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adding all work done so far (lessons 1 - 5)

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Daniel Tomlinson
2019-07-10 19:58:53 +01:00
parent 8085149a49
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13
python/Supervised Learning/Decision Trees/16. Calculating information gain on a dataset/calculate.py Normal file
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import numpy as np
def two_group_ent(first, tot):
return -(first / tot * np.log2(first / tot) +
(tot - first) / tot * np.log2((tot - first) / tot))
tot_ent = two_group_ent(10, 24)
g17_ent = 15 / 24 * two_group_ent(11, 15) + 9 / 24 * two_group_ent(6, 9)
answer = tot_ent - g17_ent
print(answer)

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python/Supervised Learning/Decision Trees/16. Calculating information gain on a dataset/ml-bugs.csv Normal file
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Species,Color,Length (mm)
Mobug,Brown,11.6
Mobug,Blue,16.3
Lobug,Blue,15.1
Lobug,Green,23.7
Lobug,Blue,18.4
Lobug,Brown,17.1
Mobug,Brown,15.7
Lobug,Green,18.6
Lobug,Blue,22.9
Lobug,Blue,21.0
Lobug,Blue,20.5
Mobug,Green,21.2
Mobug,Brown,13.8
Lobug,Blue,14.5
Lobug,Green,24.8
Mobug,Brown,18.2
Lobug,Green,17.9
Lobug,Green,22.7
Mobug,Green,19.9
Mobug,Blue,14.6
Mobug,Blue,19.2
Lobug,Brown,14.1
Lobug,Green,18.8
Mobug,Blue,13.1
1 Species Color Length (mm)
2 Mobug Brown 11.6
3 Mobug Blue 16.3
4 Lobug Blue 15.1
5 Lobug Green 23.7
6 Lobug Blue 18.4
7 Lobug Brown 17.1
8 Mobug Brown 15.7
9 Lobug Green 18.6
10 Lobug Blue 22.9
11 Lobug Blue 21.0
12 Lobug Blue 20.5
13 Mobug Green 21.2
14 Mobug Brown 13.8
15 Lobug Blue 14.5
16 Lobug Green 24.8
17 Mobug Brown 18.2
18 Lobug Green 17.9
19 Lobug Green 22.7
20 Mobug Green 19.9
21 Mobug Blue 14.6
22 Mobug Blue 19.2
23 Lobug Brown 14.1
24 Lobug Green 18.8
25 Mobug Blue 13.1

96
python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/data.csv Normal file
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python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/quiz.py Normal file
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# Import statements
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
# Read the data.
data = np.asarray(pd.read_csv('data.csv', header=None))
# Assign the features to the variable X, and the labels to the variable y.
X = data[:, 0:2]
y = data[:, 2]
# TODO: Create the decision tree model and assign it to the variable model.
# You won't need to, but if you'd like, play with hyperparameters such
# as max_depth and min_samples_leaf and see what they do to the decision
# boundary.
model = DecisionTreeClassifier(max_depth=7, min_samples_leaf=10)
# TODO: Fit the model.
model.fit(X, y)
# TODO: Make predictions. Store them in the variable y_pred.
y_pred = model.predict(X)
print(y_pred)
# TODO: Calculate the accuracy and assign it to the variable acc.
acc = accuracy_score(y, y_pred)
print(acc)

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python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/titanic.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# # Lab: Titanic Survival Exploration with Decision Trees
# ## Getting Started
# In this lab, you will see how decision trees work by implementing a decision tree in sklearn.
#
# We'll start by loading the dataset and displaying some of its rows.
# In[6]:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
# from IPython.display import display # Allows the use of display() for DataFrames
# Pretty display for notebooks
# get_ipython().run_line_magic('matplotlib', 'inline')
# Set a random seed
import random
random.seed(42)
# 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())
# Recall that these are the various features present for each passenger on the ship:
# - **Survived**: Outcome of survival (0 = No; 1 = Yes)
# - **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
# - **Name**: Name of passenger
# - **Sex**: Sex of the passenger
# - **Age**: Age of the passenger (Some entries contain `NaN`)
# - **SibSp**: Number of siblings and spouses of the passenger aboard
# - **Parch**: Number of parents and children of the passenger
# - **Ticket**: Ticket number of the passenger
# - **Fare**: Fare paid by the passenger
# - **Cabin** Cabin number of the passenger (Some entries contain `NaN`)
# - **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)
#
# Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets.
# Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`.
# In[7]:
# Store the 'Survived' feature in a new variable and remove it from the dataset
outcomes = full_data['Survived']
features_raw = full_data.drop('Survived', axis = 1)
# Show the new dataset with 'Survived' removed
display(features_raw.head())
# The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.
#
# ## Preprocessing the data
#
# Now, let's do some data preprocessing. First, we'll remove the names of the passengers, and then one-hot encode the features.
#
# **Question:** Why would it be a terrible idea to one-hot encode the data without removing the names?
# (Andw
# In[8]:
# Removing the names
features_no_names = features_raw.drop(['Name'], axis=1)
# One-hot encoding
features = pd.get_dummies(features_no_names)
# And now we'll fill in any blanks with zeroes.
# In[9]:
features = features.fillna(0.0)
display(features.head())
# ## (TODO) Training the model
#
# Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set.
# In[15]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)
# In[17]:
# Import the classifier from sklearn
from sklearn.tree import DecisionTreeClassifier
# TODO: Define the classifier, and fit it to the data
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# ## Testing the model
# Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set.
# In[18]:
# Making predictions
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
# Calculate the accuracy
from sklearn.metrics import accuracy_score
train_accuracy = accuracy_score(y_train, y_train_pred)
test_accuracy = accuracy_score(y_test, y_test_pred)
print('The training accuracy is', train_accuracy)
print('The test accuracy is', test_accuracy)
# # Exercise: Improving the model
#
# Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.
#
# So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:
# - `max_depth`
# - `min_samples_leaf`
# - `min_samples_split`
#
# You can use your intuition, trial and error, or even better, feel free to use Grid Search!
#
# **Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next.
# In[23]:
# TODO: Train the model
new_model = DecisionTreeClassifier(max_depth=10, min_samples_leaf=6, min_samples_split=8)
new_model.fit(X_train, y_train)
# TODO: Make predictions
new_y_train_pred = new_model.predict(X_train)
new_y_test_pred = new_model.predict(X_test)
# TODO: Calculate the accuracy
new_train_accuracy = accuracy_score(y_train, new_y_train_pred)
new_test_accuracy = accuracy_score(y_test, new_y_test_pred)
print(f'The training accuracy on the new model is {new_train_accuracy:.4f}')
print(f'The test accuracy on the new model is {new_test_accuracy:.4f}')

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python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# # Lab: Titanic Survival Exploration with Decision Trees
# ## Getting Started
# In this lab, you will see how decision trees work by implementing a decision tree in sklearn.
#
# We'll start by loading the dataset and displaying some of its rows.
# In[6]:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
# from IPython.display import display # Allows the use of display() for DataFrames
# Pretty display for notebooks
# get_ipython().run_line_magic('matplotlib', 'inline')
# Set a random seed
import random
random.seed(42)
# 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())
# Recall that these are the various features present for each passenger on the ship:
# - **Survived**: Outcome of survival (0 = No; 1 = Yes)
# - **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
# - **Name**: Name of passenger
# - **Sex**: Sex of the passenger
# - **Age**: Age of the passenger (Some entries contain `NaN`)
# - **SibSp**: Number of siblings and spouses of the passenger aboard
# - **Parch**: Number of parents and children of the passenger
# - **Ticket**: Ticket number of the passenger
# - **Fare**: Fare paid by the passenger
# - **Cabin** Cabin number of the passenger (Some entries contain `NaN`)
# - **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)
#
# Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets.
# Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`.
# In[7]:
# Store the 'Survived' feature in a new variable and remove it from the dataset
outcomes = full_data['Survived']
features_raw = full_data.drop('Survived', axis = 1)
# Show the new dataset with 'Survived' removed
# display(features_raw.head())
# The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.
#
# ## Preprocessing the data
#
# Now, let's do some data preprocessing. First, we'll remove the names of the passengers, and then one-hot encode the features.
#
# **Question:** Why would it be a terrible idea to one-hot encode the data without removing the names?
# (Andw
# In[8]:
# Removing the names
features_no_names = features_raw.drop(['Name'], axis=1)
# One-hot encoding
features = pd.get_dummies(features_no_names)
# And now we'll fill in any blanks with zeroes.
# In[9]:
features = features.fillna(0.0)
# display(features.head())
# ## (TODO) Training the model
#
# Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set.
# In[15]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)
# In[17]:
# Import the classifier from sklearn
from sklearn.tree import DecisionTreeClassifier
# TODO: Define the classifier, and fit it to the data
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# ## Testing the model
# Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set.
# In[18]:
# Making predictions
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
# Calculate the accuracy
from sklearn.metrics import accuracy_score
train_accuracy = accuracy_score(y_train, y_train_pred)
test_accuracy = accuracy_score(y_test, y_test_pred)
print('The training accuracy is', train_accuracy)
print('The test accuracy is', test_accuracy)
# # Exercise: Improving the model
#
# Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.
#
# So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:
# - `max_depth`
# - `min_samples_leaf`
# - `min_samples_split`
#
# You can use your intuition, trial and error, or even better, feel free to use Grid Search!
#
# **Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next.
# In[23]:
# TODO: Train the model
new_model = DecisionTreeClassifier(max_depth=10, min_samples_leaf=6, min_samples_split=8)
new_model.fit(X_train, y_train)
# TODO: Make predictions
new_y_train_pred = new_model.predict(X_train)
new_y_test_pred = new_model.predict(X_test)
# TODO: Calculate the accuracy
new_train_accuracy = accuracy_score(y_train, new_y_train_pred)
new_test_accuracy = accuracy_score(y_test, new_y_test_pred)
print(f'The training accuracy on the new model is {new_train_accuracy:.4f}')
print(f'The test accuracy on the new model is {new_test_accuracy:.4f}')

892
python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_data.csv Normal file
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,PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
0,1,0,3,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,S
1,2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,C
2,3,1,3,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,S
3,4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,S
4,5,0,3,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,S
5,6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
6,7,0,1,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,S
7,8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,3,1,349909,21.075,,S
8,9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,0,2,347742,11.1333,,S
9,10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,C
10,11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4.0,1,1,PP 9549,16.7,G6,S
11,12,1,1,"Bonnell, Miss. Elizabeth",female,58.0,0,0,113783,26.55,C103,S
12,13,0,3,"Saundercock, Mr. William Henry",male,20.0,0,0,A/5. 2151,8.05,,S
13,14,0,3,"Andersson, Mr. Anders Johan",male,39.0,1,5,347082,31.275,,S
14,15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,0,0,350406,7.8542,,S
15,16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,0,0,248706,16.0,,S
16,17,0,3,"Rice, Master. Eugene",male,2.0,4,1,382652,29.125,,Q
17,18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13.0,,S
18,19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31.0,1,0,345763,18.0,,S
19,20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
20,21,0,2,"Fynney, Mr. Joseph J",male,35.0,0,0,239865,26.0,,S
21,22,1,2,"Beesley, Mr. Lawrence",male,34.0,0,0,248698,13.0,D56,S
22,23,1,3,"McGowan, Miss. Anna ""Annie""",female,15.0,0,0,330923,8.0292,,Q
23,24,1,1,"Sloper, Mr. William Thompson",male,28.0,0,0,113788,35.5,A6,S
24,25,0,3,"Palsson, Miss. Torborg Danira",female,8.0,3,1,349909,21.075,,S
