Titanic ML Kernel

Author: Rishu Shrivastava, Babu Sivaprakasam
URL: https://www.kaggle.com/c/titanic
Date: 08 April 2018

Importing Scikit Learn Libraries



In [2]:

    
import pandas as pd
import numpy as np

Loading the Titanic dataset from downloaded CSV file



In [9]:

    
data_path = "C:/Users/Rishu/Desktop/dATA/titanic/"

titanic_train = pd.read_csv(data_path+"train.csv")

titanic_train.head()









    Out[9]:






  
    
      
      PassengerId
      Survived
      Pclass
      Name
      Sex
      Age
      SibSp
      Parch
      Ticket
      Fare
      Cabin
      Embarked
    
  
  
    
      0
      1
      0
      3
      Braund, Mr. Owen Harris
      male
      22.0
      1
      0
      A/5 21171
      7.2500
      NaN
      S
    
    
      1
      2
      1
      1
      Cumings, Mrs. John Bradley (Florence Briggs Th...
      female
      38.0
      1
      0
      PC 17599
      71.2833
      C85
      C
    
    
      2
      3
      1
      3
      Heikkinen, Miss. Laina
      female
      26.0
      0
      0
      STON/O2. 3101282
      7.9250
      NaN
      S
    
    
      3
      4
      1
      1
      Futrelle, Mrs. Jacques Heath (Lily May Peel)
      female
      35.0
      1
      0
      113803
      53.1000
      C123
      S
    
    
      4
      5
      0
      3
      Allen, Mr. William Henry
      male
      35.0
      0
      0
      373450
      8.0500
      NaN
      S

Understanding the Dataset

Information about the train data



In [8]:

    
titanic_train.info()









    



<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

Finding the number of features having NULL or NaN in the dataset



In [5]:

    
titanic_train.isnull().sum()









    Out[5]:





PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

Getting a small insight into the measurable features



In [6]:

    
titanic_train.describe()









    Out[6]:






  
    
      
      PassengerId
      Survived
      Pclass
      Age
      SibSp
      Parch
      Fare
    
  
  
    
      count
      891.000000
      891.000000
      891.000000
      714.000000
      891.000000
      891.000000
      891.000000
    
    
      mean
      446.000000
      0.383838
      2.308642
      29.699118
      0.523008
      0.381594
      32.204208
    
    
      std
      257.353842
      0.486592
      0.836071
      14.526497
      1.102743
      0.806057
      49.693429
    
    
      min
      1.000000
      0.000000
      1.000000
      0.420000
      0.000000
      0.000000
      0.000000
    
    
      25%
      223.500000
      0.000000
      2.000000
      20.125000
      0.000000
      0.000000
      7.910400
    
    
      50%
      446.000000
      0.000000
      3.000000
      28.000000
      0.000000
      0.000000
      14.454200
    
    
      75%
      668.500000
      1.000000
      3.000000
      38.000000
      1.000000
      0.000000
      31.000000
    
    
      max
      891.000000
      1.000000
      3.000000
      80.000000
      8.000000
      6.000000
      512.329200

Fixing the noise in the dataset

Feature: Age



In [20]:

    
mean_age = titanic_train['Age'].mean()
titanic_train['Age'] = titanic_train['Age'].fillna(mean_age)
#return titanic_train

Finding the mean age of all the passengers excluding the NaN. Next assigning the null values with the Mean Age.

Feature: Cabin



In [27]:

    
#print(titanic_train['Cabin'].head())

titanic_train['Cabin'] = titanic_train['Cabin'].fillna('N')
titanic_train['Cabin'] = titanic_train['Cabin'].apply(lambda x: x[0])

For the Cabin feature, firstly filling the Null/NaN values with default: N. Since the cabin data is also having noise in the rest of the data, splitting only the first character of the data using lamda function. This will be bring a consistency to the data set

Feature: Embarked



In [ ]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200