简介

Titanic是Kaggle竞赛的一道入门题，参赛者需要根据旅客的阶级、性别、年龄、船舱种类等信息预测其是否能在海难中生还，详细信息可以参看https://www.kaggle.com/，本文的分析代码也取自 kaggle 中该竞赛的 kernal。

数据介绍

给出的数据格式如下：

PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked

1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S

2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C

数据项的含义如下：

PassengerId：乘客ID

Survived：是否生还，0表示遇难，1表示生还

Pclass：阶级，1表示最高阶级，3最低

Name：姓名

Sex：性别

Age：年龄

SibSp：同乘船的兄弟姐妹的数量

Parch：是否有配偶同乘，1表示是

Ticket：船票编号

Fare：恐惧指数

Cabin：船舱号

Embarked：登船港口

问题分析

这是一个比较典型的基于特征的分类问题，根据一般的数据处理流程可以将问题的求解分解成为以下步骤：

数据预处理

读取数据，在本文代码中使用了 python 的 pandas 包管理数据结构

特征向量化，在本文代码中将性别和登船港口特征转成向量化表示

处理残缺数据，在本文代码中将残缺年龄用平均年龄表示，残缺的登船港口用频繁项表示

扔掉多余项，姓名、ID、舱号、票号在本问题中被认为是对分类没有帮助的信息，扔掉了这些特征项

数据训练

在本文代码中使用了 sklearn 中的随机森林进行分类，随机森林每次随机选取若干特征和数据项生成决策树，最后采用投票的方式来生成预测结果，本文代码中将第一列作为分类项，后n列作为特征项，随机生成100棵决策树对数据进行训练

预测并生成结果

代码实现

import pandas as pd

import numpy as np

import csv as csv

from sklearn.ensemble import RandomForestClassifier

# Data cleanup

# TRAIN DATA

train_df = pd.read_csv('train.csv', header=0) # Load the train file into a dataframe

# I need to convert all strings to integer classifiers.

# I need to fill in the missing values of the data and make it complete.

# female = 0, Male = 1

train_df['Gender'] = train_df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)

# Embarked from 'C', 'Q', 'S'

# Note this is not ideal: in translating categories to numbers, Port "2" is not 2 times greater than Port "1", etc.

# All missing Embarked -> just make them embark from most common place

if len(train_df.Embarked[ train_df.Embarked.isnull() ]) > 0:

train_df.Embarked[ train_df.Embarked.isnull() ] = train_df.Embarked.dropna().mode().values

Ports = list(enumerate(np.unique(train_df['Embarked']))) # determine all values of Embarked,

Ports_dict = { name : i for i, name in Ports } # set up a dictionary in the form Ports : index

train_df.Embarked = train_df.Embarked.map( lambda x: Ports_dict[x]).astype(int) # Convert all Embark strings to int

# All the ages with no data -> make the median of all Ages

median_age = train_df['Age'].dropna().median()

if len(train_df.Age[ train_df.Age.isnull() ]) > 0:

train_df.loc[ (train_df.Age.isnull()), 'Age'] = median_age

# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)

train_df = train_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'PassengerId'], axis=1)

# TEST DATA

test_df = pd.read_csv('test.csv', header=0) # Load the test file into a dataframe

# I need to do the same with the test data now, so that the columns are the same as the training data

# I need to convert all strings to integer classifiers:

# female = 0, Male = 1

test_df['Gender'] = test_df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)

# Embarked from 'C', 'Q', 'S'

# All missing Embarked -> just make them embark from most common place

if len(test_df.Embarked[ test_df.Embarked.isnull() ]) > 0:

test_df.Embarked[ test_df.Embarked.isnull() ] = test_df.Embarked.dropna().mode().values

# Again convert all Embarked strings to int

test_df.Embarked = test_df.Embarked.map( lambda x: Ports_dict[x]).astype(int)

# All the ages with no data -> make the median of all Ages

median_age = test_df['Age'].dropna().median()

if len(test_df.Age[ test_df.Age.isnull() ]) > 0:

test_df.loc[ (test_df.Age.isnull()), 'Age'] = median_age

# All the missing Fares -> assume median of their respective class

if len(test_df.Fare[ test_df.Fare.isnull() ]) > 0:

median_fare = np.zeros(3)

for f in range(0,3): # loop 0 to 2

median_fare[f] = test_df[ test_df.Pclass == f+1 ]['Fare'].dropna().median()

for f in range(0,3): # loop 0 to 2

test_df.loc[ (test_df.Fare.isnull()) & (test_df.Pclass == f+1 ), 'Fare'] = median_fare[f]

# Collect the test data's PassengerIds before dropping it

ids = test_df['PassengerId'].values

# Remove the Name column, Cabin, Ticket, and Sex (since I copied and filled it to Gender)

test_df = test_df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'PassengerId'], axis=1)

# The data is now ready to go. So lets fit to the train, then predict to the test!

# Convert back to a numpy array

train_data = train_df.values

test_data = test_df.values

print 'Training...'

forest = RandomForestClassifier(n_estimators=100)

forest = forest.fit( train_data[0::,1::], train_data[0::,0] )

print 'Predicting...'

output = forest.predict(test_data).astype(int)

predictions_file = open("myfirstforest.csv", "wb")

open_file_object = csv.writer(predictions_file)

open_file_object.writerow(["PassengerId","Survived"])

open_file_object.writerows(zip(ids, output))

predictions_file.close()

print 'Done.'

后续思考

这是一个比较简单流程也较为完整的解决方案，但是也存在一些问题，比如

没有对测试结果的准确率和召回率进行评估

模型的参数选择是否可以进一步调整取得更好的效果？

如果采用一些集成学习的办法效果会不会进一步提升？