25,26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,1,5,347077,31.3875,,S
26,27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
27,28,0,1,"Fortune, Mr. Charles Alexander",male,19.0,3,2,19950,263.0,C23 C25 C27,S
28,29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
29,30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
30,31,0,1,"Uruchurtu, Don. Manuel E",male,40.0,0,0,PC 17601,27.7208,,C
31,32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
32,33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
33,34,0,2,"Wheadon, Mr. Edward H",male,66.0,0,0,C.A. 24579,10.5,,S
34,35,0,1,"Meyer, Mr. Edgar Joseph",male,28.0,1,0,PC 17604,82.1708,,C
35,36,0,1,"Holverson, Mr. Alexander Oskar",male,42.0,1,0,113789,52.0,,S
36,37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
37,38,0,3,"Cann, Mr. Ernest Charles",male,21.0,0,0,A./5. 2152,8.05,,S
38,39,0,3,"Vander Planke, Miss. Augusta Maria",female,18.0,2,0,345764,18.0,,S
39,40,1,3,"Nicola-Yarred, Miss. Jamila",female,14.0,1,0,2651,11.2417,,C
40,41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,1,0,7546,9.475,,S
41,42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,1,0,11668,21.0,,S
42,43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
43,44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,1,2,SC/Paris 2123,41.5792,,C
44,45,1,3,"Devaney, Miss. Margaret Delia",female,19.0,0,0,330958,7.8792,,Q
45,46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
46,47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
47,48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
48,49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
49,50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,1,0,349237,17.8,,S
50,51,0,3,"Panula, Master. Juha Niilo",male,7.0,4,1,3101295,39.6875,,S
51,52,0,3,"Nosworthy, Mr. Richard Cater",male,21.0,0,0,A/4. 39886,7.8,,S
52,53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,1,0,PC 17572,76.7292,D33,C
53,54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,1,0,2926,26.0,,S
54,55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65.0,0,1,113509,61.9792,B30,C
55,56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
56,57,1,2,"Rugg, Miss. Emily",female,21.0,0,0,C.A. 31026,10.5,,S
57,58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
58,59,1,2,"West, Miss. Constance Mirium",female,5.0,1,2,C.A. 34651,27.75,,S
59,60,0,3,"Goodwin, Master. William Frederick",male,11.0,5,2,CA 2144,46.9,,S
60,61,0,3,"Sirayanian, Mr. Orsen",male,22.0,0,0,2669,7.2292,,C
61,62,1,1,"Icard, Miss. Amelie",female,38.0,0,0,113572,80.0,B28,
62,63,0,1,"Harris, Mr. Henry Birkhardt",male,45.0,1,0,36973,83.475,C83,S
63,64,0,3,"Skoog, Master. Harald",male,4.0,3,2,347088,27.9,,S
64,65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
65,66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
66,67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,0,0,C.A. 29395,10.5,F33,S
67,68,0,3,"Crease, Mr. Ernest James",male,19.0,0,0,S.P. 3464,8.1583,,S
68,69,1,3,"Andersson, Miss. Erna Alexandra",female,17.0,4,2,3101281,7.925,,S
69,70,0,3,"Kink, Mr. Vincenz",male,26.0,2,0,315151,8.6625,,S
70,71,0,2,"Jenkin, Mr. Stephen Curnow",male,32.0,0,0,C.A. 33111,10.5,,S
71,72,0,3,"Goodwin, Miss. Lillian Amy",female,16.0,5,2,CA 2144,46.9,,S
72,73,0,2,"Hood, Mr. Ambrose Jr",male,21.0,0,0,S.O.C. 14879,73.5,,S
73,74,0,3,"Chronopoulos, Mr. Apostolos",male,26.0,1,0,2680,14.4542,,C
74,75,1,3,"Bing, Mr. Lee",male,32.0,0,0,1601,56.4958,,S
75,76,0,3,"Moen, Mr. Sigurd Hansen",male,25.0,0,0,348123,7.65,F G73,S
76,77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
77,78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
78,79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29.0,,S
79,80,1,3,"Dowdell, Miss. Elizabeth",female,30.0,0,0,364516,12.475,,S
80,81,0,3,"Waelens, Mr. Achille",male,22.0,0,0,345767,9.0,,S
81,82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29.0,0,0,345779,9.5,,S
82,83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
83,84,0,1,"Carrau, Mr. Francisco M",male,28.0,0,0,113059,47.1,,S
84,85,1,2,"Ilett, Miss. Bertha",female,17.0,0,0,SO/C 14885,10.5,,S
85,86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3,0,3101278,15.85,,S
86,87,0,3,"Ford, Mr. William Neal",male,16.0,1,3,W./C. 6608,34.375,,S
87,88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
88,89,1,1,"Fortune, Miss. Mabel Helen",female,23.0,3,2,19950,263.0,C23 C25 C27,S
89,90,0,3,"Celotti, Mr. Francesco",male,24.0,0,0,343275,8.05,,S
90,91,0,3,"Christmann, Mr. Emil",male,29.0,0,0,343276,8.05,,S
91,92,0,3,"Andreasson, Mr. Paul Edvin",male,20.0,0,0,347466,7.8542,,S
92,93,0,1,"Chaffee, Mr. Herbert Fuller",male,46.0,1,0,W.E.P. 5734,61.175,E31,S
93,94,0,3,"Dean, Mr. Bertram Frank",male,26.0,1,2,C.A. 2315,20.575,,S
94,95,0,3,"Coxon, Mr. Daniel",male,59.0,0,0,364500,7.25,,S
95,96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
96,97,0,1,"Goldschmidt, Mr. George B",male,71.0,0,0,PC 17754,34.6542,A5,C
97,98,1,1,"Greenfield, Mr. William Bertram",male,23.0,0,1,PC 17759,63.3583,D10 D12,C
98,99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,0,1,231919,23.0,,S
99,100,0,2,"Kantor, Mr. Sinai",male,34.0,1,0,244367,26.0,,S
100,101,0,3,"Petranec, Miss. Matilda",female,28.0,0,0,349245,7.8958,,S
101,102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
102,103,0,1,"White, Mr. Richard Frasar",male,21.0,0,1,35281,77.2875,D26,S
103,104,0,3,"Johansson, Mr. Gustaf Joel",male,33.0,0,0,7540,8.6542,,S
104,105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37.0,2,0,3101276,7.925,,S
105,106,0,3,"Mionoff, Mr. Stoytcho",male,28.0,0,0,349207,7.8958,,S
106,107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21.0,0,0,343120,7.65,,S
107,108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
108,109,0,3,"Rekic, Mr. Tido",male,38.0,0,0,349249,7.8958,,S
109,110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
110,111,0,1,"Porter, Mr. Walter Chamberlain",male,47.0,0,0,110465,52.0,C110,S
111,112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
112,113,0,3,"Barton, Mr. David John",male,22.0,0,0,324669,8.05,,S
113,114,0,3,"Jussila, Miss. Katriina",female,20.0,1,0,4136,9.825,,S
114,115,0,3,"Attalah, Miss. Malake",female,17.0,0,0,2627,14.4583,,C
115,116,0,3,"Pekoniemi, Mr. Edvard",male,21.0,0,0,STON/O 2. 3101294,7.925,,S
116,117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
117,118,0,2,"Turpin, Mr. William John Robert",male,29.0,1,0,11668,21.0,,S
118,119,0,1,"Baxter, Mr. Quigg Edmond",male,24.0,0,1,PC 17558,247.5208,B58 B60,C
119,120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2.0,4,2,347082,31.275,,S
120,121,0,2,"Hickman, Mr. Stanley George",male,21.0,2,0,S.O.C. 14879,73.5,,S
121,122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
122,123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
123,124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13.0,E101,S
124,125,0,1,"White, Mr. Percival Wayland",male,54.0,0,1,35281,77.2875,D26,S
125,126,1,3,"Nicola-Yarred, Master. Elias",male,12.0,1,0,2651,11.2417,,C
126,127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
127,128,1,3,"Madsen, Mr. Fridtjof Arne",male,24.0,0,0,C 17369,7.1417,,S
128,129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
129,130,0,3,"Ekstrom, Mr. Johan",male,45.0,0,0,347061,6.975,,S
130,131,0,3,"Drazenoic, Mr. Jozef",male,33.0,0,0,349241,7.8958,,C
131,132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20.0,0,0,SOTON/O.Q. 3101307,7.05,,S
132,133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47.0,1,0,A/5. 3337,14.5,,S
133,134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29.0,1,0,228414,26.0,,S
134,135,0,2,"Sobey, Mr. Samuel James Hayden",male,25.0,0,0,C.A. 29178,13.0,,S
135,136,0,2,"Richard, Mr. Emile",male,23.0,0,0,SC/PARIS 2133,15.0458,,C
136,137,1,1,"Newsom, Miss. Helen Monypeny",female,19.0,0,2,11752,26.2833,D47,S
137,138,0,1,"Futrelle, Mr. Jacques Heath",male,37.0,1,0,113803,53.1,C123,S
138,139,0,3,"Osen, Mr. Olaf Elon",male,16.0,0,0,7534,9.2167,,S
139,140,0,1,"Giglio, Mr. Victor",male,24.0,0,0,PC 17593,79.2,B86,C
140,141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
141,142,1,3,"Nysten, Miss. Anna Sofia",female,22.0,0,0,347081,7.75,,S
142,143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24.0,1,0,STON/O2. 3101279,15.85,,S
143,144,0,3,"Burke, Mr. Jeremiah",male,19.0,0,0,365222,6.75,,Q
144,145,0,2,"Andrew, Mr. Edgardo Samuel",male,18.0,0,0,231945,11.5,,S
145,146,0,2,"Nicholls, Mr. Joseph Charles",male,19.0,1,1,C.A. 33112,36.75,,S
146,147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27.0,0,0,350043,7.7958,,S
147,148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9.0,2,2,W./C. 6608,34.375,,S
148,149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26.0,F2,S
149,150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42.0,0,0,244310,13.0,,S
150,151,0,2,"Bateman, Rev. Robert James",male,51.0,0,0,S.O.P. 1166,12.525,,S
151,152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22.0,1,0,113776,66.6,C2,S
152,153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
153,154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
154,155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
155,156,0,1,"Williams, Mr. Charles Duane",male,51.0,0,1,PC 17597,61.3792,,C
156,157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16.0,0,0,35851,7.7333,,Q
157,158,0,3,"Corn, Mr. Harry",male,30.0,0,0,SOTON/OQ 392090,8.05,,S
158,159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
159,160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
160,161,0,3,"Cribb, Mr. John Hatfield",male,44.0,0,1,371362,16.1,,S
161,162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40.0,0,0,C.A. 33595,15.75,,S
162,163,0,3,"Bengtsson, Mr. John Viktor",male,26.0,0,0,347068,7.775,,S
163,164,0,3,"Calic, Mr. Jovo",male,17.0,0,0,315093,8.6625,,S
164,165,0,3,"Panula, Master. Eino Viljami",male,1.0,4,1,3101295,39.6875,,S
165,166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9.0,0,2,363291,20.525,,S
166,167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55.0,E33,S
167,168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45.0,1,4,347088,27.9,,S
168,169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
169,170,0,3,"Ling, Mr. Lee",male,28.0,0,0,1601,56.4958,,S
170,171,0,1,"Van der hoef, Mr. Wyckoff",male,61.0,0,0,111240,33.5,B19,S
171,172,0,3,"Rice, Master. Arthur",male,4.0,4,1,382652,29.125,,Q
172,173,1,3,"Johnson, Miss. Eleanor Ileen",female,1.0,1,1,347742,11.1333,,S
173,174,0,3,"Sivola, Mr. Antti Wilhelm",male,21.0,0,0,STON/O 2. 3101280,7.925,,S
174,175,0,1,"Smith, Mr. James Clinch",male,56.0,0,0,17764,30.6958,A7,C
175,176,0,3,"Klasen, Mr. Klas Albin",male,18.0,1,1,350404,7.8542,,S
176,177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
177,178,0,1,"Isham, Miss. Ann Elizabeth",female,50.0,0,0,PC 17595,28.7125,C49,C
178,179,0,2,"Hale, Mr. Reginald",male,30.0,0,0,250653,13.0,,S
179,180,0,3,"Leonard, Mr. Lionel",male,36.0,0,0,LINE,0.0,,S
180,181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
181,182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
182,183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9.0,4,2,347077,31.3875,,S
183,184,1,2,"Becker, Master. Richard F",male,1.0,2,1,230136,39.0,F4,S
184,185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4.0,0,2,315153,22.025,,S
185,186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50.0,A32,S
186,187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
187,188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45.0,0,0,111428,26.55,,S
188,189,0,3,"Bourke, Mr. John",male,40.0,1,1,364849,15.5,,Q
189,190,0,3,"Turcin, Mr. Stjepan",male,36.0,0,0,349247,7.8958,,S
190,191,1,2,"Pinsky, Mrs. (Rosa)",female,32.0,0,0,234604,13.0,,S
191,192,0,2,"Carbines, Mr. William",male,19.0,0,0,28424,13.0,,S
192,193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19.0,1,0,350046,7.8542,,S
193,194,1,2,"Navratil, Master. Michel M",male,3.0,1,1,230080,26.0,F2,S
194,195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44.0,0,0,PC 17610,27.7208,B4,C
195,196,1,1,"Lurette, Miss. Elise",female,58.0,0,0,PC 17569,146.5208,B80,C
196,197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
197,198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42.0,0,1,4579,8.4042,,S
198,199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
199,200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24.0,0,0,248747,13.0,,S
200,201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28.0,0,0,345770,9.5,,S
201,202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
202,203,0,3,"Johanson, Mr. Jakob Alfred",male,34.0,0,0,3101264,6.4958,,S
203,204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
204,205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18.0,0,0,A/5 3540,8.05,,S
205,206,0,3,"Strom, Miss. Telma Matilda",female,2.0,0,1,347054,10.4625,G6,S
206,207,0,3,"Backstrom, Mr. Karl Alfred",male,32.0,1,0,3101278,15.85,,S
207,208,1,3,"Albimona, Mr. Nassef Cassem",male,26.0,0,0,2699,18.7875,,C
208,209,1,3,"Carr, Miss. Helen ""Ellen""",female,16.0,0,0,367231,7.75,,Q
209,210,1,1,"Blank, Mr. Henry",male,40.0,0,0,112277,31.0,A31,C
