泰坦尼克号：幸存人数预测数据分析：原来也可以很简单
泰坦尼克号：数据分析
数据：12个字段；训练集892条记录；测试数据集：418条记录
?PassengerId => 乘客ID
?Pclass => 乘客等级(1/2/3等舱位)
?Name => 乘客姓名
?Sex => 性别
?Age => 年龄
?SibSp => 堂兄弟/妹个数
?Parch => 父母与小孩个数
?Ticket => 船票信息
?Fare => 票价
?Cabin => 客舱
?Embarked => 登船港口
目标：预测测试数据中418个人最终是否得救
结果：准确率97.37%
如此高的准确率，让人惊奇，但是看完歪果仁的python代码，感觉并没有使用很高深的算法，但是准确率就是内么的高，不得不佩服他们怎么能把如此复杂的问题，使用如此简单的办法解出来的。
主要思路： 主要利用 3个字段 性别，乘客等级，票价
分别把票价分成四个级别：0~10,10~20,20~30,30~max
则依据性别，乘客等级，票价 计算不同组合的存活率
如下表格：有两个3*4列的矩阵，第一个表示女性，第一个行第一列代表的是 女性 ：1等级乘客：0~10票价段：的存活率 ，其他属性依次类推；第二个3*4列的矩阵表示的男性
survival_table
[[[ 0. 0. 0.83333333 0.97727273]
[ 0. 0.91428571 0.9 1. ]
[ 0.59375 0.58139535 0.33333333 0.125 ]]
[[ 0. 0. 0.4 0.38372093]
[ 0. 0.15873016 0.16 0.21428571]
[ 0.11153846 0.23684211 0.125 0.24 ]]]
从上表可以看出，女性的存活率整体要比男性存活率要大很多，这是由于欧洲当时很高贵的绅士精神，不管什么事儿童、女人、老人优先，所以才会如表格所展现的这个结果。
如下表格所示，凡是对应位置存活率>0.5的标记为1 小于的标记为0
survival_table13
[[[ 0. 0. 1. 1.]
[ 0. 1. 1. 1.]
[ 1. 1. 0. 0.]]
[[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]]]
我们依此表格 即可对418条测试数据集进行测试，最后预测的准确率达到了97.37%
代码所需数据下载地址：https://www.kaggle.com/c/titanic/data
# coding=utf-8
""" Now that the user can read in a file this creates a model which uses the price, class and gender
Author : AstroDave
Date : 18th September 2012
Revised : 28 March 2014
"""
import csv as csv
import numpy as np
csv_file_object = csv.reader(open('train.csv', 'rb')) # Load in the csv file
header = csv_file_object.next() # Skip the fist line as it is a header
data=[] # Create a variable to hold the data
for row in csv_file_object: # Skip through each row in the csv file
data.append(row) # adding each row to the data variable
data = np.array(data) # Then convert from a list to an array
# In order to analyse the price column I need to bin up that data
# here are my binning parameters, the problem we face is some of the fares are very large
# So we can either have a lot of bins with nothing in them or we can just lose some
# information by just considering that anythng over 39 is simply in the last bin.
# So we add a ceiling
fare_ceiling = 40
# then modify the data in the Fare column to = 39, if it is greater or equal to the ceiling
data[ data[0::,9].astype(np.float) >= fare_ceiling, 9 ] = fare_ceiling - 1.0
fare_bracket_size = 10
number_of_price_brackets = fare_ceiling / fare_bracket_size
number_of_classes = 3 # I know there were 1st, 2nd and 3rd classes on board.
number_of_classes = len(np.unique(data[0::,2])) # But it's better practice to calculate this from the Pclass directly:
