Introduction

This is my first work of machine learning. the notebook is written in python and has inspired from "Exploring Survival on Titanic" by Megan Risdal, a Kernel in R on Kaggle.



In [ ]:

    
%matplotlib inline
import numpy as np
import pandas as pd
import re as re

train = pd.read_csv('../input/train.csv', header = 0, dtype={'Age': np.float64})
test  = pd.read_csv('../input/test.csv' , header = 0, dtype={'Age': np.float64})
full_data = [train, test]

print (train.info())

Feature Engineering

1. Pclass

there is no missing value on this feature and already a numerical value. so let's check it's impact on our train set.



In [ ]:

    
print (train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean())

2. Sex



In [ ]:

    
print (train[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean())

3. SibSp and Parch

With the number of siblings/spouse and the number of children/parents we can create new feature called Family Size.



In [ ]:

    
for dataset in full_data:
    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
print (train[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean())

it seems has a good effect on our prediction but let's go further and categorize people to check whether they are alone in this ship or not.



In [ ]:

    
for dataset in full_data:
    dataset['IsAlone'] = 0
    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
print (train[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean())

good! the impact is considerable.

4. Embarked

the embarked feature has some missing value. and we try to fill those with the most occurred value ( 'S' ).



In [ ]:

    
for dataset in full_data:
    dataset['Embarked'] = dataset['Embarked'].fillna('S')
print (train[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean())

5. Fare

Fare also has some missing value and we will replace it with the median. then we categorize it into 4 ranges.



In [ ]:

    
for dataset in full_data:
    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())
train['CategoricalFare'] = pd.qcut(train['Fare'], 4)
print (train[['CategoricalFare', 'Survived']].groupby(['CategoricalFare'], as_index=False).mean())

6. Age

we have plenty of missing values in this feature. # generate random numbers between (mean - std) and (mean + std). then we categorize age into 5 range.



In [ ]:

    
for dataset in full_data:
    age_avg 	   = dataset['Age'].mean()
    age_std 	   = dataset['Age'].std()
    age_null_count = dataset['Age'].isnull().sum()
    
    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)
    dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list
    dataset['Age'] = dataset['Age'].astype(int)
    
train['CategoricalAge'] = pd.cut(train['Age'], 5)

print (train[['CategoricalAge', 'Survived']].groupby(['CategoricalAge'], as_index=False).mean())

7. Name

inside this feature we can find the title of people.



In [ ]:

    
def get_title(name):
	title_search = re.search(' ([A-Za-z]+)\.', name)
	# If the title exists, extract and return it.
	if title_search:
		return title_search.group(1)
	return ""

for dataset in full_data:
    dataset['Title'] = dataset['Name'].apply(get_title)

print(pd.crosstab(train['Title'], train['Sex']))

so we have titles. let's categorize it and check the title impact on survival rate.



In [ ]:

    
for dataset in full_data:
    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
 	'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')

    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')

print (train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean())

Data Cleaning

great! now let's clean our data and map our features into numerical values.



In [ ]:

    
for dataset in full_data:
    # Mapping Sex
    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)
    
    # Mapping titles
    title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
    dataset['Title'] = dataset['Title'].map(title_mapping)
    dataset['Title'] = dataset['Title'].fillna(0)
    
    # Mapping Embarked
    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)
    
    # Mapping Fare
    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] 						        = 0
    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1
    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2
    dataset.loc[ dataset['Fare'] > 31, 'Fare'] 							        = 3
    dataset['Fare'] = dataset['Fare'].astype(int)
    
    # Mapping Age
    dataset.loc[ dataset['Age'] <= 16, 'Age'] 					       = 0
    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1
    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2
    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3
    dataset.loc[ dataset['Age'] > 64, 'Age']                           = 4

# Feature Selection
drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp',\
                 'Parch', 'FamilySize']
train = train.drop(drop_elements, axis = 1)
train = train.drop(['CategoricalAge', 'CategoricalFare'], axis = 1)

test  = test.drop(drop_elements, axis = 1)

print (train.head(10))

train = train.values
test  = test.values

good! now we have a clean dataset and ready to predict. let's find which classifier works better on this dataset.

Classifier Comparison



In [ ]:

    
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.metrics import accuracy_score, log_loss
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression

classifiers = [
    KNeighborsClassifier(3),
    SVC(probability=True),
    DecisionTreeClassifier(),
    RandomForestClassifier(),
	AdaBoostClassifier(),
    GradientBoostingClassifier(),
    GaussianNB(),
    LinearDiscriminantAnalysis(),
    QuadraticDiscriminantAnalysis(),
    LogisticRegression()]

log_cols = ["Classifier", "Accuracy"]
log 	 = pd.DataFrame(columns=log_cols)

sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=0)

X = train[0::, 1::]
y = train[0::, 0]

acc_dict = {}

for train_index, test_index in sss.split(X, y):
	X_train, X_test = X[train_index], X[test_index]
	y_train, y_test = y[train_index], y[test_index]
	
	for clf in classifiers:
		name = clf.__class__.__name__
		clf.fit(X_train, y_train)
		train_predictions = clf.predict(X_test)
		acc = accuracy_score(y_test, train_predictions)
		if name in acc_dict:
			acc_dict[name] += acc
		else:
			acc_dict[name] = acc

for clf in acc_dict:
	acc_dict[clf] = acc_dict[clf] / 10.0
	log_entry = pd.DataFrame([[clf, acc_dict[clf]]], columns=log_cols)
	log = log.append(log_entry)

plt.xlabel('Accuracy')
plt.title('Classifier Accuracy')

sns.set_color_codes("muted")
sns.barplot(x='Accuracy', y='Classifier', data=log, color="b")

Prediction

now we can use SVC classifier to predict our data.



In [ ]:

    
candidate_classifier = SVC()
candidate_classifier.fit(train[0::, 1::], train[0::, 0])
result = candidate_classifier.predict(test)