210,211,0,3,"Ali, Mr. Ahmed",male,24.0,0,0,SOTON/O.Q. 3101311,7.05,,S
211,212,1,2,"Cameron, Miss. Clear Annie",female,35.0,0,0,F.C.C. 13528,21.0,,S
212,213,0,3,"Perkin, Mr. John Henry",male,22.0,0,0,A/5 21174,7.25,,S
213,214,0,2,"Givard, Mr. Hans Kristensen",male,30.0,0,0,250646,13.0,,S
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215,216,1,1,"Newell, Miss. Madeleine",female,31.0,1,0,35273,113.275,D36,C
216,217,1,3,"Honkanen, Miss. Eliina",female,27.0,0,0,STON/O2. 3101283,7.925,,S
217,218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42.0,1,0,243847,27.0,,S
218,219,1,1,"Bazzani, Miss. Albina",female,32.0,0,0,11813,76.2917,D15,C
219,220,0,2,"Harris, Mr. Walter",male,30.0,0,0,W/C 14208,10.5,,S
220,221,1,3,"Sunderland, Mr. Victor Francis",male,16.0,0,0,SOTON/OQ 392089,8.05,,S
221,222,0,2,"Bracken, Mr. James H",male,27.0,0,0,220367,13.0,,S
222,223,0,3,"Green, Mr. George Henry",male,51.0,0,0,21440,8.05,,S
223,224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
224,225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38.0,1,0,19943,90.0,C93,S
225,226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22.0,0,0,PP 4348,9.35,,S
226,227,1,2,"Mellors, Mr. William John",male,19.0,0,0,SW/PP 751,10.5,,S
227,228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
228,229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18.0,0,0,236171,13.0,,S
229,230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
230,231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35.0,1,0,36973,83.475,C83,S
231,232,0,3,"Larsson, Mr. Bengt Edvin",male,29.0,0,0,347067,7.775,,S
232,233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59.0,0,0,237442,13.5,,S
233,234,1,3,"Asplund, Miss. Lillian Gertrud",female,5.0,4,2,347077,31.3875,,S
234,235,0,2,"Leyson, Mr. Robert William Norman",male,24.0,0,0,C.A. 29566,10.5,,S
235,236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
236,237,0,2,"Hold, Mr. Stephen",male,44.0,1,0,26707,26.0,,S
237,238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8.0,0,2,C.A. 31921,26.25,,S
238,239,0,2,"Pengelly, Mr. Frederick William",male,19.0,0,0,28665,10.5,,S
239,240,0,2,"Hunt, Mr. George Henry",male,33.0,0,0,SCO/W 1585,12.275,,S
240,241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
241,242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
242,243,0,2,"Coleridge, Mr. Reginald Charles",male,29.0,0,0,W./C. 14263,10.5,,S
243,244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22.0,0,0,STON/O 2. 3101275,7.125,,S
244,245,0,3,"Attalah, Mr. Sleiman",male,30.0,0,0,2694,7.225,,C
245,246,0,1,"Minahan, Dr. William Edward",male,44.0,2,0,19928,90.0,C78,Q
246,247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25.0,0,0,347071,7.775,,S
247,248,1,2,"Hamalainen, Mrs. William (Anna)",female,24.0,0,2,250649,14.5,,S
248,249,1,1,"Beckwith, Mr. Richard Leonard",male,37.0,1,1,11751,52.5542,D35,S
249,250,0,2,"Carter, Rev. Ernest Courtenay",male,54.0,1,0,244252,26.0,,S
250,251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
251,252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29.0,1,1,347054,10.4625,G6,S
252,253,0,1,"Stead, Mr. William Thomas",male,62.0,0,0,113514,26.55,C87,S
253,254,0,3,"Lobb, Mr. William Arthur",male,30.0,1,0,A/5. 3336,16.1,,S
254,255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41.0,0,2,370129,20.2125,,S
255,256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29.0,0,2,2650,15.2458,,C
256,257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
257,258,1,1,"Cherry, Miss. Gladys",female,30.0,0,0,110152,86.5,B77,S
258,259,1,1,"Ward, Miss. Anna",female,35.0,0,0,PC 17755,512.3292,,C
259,260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50.0,0,1,230433,26.0,,S
260,261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
261,262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3.0,4,2,347077,31.3875,,S
262,263,0,1,"Taussig, Mr. Emil",male,52.0,1,1,110413,79.65,E67,S
263,264,0,1,"Harrison, Mr. William",male,40.0,0,0,112059,0.0,B94,S
264,265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
265,266,0,2,"Reeves, Mr. David",male,36.0,0,0,C.A. 17248,10.5,,S
266,267,0,3,"Panula, Mr. Ernesti Arvid",male,16.0,4,1,3101295,39.6875,,S
267,268,1,3,"Persson, Mr. Ernst Ulrik",male,25.0,1,0,347083,7.775,,S
268,269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58.0,0,1,PC 17582,153.4625,C125,S
269,270,1,1,"Bissette, Miss. Amelia",female,35.0,0,0,PC 17760,135.6333,C99,S
270,271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31.0,,S
271,272,1,3,"Tornquist, Mr. William Henry",male,25.0,0,0,LINE,0.0,,S
272,273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41.0,0,1,250644,19.5,,S
273,274,0,1,"Natsch, Mr. Charles H",male,37.0,0,1,PC 17596,29.7,C118,C
274,275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
275,276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63.0,1,0,13502,77.9583,D7,S
276,277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45.0,0,0,347073,7.75,,S
277,278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0.0,,S
278,279,0,3,"Rice, Master. Eric",male,7.0,4,1,382652,29.125,,Q
279,280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35.0,1,1,C.A. 2673,20.25,,S
280,281,0,3,"Duane, Mr. Frank",male,65.0,0,0,336439,7.75,,Q
281,282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28.0,0,0,347464,7.8542,,S
282,283,0,3,"de Pelsmaeker, Mr. Alfons",male,16.0,0,0,345778,9.5,,S
283,284,1,3,"Dorking, Mr. Edward Arthur",male,19.0,0,0,A/5. 10482,8.05,,S
284,285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26.0,A19,S
285,286,0,3,"Stankovic, Mr. Ivan",male,33.0,0,0,349239,8.6625,,C
286,287,1,3,"de Mulder, Mr. Theodore",male,30.0,0,0,345774,9.5,,S
287,288,0,3,"Naidenoff, Mr. Penko",male,22.0,0,0,349206,7.8958,,S
288,289,1,2,"Hosono, Mr. Masabumi",male,42.0,0,0,237798,13.0,,S
289,290,1,3,"Connolly, Miss. Kate",female,22.0,0,0,370373,7.75,,Q
290,291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26.0,0,0,19877,78.85,,S
291,292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19.0,1,0,11967,91.0792,B49,C
292,293,0,2,"Levy, Mr. Rene Jacques",male,36.0,0,0,SC/Paris 2163,12.875,D,C
293,294,0,3,"Haas, Miss. Aloisia",female,24.0,0,0,349236,8.85,,S
294,295,0,3,"Mineff, Mr. Ivan",male,24.0,0,0,349233,7.8958,,S
295,296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
296,297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
297,298,0,1,"Allison, Miss. Helen Loraine",female,2.0,1,2,113781,151.55,C22 C26,S
298,299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
299,300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50.0,0,1,PC 17558,247.5208,B58 B60,C
300,301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
301,302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
302,303,0,3,"Johnson, Mr. William Cahoone Jr",male,19.0,0,0,LINE,0.0,,S
303,304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
304,305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
305,306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
306,307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
307,308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17.0,1,0,PC 17758,108.9,C65,C
308,309,0,2,"Abelson, Mr. Samuel",male,30.0,1,0,P/PP 3381,24.0,,C
309,310,1,1,"Francatelli, Miss. Laura Mabel",female,30.0,0,0,PC 17485,56.9292,E36,C
310,311,1,1,"Hays, Miss. Margaret Bechstein",female,24.0,0,0,11767,83.1583,C54,C
311,312,1,1,"Ryerson, Miss. Emily Borie",female,18.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C
312,313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26.0,1,1,250651,26.0,,S
313,314,0,3,"Hendekovic, Mr. Ignjac",male,28.0,0,0,349243,7.8958,,S
314,315,0,2,"Hart, Mr. Benjamin",male,43.0,1,1,F.C.C. 13529,26.25,,S
315,316,1,3,"Nilsson, Miss. Helmina Josefina",female,26.0,0,0,347470,7.8542,,S
316,317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24.0,1,0,244367,26.0,,S
317,318,0,2,"Moraweck, Dr. Ernest",male,54.0,0,0,29011,14.0,,S
318,319,1,1,"Wick, Miss. Mary Natalie",female,31.0,0,2,36928,164.8667,C7,S
319,320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40.0,1,1,16966,134.5,E34,C
320,321,0,3,"Dennis, Mr. Samuel",male,22.0,0,0,A/5 21172,7.25,,S
321,322,0,3,"Danoff, Mr. Yoto",male,27.0,0,0,349219,7.8958,,S
322,323,1,2,"Slayter, Miss. Hilda Mary",female,30.0,0,0,234818,12.35,,Q
323,324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22.0,1,1,248738,29.0,,S
324,325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
325,326,1,1,"Young, Miss. Marie Grice",female,36.0,0,0,PC 17760,135.6333,C32,C
326,327,0,3,"Nysveen, Mr. Johan Hansen",male,61.0,0,0,345364,6.2375,,S
327,328,1,2,"Ball, Mrs. (Ada E Hall)",female,36.0,0,0,28551,13.0,D,S
328,329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31.0,1,1,363291,20.525,,S
329,330,1,1,"Hippach, Miss. Jean Gertrude",female,16.0,0,1,111361,57.9792,B18,C
330,331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
331,332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
332,333,0,1,"Graham, Mr. George Edward",male,38.0,0,1,PC 17582,153.4625,C91,S
333,334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16.0,2,0,345764,18.0,,S
334,335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
335,336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
336,337,0,1,"Pears, Mr. Thomas Clinton",male,29.0,1,0,113776,66.6,C2,S
337,338,1,1,"Burns, Miss. Elizabeth Margaret",female,41.0,0,0,16966,134.5,E40,C
338,339,1,3,"Dahl, Mr. Karl Edwart",male,45.0,0,0,7598,8.05,,S
339,340,0,1,"Blackwell, Mr. Stephen Weart",male,45.0,0,0,113784,35.5,T,S
340,341,1,2,"Navratil, Master. Edmond Roger",male,2.0,1,1,230080,26.0,F2,S
341,342,1,1,"Fortune, Miss. Alice Elizabeth",female,24.0,3,2,19950,263.0,C23 C25 C27,S
342,343,0,2,"Collander, Mr. Erik Gustaf",male,28.0,0,0,248740,13.0,,S
343,344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25.0,0,0,244361,13.0,,S
344,345,0,2,"Fox, Mr. Stanley Hubert",male,36.0,0,0,229236,13.0,,S
345,346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24.0,0,0,248733,13.0,F33,S
346,347,1,2,"Smith, Miss. Marion Elsie",female,40.0,0,0,31418,13.0,,S
347,348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
348,349,1,3,"Coutts, Master. William Loch ""William""",male,3.0,1,1,C.A. 37671,15.9,,S
349,350,0,3,"Dimic, Mr. Jovan",male,42.0,0,0,315088,8.6625,,S
350,351,0,3,"Odahl, Mr. Nils Martin",male,23.0,0,0,7267,9.225,,S
351,352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35.0,C128,S
352,353,0,3,"Elias, Mr. Tannous",male,15.0,1,1,2695,7.2292,,C
353,354,0,3,"Arnold-Franchi, Mr. Josef",male,25.0,1,0,349237,17.8,,S
354,355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
355,356,0,3,"Vanden Steen, Mr. Leo Peter",male,28.0,0,0,345783,9.5,,S
356,357,1,1,"Bowerman, Miss. Elsie Edith",female,22.0,0,1,113505,55.0,E33,S
357,358,0,2,"Funk, Miss. Annie Clemmer",female,38.0,0,0,237671,13.0,,S
358,359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
359,360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
360,361,0,3,"Skoog, Mr. Wilhelm",male,40.0,1,4,347088,27.9,,S
361,362,0,2,"del Carlo, Mr. Sebastiano",male,29.0,1,0,SC/PARIS 2167,27.7208,,C
362,363,0,3,"Barbara, Mrs. (Catherine David)",female,45.0,0,1,2691,14.4542,,C
363,364,0,3,"Asim, Mr. Adola",male,35.0,0,0,SOTON/O.Q. 3101310,7.05,,S
364,365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
365,366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30.0,0,0,C 7076,7.25,,S
366,367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60.0,1,0,110813,75.25,D37,C
367,368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
368,369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
369,370,1,1,"Aubart, Mme. Leontine Pauline",female,24.0,0,0,PC 17477,69.3,B35,C
370,371,1,1,"Harder, Mr. George Achilles",male,25.0,1,0,11765,55.4417,E50,C
371,372,0,3,"Wiklund, Mr. Jakob Alfred",male,18.0,1,0,3101267,6.4958,,S
372,373,0,3,"Beavan, Mr. William Thomas",male,19.0,0,0,323951,8.05,,S
373,374,0,1,"Ringhini, Mr. Sante",male,22.0,0,0,PC 17760,135.6333,,C
374,375,0,3,"Palsson, Miss. Stina Viola",female,3.0,3,1,349909,21.075,,S
375,376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
376,377,1,3,"Landergren, Miss. Aurora Adelia",female,22.0,0,0,C 7077,7.25,,S
377,378,0,1,"Widener, Mr. Harry Elkins",male,27.0,0,2,113503,211.5,C82,C
378,379,0,3,"Betros, Mr. Tannous",male,20.0,0,0,2648,4.0125,,C
379,380,0,3,"Gustafsson, Mr. Karl Gideon",male,19.0,0,0,347069,7.775,,S
380,381,1,1,"Bidois, Miss. Rosalie",female,42.0,0,0,PC 17757,227.525,,C
381,382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1.0,0,2,2653,15.7417,,C
382,383,0,3,"Tikkanen, Mr. Juho",male,32.0,0,0,STON/O 2. 3101293,7.925,,S
383,384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35.0,1,0,113789,52.0,,S
384,385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
385,386,0,2,"Davies, Mr. Charles Henry",male,18.0,0,0,S.O.C. 14879,73.5,,S
386,387,0,3,"Goodwin, Master. Sidney Leonard",male,1.0,5,2,CA 2144,46.9,,S
387,388,1,2,"Buss, Miss. Kate",female,36.0,0,0,27849,13.0,,S
388,389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
389,390,1,2,"Lehmann, Miss. Bertha",female,17.0,0,0,SC 1748,12.0,,C
390,391,1,1,"Carter, Mr. William Ernest",male,36.0,1,2,113760,120.0,B96 B98,S
