Classification and Prediction

# coding: utf-8

# In[128]:

get_ipython().magic(u'matplotlib inline')
import pandas as pd
from pandas import Series,DataFrame
import seaborn as sns
sns.set_style('whitegrid')
pd.set_option('display.mpl_style', 'default')
import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

train_df= pd.read_csv("/home/lpstudy/下载/train.csv")
test_df = pd.read_csv("/home/lpstudy/下载/test.csv")

train_df.head()  

test_df.head()

# In[129]:

train_df = train_df.drop(["Ticket","PassengerId","Name"],axis = 1)
test_df = test_df.drop(["Name","Ticket"],axis =1)

# In[130]:

train_df.head()

# In[131]:

train_df["Embarked"] = train_df["Embarked"].fillna("S")
#plot
sns.factorplot("Embarked","Survived",data = train_df,size = 6,aspect = 2)

fig,(axis1,axis2,axis3) = plt.subplots(1,3,figsize = (15,5))

sns.countplot(x='Embarked', data=train_df, ax=axis1)
sns.countplot(x='Survived', hue="Embarked", data=train_df, order=[1,0], ax=axis2)

embark_perc = train_df[["Embarked", "Survived"]].groupby(['Embarked'],as_index=False).mean()
sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)

embark_dummies_train  = pd.get_dummies(train_df['Embarked'])
embark_dummies_train.drop(['S'], axis=1, inplace=True)

embark_dummies_test  = pd.get_dummies(test_df['Embarked'])
embark_dummies_test.drop(['S'], axis=1, inplace=True)

train_df = train_df.join(embark_dummies_train)
test_df    = test_df.join(embark_dummies_test)

train_df.drop(['Embarked'], axis=1,inplace=True)
test_df.drop(['Embarked'], axis=1,inplace=True)

# In[132]:

test_df["Fare"].fillna(test_df["Fare"].median(), inplace=True)

train_df['Fare'] = train_df['Fare'].astype(int)
test_df['Fare']    = test_df['Fare'].astype(int)

fare_not_survived = train_df["Fare"][train_df["Survived"] == 0]
fare_survived     = train_df["Fare"][train_df["Survived"] == 1]

avgerage_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()])
std_fare      = DataFrame([fare_not_survived.std(), fare_survived.std()])

#plot
train_df['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,50))

avgerage_fare.index.names = std_fare.index.names = ["Survived"]
avgerage_fare.plot(yerr=std_fare,kind='bar',legend=False)

# In[133]:

# Age 

fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
axis1.set_title('Original Age values - Titanic')
axis2.set_title('New Age values - Titanic')

average_age_titanic   = train_df["Age"].mean()
std_age_titanic       = train_df["Age"].std()
count_nan_age_titanic = train_df["Age"].isnull().sum()

# get average, std, and number of NaN values in test_df
average_age_test   = test_df["Age"].mean()
std_age_test       = test_df["Age"].std()
count_nan_age_test = test_df["Age"].isnull().sum()

# generate random numbers between (mean - std) & (mean + std)
rand_1 = np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic, size = count_nan_age_titanic)
rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)

# plot original Age values
# NOTE: drop all null values, and convert to int
train_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)
# test_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)

# fill NaN values in Age column with random values generated
train_df["Age"][np.isnan(train_df["Age"])] = rand_1
test_df["Age"][np.isnan(test_df["Age"])] = rand_2

# convert from float to int
train_df['Age'] = train_df['Age'].astype(int)
test_df['Age']    = test_df['Age'].astype(int)

# plot new Age Values
train_df['Age'].hist(bins=70, ax=axis2)
# test_df['Age'].hist(bins=70, ax=axis4)

# In[134]:

# .... continue with plot Age column

# peaks for survived/not survived passengers by their age
facet = sns.FacetGrid(train_df, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, train_df['Age'].max()))
facet.add_legend()

# average survived passengers by age
fig, axis1 = plt.subplots(1,1,figsize=(18,4))
average_age = train_df[["Age", "Survived"]].groupby(['Age'],as_index=False).mean()
sns.barplot(x='Age', y='Survived', data=average_age)

# In[135]:

# Cabin
# It has a lot of NaN values, so it won't cause a remarkable impact on prediction
train_df.drop("Cabin",axis=1,inplace=True)
test_df.drop("Cabin",axis=1,inplace=True)

