Titanic Dataset¶

Get the Data¶

Titanic Data

import pandas as pd
train_data = pd.read_csv('./Titanic/train.csv')
test_data = pd.read_csv('./Titanic/test.csv')

train_data.head()

train_data.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

Ignore the Name and Ticket attributes
The Age, Cabin and Embarked attributes are sometimes null

train_data.describe()

train_data["Survived"].value_counts()

0    549
1    342
Name: Survived, dtype: int64

train_data["Pclass"].value_counts()

3    491
1    216
2    184
Name: Pclass, dtype: int64

train_data["Sex"].value_counts()

male      577
female    314
Name: Sex, dtype: int64

train_data["Embarked"].value_counts()

S    644
C    168
Q     77
Name: Embarked, dtype: int64

Prepare Training Set and Test Set¶

# Process the numerical attributes
from sklearn.base import BaseEstimator, TransformerMixin

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names]
    
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer

num_pipeline = Pipeline([
        ("select_numeric", DataFrameSelector(["Age", "SibSp", "Parch", "Fare"])),
        ("imputer", SimpleImputer(strategy="median")), # apply imputer to all the numerical attributes
    ])

# Process the categorical attributes
class MostFrequentImputer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],
                                        index=X.columns)
        return self
    def transform(self, X, y=None):
        return X.fillna(self.most_frequent_)
    
from sklearn.preprocessing import OneHotEncoder

cat_pipeline = Pipeline([
        ("select_cat", DataFrameSelector(["Pclass", "Sex", "Embarked"])),
        ("imputer", MostFrequentImputer()),
        ("cat_encoder", OneHotEncoder(sparse=False)),
    ])

# Join the numerical and categorical pipelines
from sklearn.pipeline import FeatureUnion

preprocess_pipeline = FeatureUnion(transformer_list=[
        ("num_pipeline", num_pipeline),
        ("cat_pipeline", cat_pipeline),
    ])

X_train = preprocess_pipeline.fit_transform(train_data)
y_train = train_data["Survived"]

Select and Train a Model¶

from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

svm_clf = SVC(gamma="auto")
svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)

forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)
forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)

svm_scores.mean(), forest_scores.mean()

(0.7365250822835092, 0.8149526160481217)

Train a Final Model¶

final_model = RandomForestClassifier(n_estimators=100, random_state=42)
final_model.fit(X_train, y_train)

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
                       max_depth=None, max_features='auto', max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=42, verbose=0,
                       warm_start=False)

Predict Test Set¶

X_test = preprocess_pipeline.transform(test_data)
y_pred = final_model.predict(X_test)

Generate Submission for Kaggle¶

s = pd.Series(y_pred, name='Survived')
frame = { 'PassengerId': test_data['PassengerId'], 'Survived': s }
result = pd.DataFrame(frame)
result.to_csv('Titanic.csv', index=False)

	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