391,392,1,3,"Jansson, Mr. Carl Olof",male,21.0,0,0,350034,7.7958,,S
392,393,0,3,"Gustafsson, Mr. Johan Birger",male,28.0,2,0,3101277,7.925,,S
393,394,1,1,"Newell, Miss. Marjorie",female,23.0,1,0,35273,113.275,D36,C
394,395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24.0,0,2,PP 9549,16.7,G6,S
395,396,0,3,"Johansson, Mr. Erik",male,22.0,0,0,350052,7.7958,,S
396,397,0,3,"Olsson, Miss. Elina",female,31.0,0,0,350407,7.8542,,S
397,398,0,2,"McKane, Mr. Peter David",male,46.0,0,0,28403,26.0,,S
398,399,0,2,"Pain, Dr. Alfred",male,23.0,0,0,244278,10.5,,S
399,400,1,2,"Trout, Mrs. William H (Jessie L)",female,28.0,0,0,240929,12.65,,S
400,401,1,3,"Niskanen, Mr. Juha",male,39.0,0,0,STON/O 2. 3101289,7.925,,S
401,402,0,3,"Adams, Mr. John",male,26.0,0,0,341826,8.05,,S
402,403,0,3,"Jussila, Miss. Mari Aina",female,21.0,1,0,4137,9.825,,S
403,404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28.0,1,0,STON/O2. 3101279,15.85,,S
404,405,0,3,"Oreskovic, Miss. Marija",female,20.0,0,0,315096,8.6625,,S
405,406,0,2,"Gale, Mr. Shadrach",male,34.0,1,0,28664,21.0,,S
406,407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51.0,0,0,347064,7.75,,S
407,408,1,2,"Richards, Master. William Rowe",male,3.0,1,1,29106,18.75,,S
408,409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21.0,0,0,312992,7.775,,S
409,410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
410,411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
411,412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
412,413,1,1,"Minahan, Miss. Daisy E",female,33.0,1,0,19928,90.0,C78,Q
413,414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0.0,,S
414,415,1,3,"Sundman, Mr. Johan Julian",male,44.0,0,0,STON/O 2. 3101269,7.925,,S
415,416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
416,417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34.0,1,1,28220,32.5,,S
417,418,1,2,"Silven, Miss. Lyyli Karoliina",female,18.0,0,2,250652,13.0,,S
418,419,0,2,"Matthews, Mr. William John",male,30.0,0,0,28228,13.0,,S
419,420,0,3,"Van Impe, Miss. Catharina",female,10.0,0,2,345773,24.15,,S
420,421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
421,422,0,3,"Charters, Mr. David",male,21.0,0,0,A/5. 13032,7.7333,,Q
422,423,0,3,"Zimmerman, Mr. Leo",male,29.0,0,0,315082,7.875,,S
423,424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28.0,1,1,347080,14.4,,S
424,425,0,3,"Rosblom, Mr. Viktor Richard",male,18.0,1,1,370129,20.2125,,S
425,426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
426,427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28.0,1,0,2003,26.0,,S
427,428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19.0,0,0,250655,26.0,,S
428,429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
429,430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32.0,0,0,SOTON/O.Q. 392078,8.05,E10,S
430,431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28.0,0,0,110564,26.55,C52,S
431,432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
432,433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42.0,1,0,SC/AH 3085,26.0,,S
433,434,0,3,"Kallio, Mr. Nikolai Erland",male,17.0,0,0,STON/O 2. 3101274,7.125,,S
434,435,0,1,"Silvey, Mr. William Baird",male,50.0,1,0,13507,55.9,E44,S
435,436,1,1,"Carter, Miss. Lucile Polk",female,14.0,1,2,113760,120.0,B96 B98,S
436,437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21.0,2,2,W./C. 6608,34.375,,S
437,438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24.0,2,3,29106,18.75,,S
438,439,0,1,"Fortune, Mr. Mark",male,64.0,1,4,19950,263.0,C23 C25 C27,S
439,440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31.0,0,0,C.A. 18723,10.5,,S
440,441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45.0,1,1,F.C.C. 13529,26.25,,S
441,442,0,3,"Hampe, Mr. Leon",male,20.0,0,0,345769,9.5,,S
442,443,0,3,"Petterson, Mr. Johan Emil",male,25.0,1,0,347076,7.775,,S
443,444,1,2,"Reynaldo, Ms. Encarnacion",female,28.0,0,0,230434,13.0,,S
444,445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
445,446,1,1,"Dodge, Master. Washington",male,4.0,0,2,33638,81.8583,A34,S
446,447,1,2,"Mellinger, Miss. Madeleine Violet",female,13.0,0,1,250644,19.5,,S
447,448,1,1,"Seward, Mr. Frederic Kimber",male,34.0,0,0,113794,26.55,,S
448,449,1,3,"Baclini, Miss. Marie Catherine",female,5.0,2,1,2666,19.2583,,C
449,450,1,1,"Peuchen, Major. Arthur Godfrey",male,52.0,0,0,113786,30.5,C104,S
450,451,0,2,"West, Mr. Edwy Arthur",male,36.0,1,2,C.A. 34651,27.75,,S
451,452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
452,453,0,1,"Foreman, Mr. Benjamin Laventall",male,30.0,0,0,113051,27.75,C111,C
453,454,1,1,"Goldenberg, Mr. Samuel L",male,49.0,1,0,17453,89.1042,C92,C
454,455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
455,456,1,3,"Jalsevac, Mr. Ivan",male,29.0,0,0,349240,7.8958,,C
456,457,0,1,"Millet, Mr. Francis Davis",male,65.0,0,0,13509,26.55,E38,S
457,458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
458,459,1,2,"Toomey, Miss. Ellen",female,50.0,0,0,F.C.C. 13531,10.5,,S
459,460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
460,461,1,1,"Anderson, Mr. Harry",male,48.0,0,0,19952,26.55,E12,S
461,462,0,3,"Morley, Mr. William",male,34.0,0,0,364506,8.05,,S
462,463,0,1,"Gee, Mr. Arthur H",male,47.0,0,0,111320,38.5,E63,S
463,464,0,2,"Milling, Mr. Jacob Christian",male,48.0,0,0,234360,13.0,,S
464,465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
465,466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38.0,0,0,SOTON/O.Q. 3101306,7.05,,S
466,467,0,2,"Campbell, Mr. William",male,,0,0,239853,0.0,,S
467,468,0,1,"Smart, Mr. John Montgomery",male,56.0,0,0,113792,26.55,,S
468,469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
469,470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
470,471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
471,472,0,3,"Cacic, Mr. Luka",male,38.0,0,0,315089,8.6625,,S
472,473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33.0,1,2,C.A. 34651,27.75,,S
473,474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23.0,0,0,SC/AH Basle 541,13.7917,D,C
474,475,0,3,"Strandberg, Miss. Ida Sofia",female,22.0,0,0,7553,9.8375,,S
475,476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52.0,A14,S
476,477,0,2,"Renouf, Mr. Peter Henry",male,34.0,1,0,31027,21.0,,S
477,478,0,3,"Braund, Mr. Lewis Richard",male,29.0,1,0,3460,7.0458,,S
478,479,0,3,"Karlsson, Mr. Nils August",male,22.0,0,0,350060,7.5208,,S
479,480,1,3,"Hirvonen, Miss. Hildur E",female,2.0,0,1,3101298,12.2875,,S
480,481,0,3,"Goodwin, Master. Harold Victor",male,9.0,5,2,CA 2144,46.9,,S
481,482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0.0,,S
482,483,0,3,"Rouse, Mr. Richard Henry",male,50.0,0,0,A/5 3594,8.05,,S
483,484,1,3,"Turkula, Mrs. (Hedwig)",female,63.0,0,0,4134,9.5875,,S
484,485,1,1,"Bishop, Mr. Dickinson H",male,25.0,1,0,11967,91.0792,B49,C
485,486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
486,487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35.0,1,0,19943,90.0,C93,S
487,488,0,1,"Kent, Mr. Edward Austin",male,58.0,0,0,11771,29.7,B37,C
488,489,0,3,"Somerton, Mr. Francis William",male,30.0,0,0,A.5. 18509,8.05,,S
489,490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9.0,1,1,C.A. 37671,15.9,,S
490,491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
491,492,0,3,"Windelov, Mr. Einar",male,21.0,0,0,SOTON/OQ 3101317,7.25,,S
492,493,0,1,"Molson, Mr. Harry Markland",male,55.0,0,0,113787,30.5,C30,S
493,494,0,1,"Artagaveytia, Mr. Ramon",male,71.0,0,0,PC 17609,49.5042,,C
494,495,0,3,"Stanley, Mr. Edward Roland",male,21.0,0,0,A/4 45380,8.05,,S
495,496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
496,497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54.0,1,0,36947,78.2667,D20,C
497,498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
498,499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25.0,1,2,113781,151.55,C22 C26,S
499,500,0,3,"Svensson, Mr. Olof",male,24.0,0,0,350035,7.7958,,S
500,501,0,3,"Calic, Mr. Petar",male,17.0,0,0,315086,8.6625,,S
501,502,0,3,"Canavan, Miss. Mary",female,21.0,0,0,364846,7.75,,Q
502,503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
503,504,0,3,"Laitinen, Miss. Kristina Sofia",female,37.0,0,0,4135,9.5875,,S
504,505,1,1,"Maioni, Miss. Roberta",female,16.0,0,0,110152,86.5,B79,S
505,506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18.0,1,0,PC 17758,108.9,C65,C
506,507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33.0,0,2,26360,26.0,,S
507,508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
508,509,0,3,"Olsen, Mr. Henry Margido",male,28.0,0,0,C 4001,22.525,,S
509,510,1,3,"Lang, Mr. Fang",male,26.0,0,0,1601,56.4958,,S
510,511,1,3,"Daly, Mr. Eugene Patrick",male,29.0,0,0,382651,7.75,,Q
511,512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
512,513,1,1,"McGough, Mr. James Robert",male,36.0,0,0,PC 17473,26.2875,E25,S
513,514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54.0,1,0,PC 17603,59.4,,C
514,515,0,3,"Coleff, Mr. Satio",male,24.0,0,0,349209,7.4958,,S
515,516,0,1,"Walker, Mr. William Anderson",male,47.0,0,0,36967,34.0208,D46,S
516,517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34.0,0,0,C.A. 34260,10.5,F33,S
517,518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
518,519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36.0,1,0,226875,26.0,,S
519,520,0,3,"Pavlovic, Mr. Stefo",male,32.0,0,0,349242,7.8958,,S
520,521,1,1,"Perreault, Miss. Anne",female,30.0,0,0,12749,93.5,B73,S
521,522,0,3,"Vovk, Mr. Janko",male,22.0,0,0,349252,7.8958,,S
522,523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
523,524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44.0,0,1,111361,57.9792,B18,C
524,525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
525,526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
526,527,1,2,"Ridsdale, Miss. Lucy",female,50.0,0,0,W./C. 14258,10.5,,S
527,528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
528,529,0,3,"Salonen, Mr. Johan Werner",male,39.0,0,0,3101296,7.925,,S
529,530,0,2,"Hocking, Mr. Richard George",male,23.0,2,1,29104,11.5,,S
530,531,1,2,"Quick, Miss. Phyllis May",female,2.0,1,1,26360,26.0,,S
531,532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
532,533,0,3,"Elias, Mr. Joseph Jr",male,17.0,1,1,2690,7.2292,,C
533,534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
534,535,0,3,"Cacic, Miss. Marija",female,30.0,0,0,315084,8.6625,,S
535,536,1,2,"Hart, Miss. Eva Miriam",female,7.0,0,2,F.C.C. 13529,26.25,,S
536,537,0,1,"Butt, Major. Archibald Willingham",male,45.0,0,0,113050,26.55,B38,S
537,538,1,1,"LeRoy, Miss. Bertha",female,30.0,0,0,PC 17761,106.425,,C
538,539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
539,540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22.0,0,2,13568,49.5,B39,C
540,541,1,1,"Crosby, Miss. Harriet R",female,36.0,0,2,WE/P 5735,71.0,B22,S
541,542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9.0,4,2,347082,31.275,,S
542,543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11.0,4,2,347082,31.275,,S
543,544,1,2,"Beane, Mr. Edward",male,32.0,1,0,2908,26.0,,S
544,545,0,1,"Douglas, Mr. Walter Donald",male,50.0,1,0,PC 17761,106.425,C86,C
545,546,0,1,"Nicholson, Mr. Arthur Ernest",male,64.0,0,0,693,26.0,,S
546,547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19.0,1,0,2908,26.0,,S
547,548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
548,549,0,3,"Goldsmith, Mr. Frank John",male,33.0,1,1,363291,20.525,,S
549,550,1,2,"Davies, Master. John Morgan Jr",male,8.0,1,1,C.A. 33112,36.75,,S
550,551,1,1,"Thayer, Mr. John Borland Jr",male,17.0,0,2,17421,110.8833,C70,C
551,552,0,2,"Sharp, Mr. Percival James R",male,27.0,0,0,244358,26.0,,S
552,553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
553,554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22.0,0,0,2620,7.225,,C
554,555,1,3,"Ohman, Miss. Velin",female,22.0,0,0,347085,7.775,,S
555,556,0,1,"Wright, Mr. George",male,62.0,0,0,113807,26.55,,S
556,557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48.0,1,0,11755,39.6,A16,C
557,558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
558,559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39.0,1,1,110413,79.65,E67,S
559,560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36.0,1,0,345572,17.4,,S
560,561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
561,562,0,3,"Sivic, Mr. Husein",male,40.0,0,0,349251,7.8958,,S
562,563,0,2,"Norman, Mr. Robert Douglas",male,28.0,0,0,218629,13.5,,S
563,564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
564,565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
565,566,0,3,"Davies, Mr. Alfred J",male,24.0,2,0,A/4 48871,24.15,,S
566,567,0,3,"Stoytcheff, Mr. Ilia",male,19.0,0,0,349205,7.8958,,S
567,568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29.0,0,4,349909,21.075,,S
568,569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
569,570,1,3,"Jonsson, Mr. Carl",male,32.0,0,0,350417,7.8542,,S
570,571,1,2,"Harris, Mr. George",male,62.0,0,0,S.W./PP 752,10.5,,S
571,572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53.0,2,0,11769,51.4792,C101,S
572,573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36.0,0,0,PC 17474,26.3875,E25,S
