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   891 obs. of  12 variables:
 $ PassengerId: num  1 2 3 4 5 6 7 8 9 10 ...
 $ Survived   : num  0 1 1 1 0 0 0 0 1 1 ...
 $ Pclass     : num  3 1 3 1 3 3 1 3 3 2 ...
 $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
 $ Sex        : chr  "male" "female" "female" "female" ...
 $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp      : num  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch      : num  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
 $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin      : chr  NA "C85" NA "C123" ...
 $ Embarked   : chr  "S" "C" "S" "S" ...

THIS FUNCTION SHOWS THE STRUCTURE OF THE TABLE.

summary(Titanic)

  PassengerId       Survived          Pclass          Name               Sex           
 Min.   :  1.0   Min.   :0.0000   Min.   :1.000   Length:891         Length:891        
 1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000   Class :character   Class :character  
 Median :446.0   Median :0.0000   Median :3.000   Mode  :character   Mode  :character  
 Mean   :446.0   Mean   :0.3838   Mean   :2.309                                        
 3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000                                        
 Max.   :891.0   Max.   :1.0000   Max.   :3.000                                        
                                                                                       
      Age            SibSp           Parch           Ticket               Fare       
 Min.   : 0.42   Min.   :0.000   Min.   :0.0000   Length:891         Min.   :  0.00  
 1st Qu.:20.12   1st Qu.:0.000   1st Qu.:0.0000   Class :character   1st Qu.:  7.91  
 Median :28.00   Median :0.000   Median :0.0000   Mode  :character   Median : 14.45  
 Mean   :29.70   Mean   :0.523   Mean   :0.3816                      Mean   : 32.20  
 3rd Qu.:38.00   3rd Qu.:1.000   3rd Qu.:0.0000                      3rd Qu.: 31.00  
 Max.   :80.00   Max.   :8.000   Max.   :6.0000                      Max.   :512.33  
 NA's   :177                                                                         
    Cabin             Embarked        
 Length:891         Length:891        
 Class :character   Class :character  
 Mode  :character   Mode  :character  
                                      
                                      
                                      
                                      

THIS FUNCTION SHOWS THE OVERALL SUMMARY OF THE TABLE.

THIS FUNCTION HELPS US TO VIEW THE RENAMED DATA.

OMITING THE NULL VALUES FROM THE DATASET.

MAKING THE VARIABLE AGE INTO NUMERIC.

SUBISTUTING THE MEAN VALUE TO FILL IN THE NA VALUES COLOUM.

str(Titanic)

Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   183 obs. of  7 variables:
 $ Survived: num  1 1 0 1 1 1 1 0 1 0 ...
 $ Pclass  : num  1 1 1 3 1 2 1 1 1 1 ...
 $ Sex     : chr  "female" "female" "male" "female" ...
 $ Age     : num  38 35 54 4 58 34 28 19 49 65 ...
 $ SibSp   : num  1 1 0 1 0 0 0 3 1 0 ...
 $ Parch   : num  0 0 0 1 0 0 0 2 0 1 ...
 $ Fare    : num  71.3 53.1 51.9 16.7 26.6 ...
 - attr(*, "na.action")= 'omit' Named int  1 3 5 6 8 9 10 13 14 15 ...
  ..- attr(*, "names")= chr  "1" "3" "5" "6" ...

CHANGING CATEGORICAL VALUES.

SURVIVAL ANALYSIS BETWEEN MALE AND FEMALE.

MALE AND FEMALE SURVIVED.

c

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Finalizing encoding... done!

ANIMATION PLOT

SURVIVED ACCORDING TO THE PCLASS.

ggplot(data = Titanic[!(is.na(Titanic[1:LT,]$Age)),],aes(x=Age,fill=Survived))+geom_histogram(binwidth =3)

Length of logical index must be 1 or 183, not 131

SURVIED GRAPH CONCERN ON THE AGE.

USING CATOOLS TEST AND TRAIN THE DATA.

summary(Titanic_eq)

Call:
glm(formula = Survived ~ ., family = "binomial", data = Train)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7848  -0.8345   0.3057   0.7990   1.9862  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  5.120993   1.416525   3.615   0.0003 ***
Pclass      -0.943987   0.525901  -1.795   0.0727 .  
Sexmale     -2.781995   0.601230  -4.627 3.71e-06 ***
Age         -0.041134   0.017174  -2.395   0.0166 *  
SibSp        0.124778   0.399146   0.313   0.7546    
Parch       -0.366017   0.379845  -0.964   0.3352    
Fare         0.002436   0.003245   0.751   0.4528    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 167.22  on 130  degrees of freedom
Residual deviance: 123.17  on 124  degrees of freedom
AIC: 137.17

Number of Fisher Scoring iterations: 5

THE MODEL OF THE DATA USING GLM MODEL.

Test_pred

         1          2          3          4          5          6          7 
0.33183508 0.28579938 0.18336024 0.18202548 0.55903832 0.65583572 0.53175246 
         8          9         10         11         12         13         14 
0.97232287 0.59590801 0.89549779 0.95463425 0.51347563 0.95905464 0.85803528 
        15         16         17         18         19         20         21 
0.97684511 0.91351204 0.96746538 0.93201531 0.39959060 0.94364989 0.90699345 
        22         23         24         25         26         27         28 
0.96638574 0.97421048 0.14311553 0.12607187 0.55689872 0.39066676 0.95611654 
        29         30         31         32         33         34         35 
0.94836817 0.49455410 0.89494963 0.89445792 0.92591525 0.94447147 0.24576261 
        36         37         38         39         40         41         42 
0.94811779 0.97077612 0.96638574 0.38320457 0.61569467 0.09957376 0.43325304 
        43         44         45         46         47         48         49 
0.95962013 0.97065871 0.17470205 0.95392464 0.92804777 0.42452603 0.95579534 
        50         51         52 
0.92649941 0.56041957 0.84684367 

PREDCTION FOR THE ANALYSIS.

sum(diag(T))/sum(T)

[1] 0.8076923

AS PER MY ANALYSIS I PROVE THAT ITS 80% ACCURATE.

ROCR GRAPH TO CHECK THE THRESHOLD.