titanic.py

# -*- coding: utf-8 -*-
"""
Created on Thu Sep 29 00:44:15 2016

@author: singh
"""
import numpy as np
import pandas as pd

# RMS Titanic data visualization code 
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline

# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)

# Print the first few entries of the RMS Titanic data
display(full_data.head())

# Store the 'Survived' feature in a new variable and remove it from the dataset
outcomes = full_data['Survived']
data = full_data.drop('Survived', axis = 1)

# Show the new dataset with 'Survived' removed
display(data.head())


def accuracy_score(truth, pred):
    """ Returns accuracy score for input truth and predictions. """
    
    # Ensure that the number of predictions matches number of outcomes
    if len(truth) == len(pred): 
        
        # Calculate and return the accuracy as a percent
        return "Predictions have an accuracy of {:.2f}%.".format((truth == pred).mean()*100)
    
    else:
        return "Number of predictions does not match number of outcomes!"
    
# Test the 'accuracy_score' function
predictions = pd.Series(np.ones(5, dtype = int))
print accuracy_score(outcomes[:5], predictions)




def predictions_0(data):
    """ Model with no features. Always predicts a passenger did not survive. """

    predictions = []
    for _, passenger in data.iterrows():
        
        # Predict the survival of 'passenger'
        predictions.append(0)
    
    # Return our predictions
    return pd.Series(predictions)

# Make the predictions
predictions = predictions_0(data)


def predictions_1(data):
    """ Model with one feature: 
            - Predict a passenger survived if they are female. """
    
    predictions = []
    for _, passenger in data.iterrows():
        
        # Remove the 'pass' statement below 
        # and write your prediction conditions here
        if passenger['Sex']=='female':
            predictions.append(1)
        else:
            predictions.append(0)
    
    # Return our predictions
    return pd.Series(predictions)

# Make the predictions
predictions = predictions_1(data)