573,574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
574,575,0,3,"Rush, Mr. Alfred George John",male,16.0,0,0,A/4. 20589,8.05,,S
575,576,0,3,"Patchett, Mr. George",male,19.0,0,0,358585,14.5,,S
576,577,1,2,"Garside, Miss. Ethel",female,34.0,0,0,243880,13.0,,S
577,578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39.0,1,0,13507,55.9,E44,S
578,579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
579,580,1,3,"Jussila, Mr. Eiriik",male,32.0,0,0,STON/O 2. 3101286,7.925,,S
580,581,1,2,"Christy, Miss. Julie Rachel",female,25.0,1,1,237789,30.0,,S
581,582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39.0,1,1,17421,110.8833,C68,C
582,583,0,2,"Downton, Mr. William James",male,54.0,0,0,28403,26.0,,S
583,584,0,1,"Ross, Mr. John Hugo",male,36.0,0,0,13049,40.125,A10,C
584,585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
585,586,1,1,"Taussig, Miss. Ruth",female,18.0,0,2,110413,79.65,E68,S
586,587,0,2,"Jarvis, Mr. John Denzil",male,47.0,0,0,237565,15.0,,S
587,588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60.0,1,1,13567,79.2,B41,C
588,589,0,3,"Gilinski, Mr. Eliezer",male,22.0,0,0,14973,8.05,,S
589,590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
590,591,0,3,"Rintamaki, Mr. Matti",male,35.0,0,0,STON/O 2. 3101273,7.125,,S
591,592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52.0,1,0,36947,78.2667,D20,C
592,593,0,3,"Elsbury, Mr. William James",male,47.0,0,0,A/5 3902,7.25,,S
593,594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
594,595,0,2,"Chapman, Mr. John Henry",male,37.0,1,0,SC/AH 29037,26.0,,S
595,596,0,3,"Van Impe, Mr. Jean Baptiste",male,36.0,1,1,345773,24.15,,S
596,597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33.0,,S
597,598,0,3,"Johnson, Mr. Alfred",male,49.0,0,0,LINE,0.0,,S
598,599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
599,600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49.0,1,0,PC 17485,56.9292,A20,C
600,601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24.0,2,1,243847,27.0,,S
601,602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
602,603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
603,604,0,3,"Torber, Mr. Ernst William",male,44.0,0,0,364511,8.05,,S
604,605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35.0,0,0,111426,26.55,,C
605,606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36.0,1,0,349910,15.55,,S
606,607,0,3,"Karaic, Mr. Milan",male,30.0,0,0,349246,7.8958,,S
607,608,1,1,"Daniel, Mr. Robert Williams",male,27.0,0,0,113804,30.5,,S
608,609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22.0,1,2,SC/Paris 2123,41.5792,,C
609,610,1,1,"Shutes, Miss. Elizabeth W",female,40.0,0,0,PC 17582,153.4625,C125,S
610,611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39.0,1,5,347082,31.275,,S
611,612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
612,613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
613,614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
614,615,0,3,"Brocklebank, Mr. William Alfred",male,35.0,0,0,364512,8.05,,S
615,616,1,2,"Herman, Miss. Alice",female,24.0,1,2,220845,65.0,,S
616,617,0,3,"Danbom, Mr. Ernst Gilbert",male,34.0,1,1,347080,14.4,,S
617,618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26.0,1,0,A/5. 3336,16.1,,S
618,619,1,2,"Becker, Miss. Marion Louise",female,4.0,2,1,230136,39.0,F4,S
619,620,0,2,"Gavey, Mr. Lawrence",male,26.0,0,0,31028,10.5,,S
620,621,0,3,"Yasbeck, Mr. Antoni",male,27.0,1,0,2659,14.4542,,C
621,622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42.0,1,0,11753,52.5542,D19,S
622,623,1,3,"Nakid, Mr. Sahid",male,20.0,1,1,2653,15.7417,,C
623,624,0,3,"Hansen, Mr. Henry Damsgaard",male,21.0,0,0,350029,7.8542,,S
624,625,0,3,"Bowen, Mr. David John ""Dai""",male,21.0,0,0,54636,16.1,,S
625,626,0,1,"Sutton, Mr. Frederick",male,61.0,0,0,36963,32.3208,D50,S
626,627,0,2,"Kirkland, Rev. Charles Leonard",male,57.0,0,0,219533,12.35,,Q
627,628,1,1,"Longley, Miss. Gretchen Fiske",female,21.0,0,0,13502,77.9583,D9,S
628,629,0,3,"Bostandyeff, Mr. Guentcho",male,26.0,0,0,349224,7.8958,,S
629,630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
630,631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80.0,0,0,27042,30.0,A23,S
631,632,0,3,"Lundahl, Mr. Johan Svensson",male,51.0,0,0,347743,7.0542,,S
632,633,1,1,"Stahelin-Maeglin, Dr. Max",male,32.0,0,0,13214,30.5,B50,C
633,634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0.0,,S
634,635,0,3,"Skoog, Miss. Mabel",female,9.0,3,2,347088,27.9,,S
635,636,1,2,"Davis, Miss. Mary",female,28.0,0,0,237668,13.0,,S
636,637,0,3,"Leinonen, Mr. Antti Gustaf",male,32.0,0,0,STON/O 2. 3101292,7.925,,S
637,638,0,2,"Collyer, Mr. Harvey",male,31.0,1,1,C.A. 31921,26.25,,S
638,639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41.0,0,5,3101295,39.6875,,S
639,640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
640,641,0,3,"Jensen, Mr. Hans Peder",male,20.0,0,0,350050,7.8542,,S
641,642,1,1,"Sagesser, Mlle. Emma",female,24.0,0,0,PC 17477,69.3,B35,C
642,643,0,3,"Skoog, Miss. Margit Elizabeth",female,2.0,3,2,347088,27.9,,S
643,644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
644,645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
645,646,1,1,"Harper, Mr. Henry Sleeper",male,48.0,1,0,PC 17572,76.7292,D33,C
646,647,0,3,"Cor, Mr. Liudevit",male,19.0,0,0,349231,7.8958,,S
647,648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56.0,0,0,13213,35.5,A26,C
648,649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
649,650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23.0,0,0,CA. 2314,7.55,,S
650,651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
651,652,1,2,"Doling, Miss. Elsie",female,18.0,0,1,231919,23.0,,S
652,653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21.0,0,0,8475,8.4333,,S
653,654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
654,655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18.0,0,0,365226,6.75,,Q
655,656,0,2,"Hickman, Mr. Leonard Mark",male,24.0,2,0,S.O.C. 14879,73.5,,S
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657,658,0,3,"Bourke, Mrs. John (Catherine)",female,32.0,1,1,364849,15.5,,Q
658,659,0,2,"Eitemiller, Mr. George Floyd",male,23.0,0,0,29751,13.0,,S
659,660,0,1,"Newell, Mr. Arthur Webster",male,58.0,0,2,35273,113.275,D48,C
660,661,1,1,"Frauenthal, Dr. Henry William",male,50.0,2,0,PC 17611,133.65,,S
661,662,0,3,"Badt, Mr. Mohamed",male,40.0,0,0,2623,7.225,,C
662,663,0,1,"Colley, Mr. Edward Pomeroy",male,47.0,0,0,5727,25.5875,E58,S
663,664,0,3,"Coleff, Mr. Peju",male,36.0,0,0,349210,7.4958,,S
664,665,1,3,"Lindqvist, Mr. Eino William",male,20.0,1,0,STON/O 2. 3101285,7.925,,S
665,666,0,2,"Hickman, Mr. Lewis",male,32.0,2,0,S.O.C. 14879,73.5,,S
666,667,0,2,"Butler, Mr. Reginald Fenton",male,25.0,0,0,234686,13.0,,S
667,668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
668,669,0,3,"Cook, Mr. Jacob",male,43.0,0,0,A/5 3536,8.05,,S
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670,671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40.0,1,1,29750,39.0,,S
671,672,0,1,"Davidson, Mr. Thornton",male,31.0,1,0,F.C. 12750,52.0,B71,S
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673,674,1,2,"Wilhelms, Mr. Charles",male,31.0,0,0,244270,13.0,,S
674,675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0.0,,S
675,676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18.0,0,0,349912,7.775,,S
676,677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
677,678,1,3,"Turja, Miss. Anna Sofia",female,18.0,0,0,4138,9.8417,,S
678,679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43.0,1,6,CA 2144,46.9,,S
679,680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36.0,0,1,PC 17755,512.3292,B51 B53 B55,C
680,681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
681,682,1,1,"Hassab, Mr. Hammad",male,27.0,0,0,PC 17572,76.7292,D49,C
682,683,0,3,"Olsvigen, Mr. Thor Anderson",male,20.0,0,0,6563,9.225,,S
683,684,0,3,"Goodwin, Mr. Charles Edward",male,14.0,5,2,CA 2144,46.9,,S
684,685,0,2,"Brown, Mr. Thomas William Solomon",male,60.0,1,1,29750,39.0,,S
685,686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25.0,1,2,SC/Paris 2123,41.5792,,C
686,687,0,3,"Panula, Mr. Jaako Arnold",male,14.0,4,1,3101295,39.6875,,S
687,688,0,3,"Dakic, Mr. Branko",male,19.0,0,0,349228,10.1708,,S
688,689,0,3,"Fischer, Mr. Eberhard Thelander",male,18.0,0,0,350036,7.7958,,S
689,690,1,1,"Madill, Miss. Georgette Alexandra",female,15.0,0,1,24160,211.3375,B5,S
690,691,1,1,"Dick, Mr. Albert Adrian",male,31.0,1,0,17474,57.0,B20,S
691,692,1,3,"Karun, Miss. Manca",female,4.0,0,1,349256,13.4167,,C
692,693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
693,694,0,3,"Saad, Mr. Khalil",male,25.0,0,0,2672,7.225,,C
694,695,0,1,"Weir, Col. John",male,60.0,0,0,113800,26.55,,S
695,696,0,2,"Chapman, Mr. Charles Henry",male,52.0,0,0,248731,13.5,,S
696,697,0,3,"Kelly, Mr. James",male,44.0,0,0,363592,8.05,,S
697,698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
698,699,0,1,"Thayer, Mr. John Borland",male,49.0,1,1,17421,110.8833,C68,C
699,700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42.0,0,0,348121,7.65,F G63,S
700,701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18.0,1,0,PC 17757,227.525,C62 C64,C
701,702,1,1,"Silverthorne, Mr. Spencer Victor",male,35.0,0,0,PC 17475,26.2875,E24,S
702,703,0,3,"Barbara, Miss. Saiide",female,18.0,0,1,2691,14.4542,,C
703,704,0,3,"Gallagher, Mr. Martin",male,25.0,0,0,36864,7.7417,,Q
704,705,0,3,"Hansen, Mr. Henrik Juul",male,26.0,1,0,350025,7.8542,,S
705,706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39.0,0,0,250655,26.0,,S
706,707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45.0,0,0,223596,13.5,,S
707,708,1,1,"Calderhead, Mr. Edward Pennington",male,42.0,0,0,PC 17476,26.2875,E24,S
708,709,1,1,"Cleaver, Miss. Alice",female,22.0,0,0,113781,151.55,,S
709,710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
710,711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24.0,0,0,PC 17482,49.5042,C90,C
711,712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
712,713,1,1,"Taylor, Mr. Elmer Zebley",male,48.0,1,0,19996,52.0,C126,S
713,714,0,3,"Larsson, Mr. August Viktor",male,29.0,0,0,7545,9.4833,,S
714,715,0,2,"Greenberg, Mr. Samuel",male,52.0,0,0,250647,13.0,,S
715,716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19.0,0,0,348124,7.65,F G73,S
716,717,1,1,"Endres, Miss. Caroline Louise",female,38.0,0,0,PC 17757,227.525,C45,C
717,718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27.0,0,0,34218,10.5,E101,S
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719,720,0,3,"Johnson, Mr. Malkolm Joackim",male,33.0,0,0,347062,7.775,,S
720,721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6.0,0,1,248727,33.0,,S
721,722,0,3,"Jensen, Mr. Svend Lauritz",male,17.0,1,0,350048,7.0542,,S
722,723,0,2,"Gillespie, Mr. William Henry",male,34.0,0,0,12233,13.0,,S
723,724,0,2,"Hodges, Mr. Henry Price",male,50.0,0,0,250643,13.0,,S
724,725,1,1,"Chambers, Mr. Norman Campbell",male,27.0,1,0,113806,53.1,E8,S
725,726,0,3,"Oreskovic, Mr. Luka",male,20.0,0,0,315094,8.6625,,S
726,727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30.0,3,0,31027,21.0,,S
727,728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
728,729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25.0,1,0,236853,26.0,,S
729,730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25.0,1,0,STON/O2. 3101271,7.925,,S
730,731,1,1,"Allen, Miss. Elisabeth Walton",female,29.0,0,0,24160,211.3375,B5,S
731,732,0,3,"Hassan, Mr. Houssein G N",male,11.0,0,0,2699,18.7875,,C
732,733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0.0,,S
733,734,0,2,"Berriman, Mr. William John",male,23.0,0,0,28425,13.0,,S
734,735,0,2,"Troupiansky, Mr. Moses Aaron",male,23.0,0,0,233639,13.0,,S
735,736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
736,737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48.0,1,3,W./C. 6608,34.375,,S
737,738,1,1,"Lesurer, Mr. Gustave J",male,35.0,0,0,PC 17755,512.3292,B101,C
738,739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
739,740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
740,741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30.0,D45,S
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743,744,0,3,"McNamee, Mr. Neal",male,24.0,1,0,376566,16.1,,S
744,745,1,3,"Stranden, Mr. Juho",male,31.0,0,0,STON/O 2. 3101288,7.925,,S
745,746,0,1,"Crosby, Capt. Edward Gifford",male,70.0,1,1,WE/P 5735,71.0,B22,S
746,747,0,3,"Abbott, Mr. Rossmore Edward",male,16.0,1,1,C.A. 2673,20.25,,S
747,748,1,2,"Sinkkonen, Miss. Anna",female,30.0,0,0,250648,13.0,,S
748,749,0,1,"Marvin, Mr. Daniel Warner",male,19.0,1,0,113773,53.1,D30,S
749,750,0,3,"Connaghton, Mr. Michael",male,31.0,0,0,335097,7.75,,Q
750,751,1,2,"Wells, Miss. Joan",female,4.0,1,1,29103,23.0,,S
751,752,1,3,"Moor, Master. Meier",male,6.0,0,1,392096,12.475,E121,S
752,753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33.0,0,0,345780,9.5,,S
753,754,0,3,"Jonkoff, Mr. Lalio",male,23.0,0,0,349204,7.8958,,S
754,755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48.0,1,2,220845,65.0,,S
755,756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
