In [1]:

    

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier


def read_and_clean(filename):
    df = pd.read_csv(filename, header=0)
    # Embarked: Fill NaN with most common value (here: 'S')
    embarked_fill = df['Embarked'].value_counts().index[0]
    df['Embarked'] = df['Embarked'].fillna(embarked_fill)
    # Embarked: Convert to numerical value
    df['Embarked'] = df['Embarked'].map({'C': 0, 'S': 1, 'Q': 2}).astype(int)
    # Same for Sex:
    df['Sex'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)
    # Age: Fill NaN with median age in each PClass with respect to Sex:
    # TODO: Include title information from Name as indicator for child/grown-up
    for sex in range(2):
        for pclass in range(1, 4):
            df.loc[(df['Age'].isnull()) &
                   (df['Sex'] == sex) &
                   (df['Pclass'] == pclass), 'Age'] = \
                df[(df['Sex'] == sex) &
                   (df['Pclass'] == pclass)]['Age'].dropna().median()
    # Similar for Fare, but also including Embarked:
    # TODO: Should also include binned age
    for sex in range(2):
        for pclass in range(1, 4):
            for embarked in range(3):
                df.loc[(df['Fare'].isnull()) &
                       (df['Sex'] == sex) &
                       (df['Pclass'] == pclass) &
                       (df['Embarked'] == embarked), 'Fare'] = \
                    df[(df['Sex'] == sex) &
                       (df['Pclass'] == pclass) &
                       (df['Embarked'] == embarked)]['Fare'].dropna().median()
    # TODO: Convert Age to categorical values
    # print df['Age'].value_counts(dropna=False)
    # print df['Age'].value_counts(bins=10, sort=False)
    # TODO: Create categorical 'Title' variable derived from Name
    # Drop non-numeric, non-categorical data
    df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)
    # Extract passenger ids
    ids = df['PassengerId'].values
    df = df.drop('PassengerId', axis=1)
    return df, ids


    

In [2]:

    

# Training data
df_train, _ = read_and_clean('train.csv')
df_train.info()
train = df_train.values
forest = RandomForestClassifier(n_estimators=100)
fit = forest.fit(train[:, 1:], train[:, 0])


    

    




<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 8 columns):
Survived    891 non-null int64
Pclass      891 non-null int64
Sex         891 non-null int64
Age         891 non-null float64
SibSp       891 non-null int64
Parch       891 non-null int64
Fare        891 non-null float64
Embarked    891 non-null int64
dtypes: float64(2), int64(6)
memory usage: 62.6 KB

In [3]:

    

# Test data
df_test, ids = read_and_clean('test.csv')
df_test.info()


    

    




<class 'pandas.core.frame.DataFrame'>
Int64Index: 418 entries, 0 to 417
Data columns (total 7 columns):
Pclass      418 non-null int64
Sex         418 non-null int64
Age         418 non-null float64
SibSp       418 non-null int64
Parch       418 non-null int64
Fare        418 non-null float64
Embarked    418 non-null int64
dtypes: float64(2), int64(5)
memory usage: 26.1 KB

In [4]:

    

# Prediction
prediction = np.array(fit.predict(df_test.values), dtype=int)
out = np.stack((np.array(ids, dtype=int), prediction), axis=-1)
np.savetxt('prediction.csv', out, fmt='%d', delimiter=',',
           header='PassengerId,Survived', comments='')