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titanic.py
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titanic.py
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# pandas
import pandas as pd
from pandas import Series,DataFrame
# numpy, matplotlib
import numpy as np
import matplotlib.pyplot as plt
# machine learning
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
# get titanic & test csv files as a DataFrame
train_df = pd.read_csv("./data/train.csv")
test_df = pd.read_csv("./data/test.csv")
# preview the data
print train_df.head()
# data info
print "Data Information: \n"
print train_df.info()
print("----------------------------")
print test_df.info()
# drop unnecessary columns, these columns won't be useful in analysis and prediction
train_df = train_df.drop(["PassengerId", "Name", "Ticket"], axis=1)
test_df = test_df.drop(["Name", "Ticket"], axis=1)
# Embarked
# Either to consider Embarked column in predictions,
# and remove "S" dummy variable,
# and leave "C" & "Q", since they seem to have a good rate for Survival.
# OR, don't create dummy variables for Embarked column, just drop it,
# because logically, Embarked doesn't seem to be useful in prediction.
embark_dummies_titanic = pd.get_dummies(train_df['Embarked'])
embark_dummies_titanic.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_titanic)
test_df = test_df.join(embark_dummies_test)
train_df.drop(['Embarked'], axis=1, inplace=True)
test_df.drop(['Embarked'], axis=1, inplace=True)
# Fare
# only for test_df, since there is a missing "Fare" values
test_df["Fare"].fillna(test_df["Fare"].median(), inplace=True)
# convert from float to int
train_df['Fare'] = train_df['Fare'].astype(int)
test_df['Fare'] = test_df['Fare'].astype(int)
# get fare for survived & didn't survive passengers
fare_not_survived = train_df["Fare"][train_df["Survived"] == 0]
fare_survived = train_df["Fare"][train_df["Survived"] == 1]
# get average and std for fare of survived/not survived passengers
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)
# 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')
# get average, std, and number of NaN values in train_df
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)
# 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)
# 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))
# 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)
train_df.drop(['Person'],axis=1,inplace=True)
test_df.drop(['Person'],axis=1,inplace=True)
# Pclass
# 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)
# 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()
# Logistic Regression
print "Logistic Regression...\n"
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
logreg.score(X_train, Y_train)
# Support Vector Machines
print "Support Vector Machines...\n"
svc = SVC()
svc.fit(X_train, Y_train)
Y_pred = svc.predict(X_test)
svc.score(X_train, Y_train)
# Random Forests
print "Random Forests...\n"
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)
# KNN
print "KNN...\n"
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
knn.score(X_train, Y_train)
# Gaussian Naive Bayes
print "Gaussian Naive Bayes...\n"
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
gaussian.score(X_train, Y_train)
# 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
print coeff_df
# Submission
print "Exporting..."
submission = pd.DataFrame({
"PassengerId": test_df["PassengerId"],
"Survived": Y_pred
})
submission.to_csv('titanic.csv', index=False)