756,757,0,3,"Carlsson, Mr. August Sigfrid",male,28.0,0,0,350042,7.7958,,S
757,758,0,2,"Bailey, Mr. Percy Andrew",male,18.0,0,0,29108,11.5,,S
758,759,0,3,"Theobald, Mr. Thomas Leonard",male,34.0,0,0,363294,8.05,,S
759,760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33.0,0,0,110152,86.5,B77,S
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761,762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41.0,0,0,SOTON/O2 3101272,7.125,,S
762,763,1,3,"Barah, Mr. Hanna Assi",male,20.0,0,0,2663,7.2292,,C
763,764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36.0,1,2,113760,120.0,B96 B98,S
764,765,0,3,"Eklund, Mr. Hans Linus",male,16.0,0,0,347074,7.775,,S
765,766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51.0,1,0,13502,77.9583,D11,S
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768,769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
769,770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32.0,0,0,8471,8.3625,,S
770,771,0,3,"Lievens, Mr. Rene Aime",male,24.0,0,0,345781,9.5,,S
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772,773,0,2,"Mack, Mrs. (Mary)",female,57.0,0,0,S.O./P.P. 3,10.5,E77,S
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776,777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
777,778,1,3,"Emanuel, Miss. Virginia Ethel",female,5.0,0,0,364516,12.475,,S
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779,780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43.0,0,1,24160,211.3375,B3,S
780,781,1,3,"Ayoub, Miss. Banoura",female,13.0,0,0,2687,7.2292,,C
781,782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17.0,1,0,17474,57.0,B20,S
782,783,0,1,"Long, Mr. Milton Clyde",male,29.0,0,0,113501,30.0,D6,S
783,784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
784,785,0,3,"Ali, Mr. William",male,25.0,0,0,SOTON/O.Q. 3101312,7.05,,S
785,786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25.0,0,0,374887,7.25,,S
786,787,1,3,"Sjoblom, Miss. Anna Sofia",female,18.0,0,0,3101265,7.4958,,S
787,788,0,3,"Rice, Master. George Hugh",male,8.0,4,1,382652,29.125,,Q
788,789,1,3,"Dean, Master. Bertram Vere",male,1.0,1,2,C.A. 2315,20.575,,S
789,790,0,1,"Guggenheim, Mr. Benjamin",male,46.0,0,0,PC 17593,79.2,B82 B84,C
790,791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
791,792,0,2,"Gaskell, Mr. Alfred",male,16.0,0,0,239865,26.0,,S
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793,794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
794,795,0,3,"Dantcheff, Mr. Ristiu",male,25.0,0,0,349203,7.8958,,S
795,796,0,2,"Otter, Mr. Richard",male,39.0,0,0,28213,13.0,,S
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798,799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30.0,0,0,2685,7.2292,,C
799,800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30.0,1,1,345773,24.15,,S
800,801,0,2,"Ponesell, Mr. Martin",male,34.0,0,0,250647,13.0,,S
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802,803,1,1,"Carter, Master. William Thornton II",male,11.0,1,2,113760,120.0,B96 B98,S
803,804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
804,805,1,3,"Hedman, Mr. Oskar Arvid",male,27.0,0,0,347089,6.975,,S
805,806,0,3,"Johansson, Mr. Karl Johan",male,31.0,0,0,347063,7.775,,S
806,807,0,1,"Andrews, Mr. Thomas Jr",male,39.0,0,0,112050,0.0,A36,S
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808,809,0,2,"Meyer, Mr. August",male,39.0,0,0,248723,13.0,,S
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812,813,0,2,"Slemen, Mr. Richard James",male,35.0,0,0,28206,10.5,,S
813,814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6.0,4,2,347082,31.275,,S
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817,818,0,2,"Mallet, Mr. Albert",male,31.0,1,1,S.C./PARIS 2079,37.0042,,C
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819,820,0,3,"Skoog, Master. Karl Thorsten",male,10.0,3,2,347088,27.9,,S
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827,828,1,2,"Mallet, Master. Andre",male,1.0,0,2,S.C./PARIS 2079,37.0042,,C
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831,832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
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833,834,0,3,"Augustsson, Mr. Albert",male,23.0,0,0,347468,7.8542,,S
834,835,0,3,"Allum, Mr. Owen George",male,18.0,0,0,2223,8.3,,S
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838,839,1,3,"Chip, Mr. Chang",male,32.0,0,0,1601,56.4958,,S
839,840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
840,841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20.0,0,0,SOTON/O2 3101287,7.925,,S
841,842,0,2,"Mudd, Mr. Thomas Charles",male,16.0,0,0,S.O./P.P. 3,10.5,,S
842,843,1,1,"Serepeca, Miss. Augusta",female,30.0,0,0,113798,31.0,,C
843,844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
844,845,0,3,"Culumovic, Mr. Jeso",male,17.0,0,0,315090,8.6625,,S
845,846,0,3,"Abbing, Mr. Anthony",male,42.0,0,0,C.A. 5547,7.55,,S
846,847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
847,848,0,3,"Markoff, Mr. Marin",male,35.0,0,0,349213,7.8958,,C
848,849,0,2,"Harper, Rev. John",male,28.0,0,1,248727,33.0,,S
849,850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
850,851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4.0,4,2,347082,31.275,,S
851,852,0,3,"Svensson, Mr. Johan",male,74.0,0,0,347060,7.775,,S
852,853,0,3,"Boulos, Miss. Nourelain",female,9.0,1,1,2678,15.2458,,C
853,854,1,1,"Lines, Miss. Mary Conover",female,16.0,0,1,PC 17592,39.4,D28,S
854,855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44.0,1,0,244252,26.0,,S
855,856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18.0,0,1,392091,9.35,,S
856,857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45.0,1,1,36928,164.8667,,S
857,858,1,1,"Daly, Mr. Peter Denis ",male,51.0,0,0,113055,26.55,E17,S
858,859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24.0,0,3,2666,19.2583,,C
859,860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
860,861,0,3,"Hansen, Mr. Claus Peter",male,41.0,2,0,350026,14.1083,,S
861,862,0,2,"Giles, Mr. Frederick Edward",male,21.0,1,0,28134,11.5,,S
862,863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48.0,0,0,17466,25.9292,D17,S
863,864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
864,865,0,2,"Gill, Mr. John William",male,24.0,0,0,233866,13.0,,S
865,866,1,2,"Bystrom, Mrs. (Karolina)",female,42.0,0,0,236852,13.0,,S
866,867,1,2,"Duran y More, Miss. Asuncion",female,27.0,1,0,SC/PARIS 2149,13.8583,,C
867,868,0,1,"Roebling, Mr. Washington Augustus II",male,31.0,0,0,PC 17590,50.4958,A24,S
868,869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
869,870,1,3,"Johnson, Master. Harold Theodor",male,4.0,1,1,347742,11.1333,,S
870,871,0,3,"Balkic, Mr. Cerin",male,26.0,0,0,349248,7.8958,,S
871,872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47.0,1,1,11751,52.5542,D35,S
872,873,0,1,"Carlsson, Mr. Frans Olof",male,33.0,0,0,695,5.0,B51 B53 B55,S
873,874,0,3,"Vander Cruyssen, Mr. Victor",male,47.0,0,0,345765,9.0,,S
874,875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28.0,1,0,P/PP 3381,24.0,,C
875,876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15.0,0,0,2667,7.225,,C
876,877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20.0,0,0,7534,9.8458,,S
877,878,0,3,"Petroff, Mr. Nedelio",male,19.0,0,0,349212,7.8958,,S
878,879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
879,880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56.0,0,1,11767,83.1583,C50,C
880,881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25.0,0,1,230433,26.0,,S
881,882,0,3,"Markun, Mr. Johann",male,33.0,0,0,349257,7.8958,,S
882,883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22.0,0,0,7552,10.5167,,S
883,884,0,2,"Banfield, Mr. Frederick James",male,28.0,0,0,C.A./SOTON 34068,10.5,,S
884,885,0,3,"Sutehall, Mr. Henry Jr",male,25.0,0,0,SOTON/OQ 392076,7.05,,S
885,886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39.0,0,5,382652,29.125,,Q
886,887,0,2,"Montvila, Rev. Juozas",male,27.0,0,0,211536,13.0,,S
887,888,1,1,"Graham, Miss. Margaret Edith",female,19.0,0,0,112053,30.0,B42,S
888,889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
889,890,1,1,"Behr, Mr. Karl Howell",male,26.0,0,0,111369,30.0,C148,C
890,891,0,3,"Dooley, Mr. Patrick",male,32.0,0,0,370376,7.75,,Q
1 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 S
3 1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.0 1 0 PC 17599 71.2833 C85 C
4 2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.925 S
5 3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1 C123 S
6 4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.05 S
7 5 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
8 6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
9 7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.075 S
10 8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 S
11 9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 C
12 10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7 G6 S
13 11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.55 C103 S
14 12 13 0 3 Saundercock, Mr. William Henry male 20.0 0 0 A/5. 2151 8.05 S
15 13 14 0 3 Andersson, Mr. Anders Johan male 39.0 1 5 347082 31.275 S
16 14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 S
17 15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 0 248706 16.0 S
18 16 17 0 3 Rice, Master. Eugene male 2.0 4 1 382652 29.125 Q
19 17 18 1 2 Williams, Mr. Charles Eugene male 0 0 244373 13.0 S
20 18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.0 1 0 345763 18.0 S
21 19 20 1 3 Masselmani, Mrs. Fatima female 0 0 2649 7.225 C
22 20 21 0 2 Fynney, Mr. Joseph J male 35.0 0 0 239865 26.0 S
23 21 22 1 2 Beesley, Mr. Lawrence male 34.0 0 0 248698 13.0 D56 S
24 22 23 1 3 McGowan, Miss. Anna "Annie" female 15.0 0 0 330923 8.0292 Q
25 23 24 1 1 Sloper, Mr. William Thompson male 28.0 0 0 113788 35.5 A6 S
26 24 25 0 3 Palsson, Miss. Torborg Danira female 8.0 3 1 349909 21.075 S
27 25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38.0 1 5 347077 31.3875 S
28 26 27 0 3 Emir, Mr. Farred Chehab male 0 0 2631 7.225 C
29 27 28 0 1 Fortune, Mr. Charles Alexander male 19.0 3 2 19950 263.0 C23 C25 C27 S
30 28 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female 0 0 330959 7.8792 Q
31 29 30 0 3 Todoroff, Mr. Lalio male 0 0 349216 7.8958 S
32 30 31 0 1 Uruchurtu, Don. Manuel E male 40.0 0 0 PC 17601 27.7208 C
33 31 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female 1 0 PC 17569 146.5208 B78 C
34 32 33 1 3 Glynn, Miss. Mary Agatha female 0 0 335677 7.75 Q
35 33 34 0 2 Wheadon, Mr. Edward H male 66.0 0 0 C.A. 24579 10.5 S
36 34 35 0 1 Meyer, Mr. Edgar Joseph male 28.0 1 0 PC 17604 82.1708 C
37 35 36 0 1 Holverson, Mr. Alexander Oskar male 42.0 1 0 113789 52.0 S
38 36 37 1 3 Mamee, Mr. Hanna male 0 0 2677 7.2292 C
39 37 38 0 3 Cann, Mr. Ernest Charles male 21.0 0 0 A./5. 2152 8.05 S
40 38 39 0 3 Vander Planke, Miss. Augusta Maria female 18.0 2 0 345764 18.0 S
41 39 40 1 3 Nicola-Yarred, Miss. Jamila female 14.0 1 0 2651 11.2417 C
42 40 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.0 1 0 7546 9.475 S
43 41 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27.0 1 0 11668 21.0 S
44 42 43 0 3 Kraeff, Mr. Theodor male 0 0 349253 7.8958 C
45 43 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.0 1 2 SC/Paris 2123 41.5792 C
46 44 45 1 3 Devaney, Miss. Margaret Delia female 19.0 0 0 330958 7.8792 Q
47 45 46 0 3 Rogers, Mr. William John male 0 0 S.C./A.4. 23567 8.05 S
48 46 47 0 3 Lennon, Mr. Denis male 1 0 370371 15.5 Q
49 47 48 1 3 O'Driscoll, Miss. Bridget female 0 0 14311 7.75 Q
50 48 49 0 3 Samaan, Mr. Youssef male 2 0 2662 21.6792 C
51 49 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.0 1 0 349237 17.8 S
52 50 51 0 3 Panula, Master. Juha Niilo male 7.0 4 1 3101295 39.6875 S
53 51 52 0 3 Nosworthy, Mr. Richard Cater male 21.0 0 0 A/4. 39886 7.8 S
54 52 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0 1 0 PC 17572 76.7292 D33 C
55 53 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29.0 1 0 2926 26.0 S
56 54 55 0 1 Ostby, Mr. Engelhart Cornelius male 65.0 0 1 113509 61.9792 B30 C
57 55 56 1 1 Woolner, Mr. Hugh male 0 0 19947 35.5 C52 S
58 56 57 1 2 Rugg, Miss. Emily female 21.0 0 0 C.A. 31026 10.5 S
59 57 58 0 3 Novel, Mr. Mansouer male 28.5 0 0 2697 7.2292 C
60 58 59 1 2 West, Miss. Constance Mirium female 5.0 1 2 C.A. 34651 27.75 S
61 59 60 0 3 Goodwin, Master. William Frederick male 11.0 5 2 CA 2144 46.9 S
62 60 61 0 3 Sirayanian, Mr. Orsen male 22.0 0 0 2669 7.2292 C
63 61 62 1 1 Icard, Miss. Amelie female 38.0 0 0 113572 80.0 B28
64 62 63 0 1 Harris, Mr. Henry Birkhardt male 45.0 1 0 36973 83.475 C83 S
65 63 64 0 3 Skoog, Master. Harald male 4.0 3 2 347088 27.9 S
66 64 65 0 1 Stewart, Mr. Albert A male 0 0 PC 17605 27.7208 C
67 65 66 1 3 Moubarek, Master. Gerios male 1 1 2661 15.2458 C
68 66 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.0 0 0 C.A. 29395 10.5 F33 S
69 67 68 0 3 Crease, Mr. Ernest James male 19.0 0 0 S.P. 3464 8.1583 S
70 68 69 1 3 Andersson, Miss. Erna Alexandra female 17.0 4 2 3101281 7.925 S
71 69 70 0 3 Kink, Mr. Vincenz male 26.0 2 0 315151 8.6625 S
72 70 71 0 2 Jenkin, Mr. Stephen Curnow male 32.0 0 0 C.A. 33111 10.5 S
