Here we got the data dictionary. This useful info will help you to understand the csv.
| Variable | Definition | Key |
|---|---|---|
| survival | Survival | 0 = No, 1 = Yes |
| pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd |
| sex | Sex | |
| Age | Age in years | |
| sibsp | # of siblings / spouses aboard the Titanic | |
| parch | # of parents / children aboard the Titanic | |
| ticket | Ticket number | |
| fare | Passenger fare | |
| cabin | Cabin number | |
| embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |
pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower
age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)
parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.
Please check https://www.kaggle.com/c/titanic/data
Let's waggle the data!!!
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%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('ggplot')
# Anaconda on Windows will get warning
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df=pd.read_csv('train.csv')
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df.head()
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df.hist( figsize=(16, 10))
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df.describe()
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df.keys()
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df2=df.drop(['Name','PassengerId','Cabin','Ticket'],1)
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df2.head()
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code={'female':0,'male':1}
for k,i in df2.iterrows():
df2.loc[k,"sexc"]=code[i['Sex']]
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df2.head()
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df2["Embarked"].value_counts()
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code={'C':0,'Q':1, 'S':2}
for k,i in df2.iterrows():
if i['Embarked']==i['Embarked']:
df2.loc[k,"embarkedc"]=code[i['Embarked']]
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df2.head(10)
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#remove the original column
df3=df2.drop(['Sex','Embarked'],1)
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df4=df3.dropna(axis=0)
df4.head(10)
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import numpy as np
A=df4.as_matrix([df4.columns[:]])
X=df4.as_matrix([df4.columns[[2,5]]])
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#X[:,1]=np.log(X[:,1]+1)
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from sklearn.cluster import DBSCAN
db = DBSCAN(eps=1.5, min_samples=20).fit(X)
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db.labels_.max()
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ax=plt.figure(figsize=(12,9))
#plt.rcParams['figure.figsize'] = (12,9)
plt.xlabel("Age")
plt.ylabel("log(Fare+1)")
plt.title("Titanic")
plt.yscale('log')
color_list=['r','g','b','c','m','y','k']
for i in xrange( -1,db.labels_.max()+1 ):
is_survive=(A[:,0]==1) & (db.labels_==i)
plt.scatter(X[is_survive,0], X[is_survive,1], marker='o',color=color_list[i])
is_not_survive=(A[:,0]==0) & (db.labels_==i)
plt.scatter(X[is_not_survive,0], X[is_not_survive,1], marker='x',color=color_list[i])
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