1- A Simple Heuristic
In this exercise, we will perform some rudimentary practices similar to those of
an actual data scientist.
Part of a data scientist's job is to use her or his intuition and insight to
write algorithms and heuristics. A data scientist also creates mathematical models
to make predictions based on some attributes from the data that they are examining.
We would like for you to take your knowledge and intuition about the Titanic
and its passengers' attributes to predict whether or not the passengers survived
or perished. You can read more about the Titanic and specifics about this dataset at:
http://en.wikipedia.org/wiki/RMS_Titanic
http://www.kaggle.com/c/titanic-gettingStarted
In this exercise and the following ones, you are given a list of Titantic passengers
and their associated information. More information about the data can be seen at the
link below:
http://www.kaggle.com/c/titanic-gettingStarted/data.
For this exercise, you need to write a simple heuristic that will use
the passengers' gender to predict if that person survived the Titanic disaster.
You prediction should be 78% accurate or higher.
Here's a simple heuristic to start off:
1) If the passenger is female, your heuristic should assume that the
passenger survived.
2) If the passenger is male, you heuristic should
assume that the passenger did not survive.
You can access the gender of a passenger via passenger['Sex'].
If the passenger is male, passenger['Sex'] will return a string "male".
If the passenger is female, passenger['Sex'] will return a string "female".
Write your prediction back into the "predictions" dictionary. The
key of the dictionary should be the passenger's id (which can be accessed
via passenger["PassengerId"]) and the associated value should be 1 if the
passenger survied or 0 otherwise.
For example, if a passenger is predicted to have survived:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 1
And if a passenger is predicted to have perished in the disaster:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 0
You can also look at the Titantic data that you will be working with
at the link below:
https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/titanic_data.csv
import numpy
import pandas
import statsmodels.api as sm
def simple_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index, passenger in df.iterrows():
passenger_id = passenger['PassengerId']
return predictions
import numpy
import pandas
import statsmodels.api as sm
def simple_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index,passenger in df.iterrows():
passenger_id = passenger['PassengerId']
if passenger['Sex'] == 'male':
predictions[passenger_id] = 0
else:
predictions[passenger_id] = 1
return predictions
2 - A More Complex Heuristic
'''
You are given a list of Titantic passengers and their associated
information. More information about the data can be seen at the link below:
http://www.kaggle.com/c/titanic-gettingStarted/data
For this exercise, you need to write a more sophisticated algorithm
that will use the passengers' gender and their socioeconomical class and age
to predict if they survived the Titanic diaster.
You prediction should be 79% accurate or higher.
Here's the algorithm, predict the passenger survived if:
1) If the passenger is female or
2) if his/her socioeconomic status is high AND if the passenger is under 18
Otherwise, your algorithm should predict that the passenger perished in the disaster.
Or more specifically in terms of coding:
female or (high status and under 18)
You can access the gender of a passenger via passenger['Sex'].
If the passenger is male, passenger['Sex'] will return a string "male".
If the passenger is female, passenger['Sex'] will return a string "female".
You can access the socioeconomic status of a passenger via passenger['Pclass']:
High socioeconomic status -- passenger['Pclass'] is 1
Medium socioeconomic status -- passenger['Pclass'] is 2
Low socioeconomic status -- passenger['Pclass'] is 3
You can access the age of a passenger via passenger['Age'].
Write your prediction back into the "predictions" dictionary. The
key of the dictionary should be the Passenger's id (which can be accessed
via passenger["PassengerId"]) and the associated value should be 1 if the
passenger survived or 0 otherwise.
For example, if a passenger is predicted to have survived:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 1
And if a passenger is predicted to have perished in the disaster:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 0
You can also look at the Titantic data that you will be working with
at the link below:
https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/titanic_data.csv
'''
import numpy
import pandas
import statsmodels.api as sm
def complex_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index, passenger in df.iterrows():
passenger_id = passenger['PassengerId']
return predictions
import numpy
import pandas
import statsmodel.api as sm
def complex_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index,passenger in df.iterrows():
passenger_id = passenger['PassengerId']
if (passenger['Sex'] == 'female') or (passenger['Pclass'] == 1 and passenger['Age'] < 18):
predictions[passenger_id] = 1
else:
predictions[passenger_id] = 0
return predictions
3 - Your Custom Heuristic
'''
You are given a list of Titantic passengers and their associated
information. More information about the data can be seen at the link below:
http://www.kaggle.com/c/titanic-gettingStarted/data
For this exercise, you need to write a custom heuristic that will take
in some combination of the passenger's attributes and predict if the passenger
survived the Titanic diaster.
Can your custom heuristic beat 80% accuracy?
The available attributes are:
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)
SPECIAL NOTES:
Pclass is a proxy for socioeconomic status (SES)
1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower
Age is in years; fractional if age less than one
If the age is estimated, it is in the form xx.5
With respect to the family relation variables (i.e. SibSp and Parch)
some relations were ignored. The following are the definitions used
for SibSp and Parch.
Sibling: brother, sister, stepbrother, or stepsister of passenger aboard Titanic
Spouse: husband or wife of passenger aboard Titanic (mistresses and fiancees ignored)
Parent: mother or father of passenger aboard Titanic
Child: son, daughter, stepson, or stepdaughter of passenger aboard Titanic
Write your prediction back into the "predictions" dictionary. The
key of the dictionary should be the passenger's id (which can be accessed
via passenger["PassengerId"]) and the associating value should be 1 if the
passenger survvied or 0 otherwise.
For example, if a passenger is predicted to have survived:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 1
And if a passenger is predicted to have perished in the disaster:
passenger_id = passenger['PassengerId']
predictions[passenger_id] = 0
You can also look at the Titantic data that you will be working with
at the link below:
https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/titanic_data.csv
'''
import numpy
import pandas
import statsmodels.api as sm
def custom_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index, passenger in df.iterrows():
return predictions
import numpy
import pandas
import statsmodels.api as sm
def custom_heuristic(file_path):
predictions = {}
df = pandas.read_csv(file_path)
for passenger_index, passenger in df.iterrows():
return predictions