73 71 72 0 3 Goodwin, Miss. Lillian Amy female 16.0 5 2 CA 2144 46.9 S
74 72 73 0 2 Hood, Mr. Ambrose Jr male 21.0 0 0 S.O.C. 14879 73.5 S
75 73 74 0 3 Chronopoulos, Mr. Apostolos male 26.0 1 0 2680 14.4542 C
76 74 75 1 3 Bing, Mr. Lee male 32.0 0 0 1601 56.4958 S
77 75 76 0 3 Moen, Mr. Sigurd Hansen male 25.0 0 0 348123 7.65 F G73 S
78 76 77 0 3 Staneff, Mr. Ivan male 0 0 349208 7.8958 S
79 77 78 0 3 Moutal, Mr. Rahamin Haim male 0 0 374746 8.05 S
80 78 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.0 S
81 79 80 1 3 Dowdell, Miss. Elizabeth female 30.0 0 0 364516 12.475 S
82 80 81 0 3 Waelens, Mr. Achille male 22.0 0 0 345767 9.0 S
83 81 82 1 3 Sheerlinck, Mr. Jan Baptist male 29.0 0 0 345779 9.5 S
84 82 83 1 3 McDermott, Miss. Brigdet Delia female 0 0 330932 7.7875 Q
85 83 84 0 1 Carrau, Mr. Francisco M male 28.0 0 0 113059 47.1 S
86 84 85 1 2 Ilett, Miss. Bertha female 17.0 0 0 SO/C 14885 10.5 S
87 85 86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) female 33.0 3 0 3101278 15.85 S
88 86 87 0 3 Ford, Mr. William Neal male 16.0 1 3 W./C. 6608 34.375 S
89 87 88 0 3 Slocovski, Mr. Selman Francis male 0 0 SOTON/OQ 392086 8.05 S
90 88 89 1 1 Fortune, Miss. Mabel Helen female 23.0 3 2 19950 263.0 C23 C25 C27 S
91 89 90 0 3 Celotti, Mr. Francesco male 24.0 0 0 343275 8.05 S
92 90 91 0 3 Christmann, Mr. Emil male 29.0 0 0 343276 8.05 S
93 91 92 0 3 Andreasson, Mr. Paul Edvin male 20.0 0 0 347466 7.8542 S
94 92 93 0 1 Chaffee, Mr. Herbert Fuller male 46.0 1 0 W.E.P. 5734 61.175 E31 S
95 93 94 0 3 Dean, Mr. Bertram Frank male 26.0 1 2 C.A. 2315 20.575 S
96 94 95 0 3 Coxon, Mr. Daniel male 59.0 0 0 364500 7.25 S
97 95 96 0 3 Shorney, Mr. Charles Joseph male 0 0 374910 8.05 S
98 96 97 0 1 Goldschmidt, Mr. George B male 71.0 0 0 PC 17754 34.6542 A5 C
99 97 98 1 1 Greenfield, Mr. William Bertram male 23.0 0 1 PC 17759 63.3583 D10 D12 C
100 98 99 1 2 Doling, Mrs. John T (Ada Julia Bone) female 34.0 0 1 231919 23.0 S
101 99 100 0 2 Kantor, Mr. Sinai male 34.0 1 0 244367 26.0 S
102 100 101 0 3 Petranec, Miss. Matilda female 28.0 0 0 349245 7.8958 S
103 101 102 0 3 Petroff, Mr. Pastcho ("Pentcho") male 0 0 349215 7.8958 S
104 102 103 0 1 White, Mr. Richard Frasar male 21.0 0 1 35281 77.2875 D26 S
105 103 104 0 3 Johansson, Mr. Gustaf Joel male 33.0 0 0 7540 8.6542 S
106 104 105 0 3 Gustafsson, Mr. Anders Vilhelm male 37.0 2 0 3101276 7.925 S
107 105 106 0 3 Mionoff, Mr. Stoytcho male 28.0 0 0 349207 7.8958 S
108 106 107 1 3 Salkjelsvik, Miss. Anna Kristine female 21.0 0 0 343120 7.65 S
109 107 108 1 3 Moss, Mr. Albert Johan male 0 0 312991 7.775 S
110 108 109 0 3 Rekic, Mr. Tido male 38.0 0 0 349249 7.8958 S
111 109 110 1 3 Moran, Miss. Bertha female 1 0 371110 24.15 Q
112 110 111 0 1 Porter, Mr. Walter Chamberlain male 47.0 0 0 110465 52.0 C110 S
113 111 112 0 3 Zabour, Miss. Hileni female 14.5 1 0 2665 14.4542 C
114 112 113 0 3 Barton, Mr. David John male 22.0 0 0 324669 8.05 S
115 113 114 0 3 Jussila, Miss. Katriina female 20.0 1 0 4136 9.825 S
116 114 115 0 3 Attalah, Miss. Malake female 17.0 0 0 2627 14.4583 C
117 115 116 0 3 Pekoniemi, Mr. Edvard male 21.0 0 0 STON/O 2. 3101294 7.925 S
118 116 117 0 3 Connors, Mr. Patrick male 70.5 0 0 370369 7.75 Q
119 117 118 0 2 Turpin, Mr. William John Robert male 29.0 1 0 11668 21.0 S
120 118 119 0 1 Baxter, Mr. Quigg Edmond male 24.0 0 1 PC 17558 247.5208 B58 B60 C
121 119 120 0 3 Andersson, Miss. Ellis Anna Maria female 2.0 4 2 347082 31.275 S
122 120 121 0 2 Hickman, Mr. Stanley George male 21.0 2 0 S.O.C. 14879 73.5 S
123 121 122 0 3 Moore, Mr. Leonard Charles male 0 0 A4. 54510 8.05 S
124 122 123 0 2 Nasser, Mr. Nicholas male 32.5 1 0 237736 30.0708 C
125 123 124 1 2 Webber, Miss. Susan female 32.5 0 0 27267 13.0 E101 S
126 124 125 0 1 White, Mr. Percival Wayland male 54.0 0 1 35281 77.2875 D26 S
127 125 126 1 3 Nicola-Yarred, Master. Elias male 12.0 1 0 2651 11.2417 C
128 126 127 0 3 McMahon, Mr. Martin male 0 0 370372 7.75 Q
129 127 128 1 3 Madsen, Mr. Fridtjof Arne male 24.0 0 0 C 17369 7.1417 S
130 128 129 1 3 Peter, Miss. Anna female 1 1 2668 22.3583 F E69 C
131 129 130 0 3 Ekstrom, Mr. Johan male 45.0 0 0 347061 6.975 S
132 130 131 0 3 Drazenoic, Mr. Jozef male 33.0 0 0 349241 7.8958 C
133 131 132 0 3 Coelho, Mr. Domingos Fernandeo male 20.0 0 0 SOTON/O.Q. 3101307 7.05 S
134 132 133 0 3 Robins, Mrs. Alexander A (Grace Charity Laury) female 47.0 1 0 A/5. 3337 14.5 S
135 133 134 1 2 Weisz, Mrs. Leopold (Mathilde Francoise Pede) female 29.0 1 0 228414 26.0 S
136 134 135 0 2 Sobey, Mr. Samuel James Hayden male 25.0 0 0 C.A. 29178 13.0 S
137 135 136 0 2 Richard, Mr. Emile male 23.0 0 0 SC/PARIS 2133 15.0458 C
138 136 137 1 1 Newsom, Miss. Helen Monypeny female 19.0 0 2 11752 26.2833 D47 S
139 137 138 0 1 Futrelle, Mr. Jacques Heath male 37.0 1 0 113803 53.1 C123 S
140 138 139 0 3 Osen, Mr. Olaf Elon male 16.0 0 0 7534 9.2167 S
141 139 140 0 1 Giglio, Mr. Victor male 24.0 0 0 PC 17593 79.2 B86 C
142 140 141 0 3 Boulos, Mrs. Joseph (Sultana) female 0 2 2678 15.2458 C
143 141 142 1 3 Nysten, Miss. Anna Sofia female 22.0 0 0 347081 7.75 S
144 142 143 1 3 Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck) female 24.0 1 0 STON/O2. 3101279 15.85 S
145 143 144 0 3 Burke, Mr. Jeremiah male 19.0 0 0 365222 6.75 Q
146 144 145 0 2 Andrew, Mr. Edgardo Samuel male 18.0 0 0 231945 11.5 S
147 145 146 0 2 Nicholls, Mr. Joseph Charles male 19.0 1 1 C.A. 33112 36.75 S
148 146 147 1 3 Andersson, Mr. August Edvard ("Wennerstrom") male 27.0 0 0 350043 7.7958 S
149 147 148 0 3 Ford, Miss. Robina Maggie "Ruby" female 9.0 2 2 W./C. 6608 34.375 S
150 148 149 0 2 Navratil, Mr. Michel ("Louis M Hoffman") male 36.5 0 2 230080 26.0 F2 S
151 149 150 0 2 Byles, Rev. Thomas Roussel Davids male 42.0 0 0 244310 13.0 S
152 150 151 0 2 Bateman, Rev. Robert James male 51.0 0 0 S.O.P. 1166 12.525 S
153 151 152 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22.0 1 0 113776 66.6 C2 S
154 152 153 0 3 Meo, Mr. Alfonzo male 55.5 0 0 A.5. 11206 8.05 S
155 153 154 0 3 van Billiard, Mr. Austin Blyler male 40.5 0 2 A/5. 851 14.5 S
156 154 155 0 3 Olsen, Mr. Ole Martin male 0 0 Fa 265302 7.3125 S
157 155 156 0 1 Williams, Mr. Charles Duane male 51.0 0 1 PC 17597 61.3792 C
158 156 157 1 3 Gilnagh, Miss. Katherine "Katie" female 16.0 0 0 35851 7.7333 Q
159 157 158 0 3 Corn, Mr. Harry male 30.0 0 0 SOTON/OQ 392090 8.05 S
160 158 159 0 3 Smiljanic, Mr. Mile male 0 0 315037 8.6625 S
161 159 160 0 3 Sage, Master. Thomas Henry male 8 2 CA. 2343 69.55 S
162 160 161 0 3 Cribb, Mr. John Hatfield male 44.0 0 1 371362 16.1 S
163 161 162 1 2 Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne) female 40.0 0 0 C.A. 33595 15.75 S
164 162 163 0 3 Bengtsson, Mr. John Viktor male 26.0 0 0 347068 7.775 S
165 163 164 0 3 Calic, Mr. Jovo male 17.0 0 0 315093 8.6625 S
166 164 165 0 3 Panula, Master. Eino Viljami male 1.0 4 1 3101295 39.6875 S
167 165 166 1 3 Goldsmith, Master. Frank John William "Frankie" male 9.0 0 2 363291 20.525 S
168 166 167 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female 0 1 113505 55.0 E33 S
169 167 168 0 3 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45.0 1 4 347088 27.9 S
170 168 169 0 1 Baumann, Mr. John D male 0 0 PC 17318 25.925 S
171 169 170 0 3 Ling, Mr. Lee male 28.0 0 0 1601 56.4958 S
172 170 171 0 1 Van der hoef, Mr. Wyckoff male 61.0 0 0 111240 33.5 B19 S
173 171 172 0 3 Rice, Master. Arthur male 4.0 4 1 382652 29.125 Q
174 172 173 1 3 Johnson, Miss. Eleanor Ileen female 1.0 1 1 347742 11.1333 S
175 173 174 0 3 Sivola, Mr. Antti Wilhelm male 21.0 0 0 STON/O 2. 3101280 7.925 S
176 174 175 0 1 Smith, Mr. James Clinch male 56.0 0 0 17764 30.6958 A7 C
177 175 176 0 3 Klasen, Mr. Klas Albin male 18.0 1 1 350404 7.8542 S
178 176 177 0 3 Lefebre, Master. Henry Forbes male 3 1 4133 25.4667 S
179 177 178 0 1 Isham, Miss. Ann Elizabeth female 50.0 0 0 PC 17595 28.7125 C49 C
180 178 179 0 2 Hale, Mr. Reginald male 30.0 0 0 250653 13.0 S
181 179 180 0 3 Leonard, Mr. Lionel male 36.0 0 0 LINE 0.0 S
182 180 181 0 3 Sage, Miss. Constance Gladys female 8 2 CA. 2343 69.55 S
183 181 182 0 2 Pernot, Mr. Rene male 0 0 SC/PARIS 2131 15.05 C
184 182 183 0 3 Asplund, Master. Clarence Gustaf Hugo male 9.0 4 2 347077 31.3875 S
185 183 184 1 2 Becker, Master. Richard F male 1.0 2 1 230136 39.0 F4 S
186 184 185 1 3 Kink-Heilmann, Miss. Luise Gretchen female 4.0 0 2 315153 22.025 S
187 185 186 0 1 Rood, Mr. Hugh Roscoe male 0 0 113767 50.0 A32 S
188 186 187 1 3 O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey) female 1 0 370365 15.5 Q
189 187 188 1 1 Romaine, Mr. Charles Hallace ("Mr C Rolmane") male 45.0 0 0 111428 26.55 S
190 188 189 0 3 Bourke, Mr. John male 40.0 1 1 364849 15.5 Q
191 189 190 0 3 Turcin, Mr. Stjepan male 36.0 0 0 349247 7.8958 S
192 190 191 1 2 Pinsky, Mrs. (Rosa) female 32.0 0 0 234604 13.0 S
193 191 192 0 2 Carbines, Mr. William male 19.0 0 0 28424 13.0 S
194 192 193 1 3 Andersen-Jensen, Miss. Carla Christine Nielsine female 19.0 1 0 350046 7.8542 S
195 193 194 1 2 Navratil, Master. Michel M male 3.0 1 1 230080 26.0 F2 S
196 194 195 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44.0 0 0 PC 17610 27.7208 B4 C
197 195 196 1 1 Lurette, Miss. Elise female 58.0 0 0 PC 17569 146.5208 B80 C
198 196 197 0 3 Mernagh, Mr. Robert male 0 0 368703 7.75 Q
199 197 198 0 3 Olsen, Mr. Karl Siegwart Andreas male 42.0 0 1 4579 8.4042 S
200 198 199 1 3 Madigan, Miss. Margaret "Maggie" female 0 0 370370 7.75 Q
201 199 200 0 2 Yrois, Miss. Henriette ("Mrs Harbeck") female 24.0 0 0 248747 13.0 S
202 200 201 0 3 Vande Walle, Mr. Nestor Cyriel male 28.0 0 0 345770 9.5 S
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204 202 203 0 3 Johanson, Mr. Jakob Alfred male 34.0 0 0 3101264 6.4958 S
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python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_solution.py Normal file
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#!/usr/bin/env python
# coding: utf-8
# # Lab: Titanic Survival Exploration with Decision Trees
# ## Getting Started
# In this lab, you will see how decision trees work by implementing a decision tree in sklearn.
#
# We'll start by loading the dataset and displaying some of its rows.
# In[6]:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
# from IPython.display import display # Allows the use of display() for DataFrames
# Pretty display for notebooks
# get_ipython().run_line_magic('matplotlib', 'inline')
# Set a random seed
import random
random.seed(42)
# 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())
# Recall that these are the various features present for each passenger on the ship:
# - **Survived**: Outcome of survival (0 = No; 1 = Yes)
# - **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
# - **Name**: Name of passenger
# - **Sex**: Sex of the passenger
# - **Age**: Age of the passenger (Some entries contain `NaN`)
# - **SibSp**: Number of siblings and spouses of the passenger aboard
# - **Parch**: Number of parents and children of the passenger
# - **Ticket**: Ticket number of the passenger
# - **Fare**: Fare paid by the passenger
# - **Cabin** Cabin number of the passenger (Some entries contain `NaN`)
# - **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)
#
# Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets.
# Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`.
# In[7]:
# Store the 'Survived' feature in a new variable and remove it from the dataset
outcomes = full_data['Survived']
features_raw = full_data.drop('Survived', axis = 1)
# Show the new dataset with 'Survived' removed
# display(features_raw.head())
# The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.
#
# ## Preprocessing the data
#
# Now, let's do some data preprocessing. First, we'll remove the names of the passengers, and then one-hot encode the features.
#
# **Question:** Why would it be a terrible idea to one-hot encode the data without removing the names?
# (Andw
# In[8]:
# Removing the names
features_no_names = features_raw.drop(['Name'], axis=1)
# One-hot encoding
features = pd.get_dummies(features_no_names)
# And now we'll fill in any blanks with zeroes.
# In[9]:
features = features.fillna(0.0)
# display(features.head())
# ## (TODO) Training the model
#
# Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set.
# In[15]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)
# In[17]:
# Import the classifier from sklearn
from sklearn.tree import DecisionTreeClassifier
# TODO: Define the classifier, and fit it to the data
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
# ## Testing the model
# Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set.
# In[18]:
# Making predictions
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
# Calculate the accuracy
from sklearn.metrics import accuracy_score
train_accuracy = accuracy_score(y_train, y_train_pred)
test_accuracy = accuracy_score(y_test, y_test_pred)
print('The training accuracy is', train_accuracy)
print('The test accuracy is', test_accuracy)
# # Exercise: Improving the model
#
# Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.