# Family

# Instead of having two columns Parch & SibSp,
# we can have only one column represent if the passenger had any family member aboard or not,
# Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not.
train_df['Family'] =  train_df["Parch"] + train_df["SibSp"]
train_df['Family'].loc[train_df['Family'] > 0] = 1
train_df['Family'].loc[train_df['Family'] == 0] = 0

test_df['Family'] =  test_df["Parch"] + test_df["SibSp"]
test_df['Family'].loc[test_df['Family'] > 0] = 1
test_df['Family'].loc[test_df['Family'] == 0] = 0

# drop Parch & SibSp
train_df = train_df.drop(['SibSp','Parch'], axis=1)
test_df    = test_df.drop(['SibSp','Parch'], axis=1)

# plot
fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5))

# sns.factorplot('Family',data=train_df,kind='count',ax=axis1)
sns.countplot(x='Family', data=train_df, order=[1,0], ax=axis1)

# average of survived for those who had/didn't have any family member
family_perc = train_df[["Family", "Survived"]].groupby(['Family'],as_index=False).mean()
sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)

axis1.set_xticklabels(["With Family","Alone"], rotation=0)

# In[136]:

# Sex

# As we see, children(age < ~16) on aboard seem to have a high chances for Survival.
# So, we can classify passengers as males, females, and child
def get_person(passenger):
    age,sex = passenger
    return 'child' if age < 16 else sex

train_df['Person'] = train_df[['Age','Sex']].apply(get_person,axis=1)
test_df['Person']    = test_df[['Age','Sex']].apply(get_person,axis=1)

# No need to use Sex column since we created Person column
train_df.drop(['Sex'],axis=1,inplace=True)
test_df.drop(['Sex'],axis=1,inplace=True)

# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers
person_dummies_titanic  = pd.get_dummies(train_df['Person'])
person_dummies_titanic.columns = ['Child','Female','Male']
person_dummies_titanic.drop(['Male'], axis=1, inplace=True)

person_dummies_test  = pd.get_dummies(test_df['Person'])
person_dummies_test.columns = ['Child','Female','Male']
person_dummies_test.drop(['Male'], axis=1, inplace=True)

train_df = train_df.join(person_dummies_titanic)
test_df    = test_df.join(person_dummies_test)

fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))

# sns.factorplot('Person',data=train_df,kind='count',ax=axis1)
sns.countplot(x='Person', data=train_df, ax=axis1)

# average of survived for each Person(male, female, or child)
person_perc = train_df[["Person", "Survived"]].groupby(['Person'],as_index=False).mean()
sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child'])

train_df.drop(['Person'],axis=1,inplace=True)
test_df.drop(['Person'],axis=1,inplace=True)

# In[137]:

# Pclass

# sns.factorplot('Pclass',data=train_df,kind='count',order=[1,2,3])
sns.factorplot('Pclass','Survived',order=[1,2,3], data=train_df,size=5)

# create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengers
pclass_dummies_titanic  = pd.get_dummies(train_df['Pclass'])
pclass_dummies_titanic.columns = ['Class_1','Class_2','Class_3']
pclass_dummies_titanic.drop(['Class_3'], axis=1, inplace=True)

pclass_dummies_test  = pd.get_dummies(test_df['Pclass'])
pclass_dummies_test.columns = ['Class_1','Class_2','Class_3']
pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True)

train_df.drop(['Pclass'],axis=1,inplace=True)
test_df.drop(['Pclass'],axis=1,inplace=True)

train_df = train_df.join(pclass_dummies_titanic)
test_df    = test_df.join(pclass_dummies_test)

# In[139]:

# define training and testing sets

X_train = train_df.drop("Survived",axis=1)
Y_train = train_df["Survived"]
X_test  = test_df.drop("PassengerId",axis=1).copy()

# In[140]:

# Logistic Regression

logreg = LogisticRegression()

logreg.fit(X_train, Y_train)

Y_pred = logreg.predict(X_test)

logreg.score(X_train, Y_train)

# In[141]:

# Support Vector Machines

svc = SVC()

svc.fit(X_train, Y_train)

Y_pred = svc.predict(X_test)

svc.score(X_train, Y_train)

# In[142]:

# Random Forests

random_forest = RandomForestClassifier(n_estimators=100)

random_forest.fit(X_train, Y_train)

Y_pred = random_forest.predict(X_test)

random_forest.score(X_train, Y_train)

# In[143]:

# get Correlation Coefficient for each feature using Logistic Regression
coeff_df = DataFrame(train_df.columns.delete(0))
coeff_df.columns = ['Features']
coeff_df["Coefficient Estimate"] = pd.Series(logreg.coef_[0])

# preview
coeff_df

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