簡介

Titanic是Kaggle競賽的一道入門題，參賽者需要根據旅客的階級、性別、年齡、船艙種類等信息預測其是否能在海難中生還，詳細信息可以參看https://www.kaggle.com/，本文的分析代碼也取自 kaggle 中該競賽的 kernal。

數據介紹

給出的數據格式如下：

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

數據項的含義如下：

PassengerId：乘客ID

Survived：是否生還，0表示遇難，1表示生還

Pclass：階級，1表示最高階級，3最低

Name：姓名

Sex：性別

Age：年齡

SibSp：同乘船的兄弟姐妹的數量

Parch：是否有配偶同乘，1表示是

Ticket：船票編號

Fare：恐懼指數

Cabin：船艙號

Embarked：登船港口

問題分析

這是一個比較典型的基于特征的分類問題，根據一般的數據處理流程可以將問題的求解分解成為以下步驟：

數據預處理

讀取數據，在本文代碼中使用了 python 的 pandas 包管理數據結構

特征向量化，在本文代碼中將性別和登船港口特征轉成向量化表示

處理殘缺數據，在本文代碼中將殘缺年齡用平均年齡表示，殘缺的登船港口用頻繁項表示

扔掉多余項，姓名、ID、艙號、票號在本問題中被認為是對分類沒有幫助的信息，扔掉了這些特征項

數據訓練

在本文代碼中使用了 sklearn 中的隨機森林進行分類，隨機森林每次隨機選取若干特征和數據項生成決策樹，最后采用投票的方式來生成預測結果，本文代碼中將第一列作為分類項，后n列作為特征項，隨機生成100棵決策樹對數據進行訓練

預測并生成結果

代碼實現

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.'

后續思考

這是一個比較簡單流程也較為完整的解決方案，但是也存在一些問題，比如

沒有對測試結果的準確率和召回率進行評估

模型的參數選擇是否可以進一步調整取得更好的效果？

如果采用一些集成學習的辦法效果會不會進一步提升？

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