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The Most Basic-est Model of Them All

They all survive





In [21]:



    


import csv as csv
import numpy as np

The optimistic model: They All Survive





In [22]:



    


test_file        = open('./data/test.csv', 'rb') # Open the test data
test_file_object = csv.reader(test_file)
header           = test_file_object.next()



    











In [23]:



    


header



    


















    
Out[23]:








['PassengerId',
 'Pclass',
 'Name',
 'Sex',
 'Age',
 'SibSp',
 'Parch',
 'Ticket',
 'Fare',
 'Cabin',
 'Embarked']

Open a new model (.csv) file to write to





In [24]:



    


predictions_file = open('./models/jfaPythonModel-allSurvive.csv', 'wb')
predictions_file_object = csv.writer(predictions_file)

Write the columns header row





In [25]:



    


predictions_file_object.writerow(['PassengerID', 'Survived'])



    











In [26]:



    


for row in test_file_object:
    predictions_file_object.writerow([row[0], "1"])



    











In [27]:



    


test_file.close()
predictions_file.close()

Take a look at the resulting predictions





In [17]:



    


output_predictions_file        = open('./models/jfaPythonModel-allSurvive.csv', 'rb')
output_predictions_file_object = csv.reader(output_predictions_file)
data                           = []



    











In [18]:



    


for row in output_predictions_file_object:
    data.append(row[0:])
data = np.array(data)



    











In [19]:



    


data.shape



    


















    
Out[19]:








(418, 2)

















In [20]:



    


data



    


















    
Out[20]:








array([['PassengerID', 'Survived'],
       ['893', '1'],
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      dtype='|S11')

Kaggle Submission Results

Your submission scored 0.37321

Only 37% correct. Looks like we can do better!

The Overly Pessimistic Model

Let's create a model which predicts that all Titanic passengers die.

First open up the test data





In [74]:



    


test_data = open('./data/test.csv')
test_data_object = csv.reader(test_data)

Skip the first row because it's the header row





In [75]:



    


header = test_data_object.next()
header



    


















    
Out[75]:








['PassengerId',
 'Pclass',
 'Name',
 'Sex',
 'Age',
 'SibSp',
 'Parch',
 'Ticket',
 'Fare',
 'Cabin',
 'Embarked']

Let's open up an output prediction model/csv-file





In [76]:



    


predictions_file = open('./models/jfaPythonModel-allDie.csv', 'wb')
predictions_file_object = csv.writer(predictions_file)
Write the columns header row 




In [77]:



    


predictions_file_object.writerow(['PassengerID', 'Survived'])

Write in the prediction model that they all died





In [78]:



    


for passenger in test_data_object:
    predictions_file_object.writerow([passenger[0], "0"])

Close the test data file and the predicitons file





In [79]:



    


test_data.close()
predictions_file.close()

Take a look at the output predictions





In [80]:



    


output_predictions_file = open('./models/jfaPythonModel-allDie.csv', 'rb')
output_predictions_file_object = csv.reader(output_predictions_file)



    











In [81]:



    


data = []
for passenger in output_predictions_file_object:
    data.append(passenger[0:])
data = np.array(data)



    











In [82]:



    


data.shape



    


















    
Out[82]:








(419, 2)

















In [83]:



    


output_predictions_file.close()

Results

Your submission scored 0.62679.

Quite an improvement predicting almost 63% correct. But we can do better





In [ ]:

Content source: jasonarita/Kaggle-Titanic-R-Python

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