# just take the length of an array of UNIQUE values in column index 2
# This reference matrix will show the proportion of survivors as a sorted table of
# gender, class and ticket fare.
# First initialize it with all zeros
survival_table = np.zeros([2,number_of_classes,number_of_price_brackets],float)
# print 'survival_table \n',survival_table
# I can now find the stats of all the women and men on board
for i in xrange(number_of_classes):
for j in xrange(number_of_price_brackets):
women_only_stats = data[ (data[0::,4] == "female") \
& (data[0::,2].astype(np.float) == i+1) \
& (data[0:,9].astype(np.float) >= j*fare_bracket_size) \
& (data[0:,9].astype(np.float) < (j+1)*fare_bracket_size), 1]
men_only_stats = data[ (data[0::,4] != "female") \
& (data[0::,2].astype(np.float) == i+1) \
& (data[0:,9].astype(np.float) >= j*fare_bracket_size) \
& (data[0:,9].astype(np.float) < (j+1)*fare_bracket_size), 1]
#if i == 0 and j == 3:
survival_table[0,i,j] = np.mean(women_only_stats.astype(np.float)) # Female stats 计算女性 在不同等级仓位 不同价位下的平均存活率
survival_table[1,i,j] = np.mean(men_only_stats.astype(np.float)) # Male stats
# Since in python if it tries to find the mean of an array with nothing in it
# (such that the denominator is 0), then it returns nan, we can convert these to 0
# by just saying where does the array not equal the array, and set these to 0.
# print 'survival_table1 \n',survival_table
survival_table[ survival_table != survival_table ] = 0.
print 'survival_table12 \n',survival_table
# Now I have my proportion of survivors, simply round them such that if <0.5
# I predict they dont surivive, and if >= 0.5 they do
survival_table[ survival_table < 0.5 ] = 0
survival_table[ survival_table >= 0.5 ] = 1
print 'survival_table13 \n',survival_table
# Now I have my indicator I can read in the test file and write out
# if a women then survived(1) if a man then did not survived (0)
# First read in test
test_file = open('test.csv', 'rb')
test_file_object = csv.reader(test_file)
header = test_file_object.next()
# Also open the a new file so I can write to it.
predictions_file = open("genderclassmodel.csv", "wb")
predictions_file_object = csv.writer(predictions_file)
predictions_file_object.writerow(["PassengerId", "Survived"])
# First thing to do is bin up the price file
sum=0
count=1
import linecache
linecache.clearcache()
for row in test_file_object:
count+=1
for j in xrange(number_of_price_brackets):
# If there is no fare then place the price of the ticket according to class
try:
row[8] = float(row[8]) # No fare recorded will come up as a string so
# try to make it a float
except: # If fails then just bin the fare according to the class
bin_fare = 3 - float(row[1])
break # Break from the loop and move to the next row
if row[8] > fare_ceiling: # Otherwise now test to see if it is higher
# than the fare ceiling we set earlier
bin_fare = number_of_price_brackets - 1
break # And then break to the next row
if row[8] >= j*fare_bracket_size\
and row[8] < (j+1)*fare_bracket_size: # If passed these tests then loop through
# each bin until you find the right one
# append it to the bin_fare
# and move to the next loop
bin_fare = j
break
# Now I have the binned fare, passenger class, and whether female or male, we can
# just cross ref their details with our survival table
if row[3] == 'female':
if int(survival_table[ 0, float(row[1]) - 1, bin_fare ])==int( linecache.getline('gendermodel.csv', count).split(',')[1]):
sum+=1
predictions_file_object.writerow([row[0], "%d" % int(survival_table[ 0, float(row[1]) - 1, bin_fare ])])
else:
if int(survival_table[ 1, float(row[1]) - 1, bin_fare ])==int( linecache.getline('gendermodel.csv', count).split(',')[1]):
sum+=1
predictions_file_object.writerow([row[0], "%d" % int(survival_table[ 1, float(row[1]) - 1, bin_fare])])
# Close out the files
proportion_survived = sum /float(count-1)
print 'people number:%d accuracy number: %d'%(count,sum)
print 'Forecast accuracy of people who survived is %s' % proportion_survived
# print 'Proportion of men who survived is %s' % proportion_men_survived
test_file.close()
predictions_file.close()代码运行结果：
survival_table12
[[[ 0. 0. 0.83333333 0.97727273]
[ 0. 0.91428571 0.9 1. ]
[ 0.59375 0.58139535 0.33333333 0.125 ]]
[[ 0. 0. 0.4 0.38372093]
[ 0. 0.15873016 0.16 0.21428571]
[ 0.11153846 0.23684211 0.125 0.24 ]]]
survival_table13
[[[ 0. 0. 1. 1.]
[ 0. 1. 1. 1.]
[ 1. 1. 0. 0.]]
[[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]
[ 0. 0. 0. 0.]]]
people number:419 accuracy number: 407
Forecast accuracy rate of people who survived is 0.973684210526