Predict survival on the Titanic using Excel, Python, R & Random Forests
1.Description
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
2.DataSet
VARIABLE DESCRIPTIONS:
survival Survival
(0 = No; 1 = Yes)
pclass Passenger Class
(1 = 1st; 2 = 2nd; 3 = 3rd)
name Name
sex Sex
age Age
sibsp Number of Siblings/Spouses Aboard
parch Number of Parents/Children Aboard
ticket Ticket Number
fare Passenger Fare
cabin Cabin
embarked Port of Embarkation
(C = Cherbourg; Q = Queenstown; S = Southampton)
3.Code（Python）
# Imports
import pandas as pd
import numpy as np
from pandas import Series,DataFrame
data_train = pd.read_csv("train.csv")
#print data_train.columns
#print data_train.info()
#print data_train.describe()
import matplotlib.pyplot as plt
fig = plt.figure()
fig.set(alpha=0.3)
plt.subplot2grid((2,3), (0,0))
data_train.Survived.value_counts().plot(kind='bar')
plt.title(u"Survive(1,Survived)")
plt.ylabel(u'Count')
#plt.show()
plt.subplot2grid((2,3), (0,1))
data_train.Pclass.value_counts().plot(kind='bar')
plt.ylabel(u'Count')
plt.title(u'Prank')
plt.subplot2grid((2,3), (0,2))
plt.scatter(data_train.Survived, data_train.Age)
plt.ylabel(u'Age')
plt.grid(b=True, which='major', axis='y')
plt.title(u'Survived by age(1, Survived)')
plt.subplot2grid((2,3), (1,0), colspan=2)
data_train.Age[data_train.Pclass == 1].plot(kind='kde')
data_train.Age[data_train.Pclass == 2].plot(kind='kde')
data_train.Age[data_train.Pclass == 3].plot(kind='kde')
plt.xlabel(u'Age')
plt.ylabel(u'density')
plt.title(u'Age of all Pclass')
plt.legend((u'class_1', u'class_2',u'class_3'), loc='best')
plt.subplot2grid((2,3), (1, 2))
data_train.Embarked.value_counts().plot(kind='bar')
plt.title(u'COunt of Embarked')
plt.ylabel(u'COunt')
#plt.show()
fig = plt.figure()
fig.set(alpha=0.2)
Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'Survived':Survived_1, 'unsurvived':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u'Pclass of all')
plt.xlabel(u'Survive of all')
plt.ylabel(u'Count')
#plt.show()
fig = plt.figure()
fig.set(alpha=0.2)
Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()
Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()
df = pd.DataFrame({u'Survived':Survived_1, u'Unsurvived':Survived_0})
df.plot(kind='bar', stacked=True)
plt.title(u'Survive of all Embarked')
plt.xlabel(u'Embarked')
plt.ylabel(u'Count')
#plt.show()
fig = plt.figure()
fig.set(alpha=0.2)
Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()
Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()
df = pd.DataFrame({u'male':Survived_m, u'female':Survived_f})
df.plot(kind='bar', stacked=True)
plt.title('Survive by Sex')
plt.xlabel('Count')
plt.show()
fig = plt.figure()
fig.set(alpha=0.65)
plt.title(u'Survive by Pclass and Sex')
ax1 = fig.add_subplot(141)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label="female highclass", color='#FA2479')
ax1.set_xticklabels([u'Survived', u'Unsurvived'], rotation=0)
ax1.legend([u'female/highclass'], loc='best')
ax2 = fig.add_subplot(142, sharey=ax1)
data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink')
ax2.set_xticklabels([u"Unsurvived", u"Survived"], rotation=0)
plt.legend([u"female/lowclass"], loc='best')