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# Imports
# pandas
import pandas as pd
from pandas import Series,DataFrame
# numpy, matplotlib, seaborn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
# 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
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# get titanic & test csv files as a DataFrame
titanic_df = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test_df = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
# preview the data
titanic_df.head()
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titanic_df.info()
print("----------------------------")
test_df.info()
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# drop unnecessary columns, these columns won't be useful in analysis and prediction
titanic_df = titanic_df.drop(['PassengerId','Name','Ticket'], axis=1)
test_df = test_df.drop(['Name','Ticket'], axis=1)
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# Embarked
# only in titanic_df, fill the two missing values with the most occurred value, which is "S".
titanic_df["Embarked"] = titanic_df["Embarked"].fillna("S")
# plot
sns.factorplot('Embarked','Survived', data=titanic_df,size=4,aspect=3)
fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))
# sns.factorplot('Embarked',data=titanic_df,kind='count',order=['S','C','Q'],ax=axis1)
# sns.factorplot('Survived',hue="Embarked",data=titanic_df,kind='count',order=[1,0],ax=axis2)
sns.countplot(x='Embarked', data=titanic_df, ax=axis1)
sns.countplot(x='Survived', hue="Embarked", data=titanic_df, order=[1,0], ax=axis2)
# group by embarked, and get the mean for survived passengers for each value in Embarked
embark_perc = titanic_df[["Embarked", "Survived"]].groupby(['Embarked'],as_index=False).mean()
sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)
# 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(titanic_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)
titanic_df = titanic_df.join(embark_dummies_titanic)
test_df = test_df.join(embark_dummies_test)
titanic_df.drop(['Embarked'], axis=1,inplace=True)
test_df.drop(['Embarked'], axis=1,inplace=True)
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# 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
titanic_df['Fare'] = titanic_df['Fare'].astype(int)
test_df['Fare'] = test_df['Fare'].astype(int)
# get fare for survived & didn't survive passengers
fare_not_survived = titanic_df["Fare"][titanic_df["Survived"] == 0]
fare_survived = titanic_df["Fare"][titanic_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
titanic_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)
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# 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')
# axis3.set_title('Original Age values - Test')
# axis4.set_title('New Age values - Test')
# get average, std, and number of NaN values in titanic_df
average_age_titanic = titanic_df["Age"].mean()
std_age_titanic = titanic_df["Age"].std()
count_nan_age_titanic = titanic_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)
# fill NaN values in Age column with random values generated
titanic_df["Age"][np.isnan(titanic_df["Age"])] = rand_1
test_df["Age"][np.isnan(test_df["Age"])] = rand_2
# convert from float to int
titanic_df['Age'] = titanic_df['Age'].astype(int)
test_df['Age'] = test_df['Age'].astype(int)
# plot original Age values
titanic_df['Age'].hist(bins=70, ax=axis1)
# test_df['Age'].hist(bins=70, ax=axis3)
# plot new Age Values
titanic_df['Age'].hist(bins=70, ax=axis2)
# test_df['Age'].hist(bins=70, ax=axis4)
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# .... continue with plot Age column
# peaks for survived/not survived passengers by their age
facet = sns.FacetGrid(titanic_df, hue="Survived",aspect=4)
facet.map(sns.kdeplot,'Age',shade= True)
facet.set(xlim=(0, titanic_df['Age'].max()))
facet.add_legend()
# average survived passengers by age
fig, axis1 = plt.subplots(1,1,figsize=(18,4))
average_age = titanic_df[["Age", "Survived"]].groupby(['Age'],as_index=False).mean()
sns.barplot(x='Age', y='Survived', data=average_age)
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# Cabin
# It has a lot of NaN values, so it won't cause a remarkable impact on prediction
titanic_df.drop("Cabin",axis=1,inplace=True)
test_df.drop("Cabin",axis=1,inplace=True)
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# 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.
titanic_df['Family'] = titanic_df["Parch"] + titanic_df["SibSp"]
titanic_df['Family'].loc[titanic_df['Family'] > 0] = 1
titanic_df['Family'].loc[titanic_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
titanic_df = titanic_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=titanic_df,kind='count',ax=axis1)
sns.countplot(x='Family', data=titanic_df, order=[1,0], ax=axis1)
# average of survived for those who had/didn't have any family member
family_perc = titanic_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)
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# 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
titanic_df['Person'] = titanic_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
titanic_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(titanic_df['Person'])
person_dummies_titanic.columns = ['Male','Female','Child']
person_dummies_titanic.drop(['Male'], axis=1, inplace=True)
person_dummies_test = pd.get_dummies(test_df['Person'])
person_dummies_test.columns = ['Male','Female','Child']
person_dummies_test.drop(['Male'], axis=1, inplace=True)
titanic_df = titanic_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=titanic_df,kind='count',ax=axis1)
sns.countplot(x='Person', data=titanic_df, ax=axis1)
# average of survived for each Person(male, female, or child)
family_perc = titanic_df[["Person", "Survived"]].groupby(['Person'],as_index=False).mean()
sns.barplot(x='Person', y='Survived', data=family_perc, ax=axis2, order=['male','female','child'])
titanic_df.drop(['Person'],axis=1,inplace=True)
test_df.drop(['Person'],axis=1,inplace=True)
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# Pclass
# sns.factorplot('Pclass',data=titanic_df,kind='count',order=[1,2,3])
sns.factorplot('Pclass','Survived',order=[1,2,3], data=titanic_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(titanic_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)
titanic_df.drop(['Pclass'],axis=1,inplace=True)
test_df.drop(['Pclass'],axis=1,inplace=True)
titanic_df = titanic_df.join(pclass_dummies_titanic)
test_df = test_df.join(pclass_dummies_test)
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# define training and testing sets
X_train = titanic_df.drop("Survived",axis=1)
Y_train = titanic_df["Survived"]
X_test = test_df.drop("PassengerId",axis=1).copy()
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# Logistic Regression
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
logreg.score(X_train, Y_train)
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# Support Vector Machines
# svc = SVC()
# svc.fit(X_train, Y_train)
# Y_pred = svc.predict(X_test)
# svc.score(X_train, Y_train)
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# 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)
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# knn = KNeighborsClassifier(n_neighbors = 3)
# knn.fit(X_train, Y_train)
# Y_pred = knn.predict(X_test)
# knn.score(X_train, Y_train)
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# Gaussian Naive Bayes
# gaussian = GaussianNB()
# gaussian.fit(X_train, Y_train)
# Y_pred = gaussian.predict(X_test)
# gaussian.score(X_train, Y_train)
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# get Correlation Coefficient for each feature using Logistic Regression
coeff_df = DataFrame(titanic_df.columns.delete(0))
coeff_df.columns = ['Features']
coeff_df["Coefficient Estimate"] = pd.Series(logreg.coef_[0])
# preview
coeff_df
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submission = pd.DataFrame({
"PassengerId": test_df["PassengerId"],
"Survived": Y_pred
})
submission.to_csv('titanic.csv', index=False)