#
# So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:
# - `max_depth`
# - `min_samples_leaf`
# - `min_samples_split`
#
# You can use your intuition, trial and error, or even better, feel free to use Grid Search!
#
# **Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next.
# In[23]:
# TODO: Train the model
new_model = DecisionTreeClassifier(max_depth=6, min_samples_leaf=6, min_samples_split=10)
new_model.fit(X_train, y_train)
# TODO: Make predictions
new_y_train_pred = new_model.predict(X_train)
new_y_test_pred = new_model.predict(X_test)
# TODO: Calculate the accuracy
new_train_accuracy = accuracy_score(y_train, new_y_train_pred)
new_test_accuracy = accuracy_score(y_test, new_y_test_pred)
print(f'The training accuracy on the new model is {new_train_accuracy:.4f}')
print(f'The test accuracy on the new model is {new_test_accuracy:.4f}')

243
python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_survival_exploration.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lab: Titanic Survival Exploration with Decision Trees"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting Started\n",
"In this lab, you will see how decision trees work by implementing a decision tree in sklearn.\n",
"\n",
"We'll start by loading the dataset and displaying some of its rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import libraries necessary for this project\n",
"import numpy as np\n",
"import pandas as pd\n",
"from IPython.display import display # Allows the use of display() for DataFrames\n",
"\n",
"# Pretty display for notebooks\n",
"%matplotlib inline\n",
"\n",
"# Set a random seed\n",
"import random\n",
"random.seed(42)\n",
"\n",
"# Load the dataset\n",
"in_file = 'titanic_data.csv'\n",
"full_data = pd.read_csv(in_file)\n",
"\n",
"# Print the first few entries of the RMS Titanic data\n",
"display(full_data.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall that these are the various features present for each passenger on the ship:\n",
"- **Survived**: Outcome of survival (0 = No; 1 = Yes)\n",
"- **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n",
"- **Name**: Name of passenger\n",
"- **Sex**: Sex of the passenger\n",
"- **Age**: Age of the passenger (Some entries contain `NaN`)\n",
"- **SibSp**: Number of siblings and spouses of the passenger aboard\n",
"- **Parch**: Number of parents and children of the passenger aboard\n",
"- **Ticket**: Ticket number of the passenger\n",
"- **Fare**: Fare paid by the passenger\n",
"- **Cabin** Cabin number of the passenger (Some entries contain `NaN`)\n",
"- **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
"\n",
"Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets. \n",
"Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Store the 'Survived' feature in a new variable and remove it from the dataset\n",
"outcomes = full_data['Survived']\n",
"features_raw = full_data.drop('Survived', axis = 1)\n",
"\n",
"# Show the new dataset with 'Survived' removed\n",
"display(features_raw.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n",
"\n",
"## Preprocessing the data\n",
"\n",
"Now, let's do some data preprocessing. First, we'll remove the names of the passengers, and then one-hot encode the features.\n",
"\n",
"**Question:** Why would it be a terrible idea to one-hot encode the data without removing the names?\n",
"(Andw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Removing the names\n",
"features_no_names = features_raw.drop(['Name'], axis=1)\n",
"\n",
"# One-hot encoding\n",
"features = pd.get_dummies(features_no_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we'll fill in any blanks with zeroes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features = features.fillna(0.0)\n",
"display(features.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (TODO) Training the model\n",
"\n",
"Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import the classifier from sklearn\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"# TODO: Define the classifier, and fit it to the data\n",
"model = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the model\n",
"Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Making predictions\n",
"y_train_pred = model.predict(X_train)\n",
"y_test_pred = model.predict(X_test)\n",
"\n",
"# Calculate the accuracy\n",
"from sklearn.metrics import accuracy_score\n",
"train_accuracy = accuracy_score(y_train, y_train_pred)\n",
"test_accuracy = accuracy_score(y_test, y_test_pred)\n",
"print('The training accuracy is', train_accuracy)\n",
"print('The test accuracy is', test_accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise: Improving the model\n",
"\n",
"Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.\n",
"\n",
"So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:\n",
"- `max_depth`\n",
"- `min_samples_leaf`\n",
"- `min_samples_split`\n",
"\n",
"You can use your intuition, trial and error, or even better, feel free to use Grid Search!\n",
"\n",
"**Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# TODO: Train the model\n",
"\n",
"# TODO: Make predictions\n",
"\n",
"# TODO: Calculate the accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lab: Titanic Survival Exploration with Decision Trees"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting Started\n",
"In this lab, you will see how decision trees work by implementing a decision tree in sklearn.\n",
"\n",
"We'll start by loading the dataset and displaying some of its rows."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import libraries necessary for this project\n",
"import numpy as np\n",
"import pandas as pd\n",
"from IPython.display import display # Allows the use of display() for DataFrames\n",
"\n",
"# Pretty display for notebooks\n",
"%matplotlib inline\n",
"\n",
"# Set a random seed\n",
"import random\n",
"random.seed(42)\n",
"\n",
"# Load the dataset\n",
"in_file = 'titanic_data.csv'\n",
"full_data = pd.read_csv(in_file)\n",
"\n",
"# Print the first few entries of the RMS Titanic data\n",
"display(full_data.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall that these are the various features present for each passenger on the ship:\n",
"- **Survived**: Outcome of survival (0 = No; 1 = Yes)\n",
"- **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n",
"- **Name**: Name of passenger\n",
"- **Sex**: Sex of the passenger\n",
"- **Age**: Age of the passenger (Some entries contain `NaN`)\n",
"- **SibSp**: Number of siblings and spouses of the passenger aboard\n",
"- **Parch**: Number of parents and children of the passenger \n",
"- **Ticket**: Ticket number of the passenger\n",
"- **Fare**: Fare paid by the passenger\n",
"- **Cabin** Cabin number of the passenger (Some entries contain `NaN`)\n",
"- **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
"\n",
"Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets. \n",
"Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Name \\\n",
"0 1 3 Braund, Mr. Owen Harris \n",
"1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n",
"2 3 3 Heikkinen, Miss. Laina \n",
"3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n",
"4 5 3 Allen, Mr. William Henry \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n",
"1 female 38.0 1 0 PC 17599 71.2833 C85 C \n",
"2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 female 35.0 1 0 113803 53.1000 C123 S \n",
"4 male 35.0 0 0 373450 8.0500 NaN S "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Store the 'Survived' feature in a new variable and remove it from the dataset\n",
"outcomes = full_data['Survived']\n",
"features_raw = full_data.drop('Survived', axis = 1)\n",
"\n",
"# Show the new dataset with 'Survived' removed\n",
"display(features_raw.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n",
"\n",
"## Preprocessing the data\n",
"\n",
"Now, let's do some data preprocessing. First, we'll remove the names of the passengers, and then one-hot encode the features.\n",
"\n",
"**Question:** Why would it be a terrible idea to one-hot encode the data without removing the names?\n",
"(Andw"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Removing the names\n",
"features_no_names = features_raw.drop(['Name'], axis=1)\n",
"\n",
"# One-hot encoding\n",
"features = pd.get_dummies(features_no_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we'll fill in any blanks with zeroes."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Sex_female</th>\n",
" <th>Sex_male</th>\n",
" <th>Ticket_110152</th>\n",
" <th>Ticket_110413</th>\n",
" <th>...</th>\n",
" <th>Cabin_F G73</th>\n",
" <th>Cabin_F2</th>\n",
" <th>Cabin_F33</th>\n",
" <th>Cabin_F38</th>\n",
" <th>Cabin_F4</th>\n",
" <th>Cabin_G6</th>\n",
" <th>Cabin_T</th>\n",
" <th>Embarked_C</th>\n",
" <th>Embarked_Q</th>\n",
" <th>Embarked_S</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 839 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Pclass Age SibSp Parch Fare Sex_female Sex_male \\\n",
"0 1 3 22.0 1 0 7.2500 0 1 \n",
"1 2 1 38.0 1 0 71.2833 1 0 \n",
"2 3 3 26.0 0 0 7.9250 1 0 \n",
"3 4 1 35.0 1 0 53.1000 1 0 \n",
"4 5 3 35.0 0 0 8.0500 0 1 \n",
"\n",
" Ticket_110152 Ticket_110413 ... Cabin_F G73 Cabin_F2 Cabin_F33 \\\n",
"0 0 0 ... 0 0 0 \n",
"1 0 0 ... 0 0 0 \n",
"2 0 0 ... 0 0 0 \n",
"3 0 0 ... 0 0 0 \n",
"4 0 0 ... 0 0 0 \n",
"\n",
" Cabin_F38 Cabin_F4 Cabin_G6 Cabin_T Embarked_C Embarked_Q Embarked_S \n",
"0 0 0 0 0 0 0 1 \n",
"1 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 0 1 \n",
"3 0 0 0 0 0 0 1 \n",
"4 0 0 0 0 0 0 1 \n",
"\n",
"[5 rows x 839 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"features = features.fillna(0.0)\n",
"display(features.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (TODO) Training the model\n",
"\n",
"Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import the classifier from sklearn\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"\n",
"# TODO: Define the classifier, and fit it to the data\n",
"model = DecisionTreeClassifier()\n",
"model.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the model\n",
"Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The training accuracy is 1.0\n",
"The test accuracy is 0.815642458101\n"
]
}
],
"source": [
"# Making predictions\n",
"y_train_pred = model.predict(X_train)\n",
"y_test_pred = model.predict(X_test)\n",
"\n",
"# Calculate the accuracy\n",
"from sklearn.metrics import accuracy_score\n",
"train_accuracy = accuracy_score(y_train, y_train_pred)\n",
"test_accuracy = accuracy_score(y_test, y_test_pred)\n",
"print('The training accuracy is', train_accuracy)\n",
"print('The test accuracy is', test_accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise: Improving the model\n",
"\n",
"Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.\n",
"\n",
"So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:\n",
"- `max_depth`\n",
"- `min_samples_leaf`\n",
"- `min_samples_split`\n",
"\n",
"You can use your intuition, trial and error, or even better, feel free to use Grid Search!\n",
"\n",
"**Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The training accuracy on the new model is 0.8820\n",
"The test accuracy on the new model is 0.8603\n"
]
}
],
"source": [
"# TODO: Train the model\n",
"new_model = DecisionTreeClassifier(max_depth=10, min_samples_leaf=6, min_samples_split=8)\n",
"new_model.fit(X_train, y_train)\n",
"\n",
"# TODO: Make predictions\n",
"new_y_train_pred = new_model.predict(X_train)\n",
"new_y_test_pred = new_model.predict(X_test)\n",
"\n",
"# TODO: Calculate the accuracy\n",
"new_train_accuracy = accuracy_score(y_train, new_y_train_pred)\n",
"new_test_accuracy = accuracy_score(y_test, new_y_test_pred)\n",
"\n",
"print(f'The training accuracy on the new model is {new_train_accuracy:.4f}')\n",
"print(f'The test accuracy on the new model is {new_test_accuracy:.4f}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

92
python/Supervised Learning/Linear Regression/16 Quiz: Mini-Batch Gradient Descent/batch_graddesc.py Normal file
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import numpy as np
# Setting a random seed, feel free to change it and see different solutions.
np.random.seed(42)
# TODO: Fill in code in the function below to implement a gradient descent
# step for linear regression, following a squared error rule. See the docstring
# for parameters and returned variables.
def MSEStep(X, y, W, b, learn_rate=0.005):
"""
This function implements the gradient descent step for squared error as a
performance metric.
Parameters
X : array of predictor features
y : array of outcome values
W : predictor feature coefficients
b : regression function intercept
learn_rate : learning rate
Returns
W_new : predictor feature coefficients following gradient descent step
b_new : intercept following gradient descent step
"""
# compute errors
y_pred = np.matmul(X, W) + b
error = y - y_pred
# compute steps
W_new = W + learn_rate * np.matmul(error, X)
b_new = b + learn_rate * error.sum()
return W_new, b_new
return W_new, b_new
# The parts of the script below will be run when you press the "Test Run"
# button. The gradient descent step will be performed multiple times on
# the provided dataset, and the returned list of regression coefficients
# will be plotted.
def miniBatchGD(X, y, batch_size=20, learn_rate=0.005, num_iter=25):
"""
This function performs mini-batch gradient descent on a given dataset.
Parameters
X : array of predictor features
y : array of outcome values
batch_size : how many data points will be sampled for each iteration
learn_rate : learning rate
num_iter : number of batches used
Returns
regression_coef : array of slopes and intercepts generated by gradient
descent procedure
"""
n_points = X.shape[0]
W = np.zeros(X.shape[1]) # coefficients
b = 0 # intercept
# run iterations
regression_coef = [np.hstack((W, b))]
for _ in range(num_iter):
batch = np.random.choice(range(n_points), batch_size)
X_batch = X[batch, :]
y_batch = y[batch]
W, b = MSEStep(X_batch, y_batch, W, b, learn_rate)
regression_coef.append(np.hstack((W, b)))
return regression_coef
if __name__ == "__main__":
# perform gradient descent
data = np.loadtxt('data.csv', delimiter=',')
X = data[:, :-1]
y = data[:, -1]
regression_coef = miniBatchGD(X, y)
# plot the results
import matplotlib.pyplot as plt
plt.figure()
X_min = X.min()
X_max = X.max()
counter = len(regression_coef)
for W, b in regression_coef:
counter -= 1
color = [1 - 0.92 ** counter for _ in range(3)]
plt.plot([X_min, X_max], [X_min * W + b, X_max * W + b], color=color)
plt.scatter(X, y, zorder=3)
plt.show()

100
python/Supervised Learning/Linear Regression/16 Quiz: Mini-Batch Gradient Descent/data.csv Normal file
View File

@@ -0,0 +1,100 @@
-0.72407,2.23863
-2.40724,-0.00156
2.64837,3.01665
0.36092,2.31019
0.67312,2.05950
-0.45460,1.24736
2.20168,2.82497
1.15605,2.21802
0.50694,1.43644
-0.85952,1.74980
-0.59970,1.63259
1.46804,2.43461
-1.05659,1.02226
1.29177,3.11769
-0.74565,0.81194
0.15033,2.81910
-1.49627,0.53105
-0.72071,1.64845
0.32924,1.91416
-0.28053,2.11376
-1.36115,1.70969
0.74678,2.92253
0.10621,3.29827
0.03256,1.58565
-0.98290,2.30455
-1.15661,1.79169
0.09024,1.54723
-1.03816,1.06893
-0.00604,1.78802
0.16278,1.84746
-0.69869,1.58732
1.03857,1.94799
-0.11783,3.09324
-0.95409,1.86155
-0.81839,1.88817
-1.28802,1.39474
0.62822,1.71526
-2.29674,1.75695
-0.85601,1.12981
-1.75223,1.67000
-1.19662,0.66711
0.97781,3.11987
-1.17110,0.56924
0.15835,2.28231
-0.58918,1.23798
-1.79678,1.35803
-0.95727,1.75579
0.64556,1.91470
0.24625,2.33029
0.45917,3.25263
1.21036,2.07602
-0.60116,1.54254
0.26851,2.79202
0.49594,1.96178
-2.67877,0.95898
0.49402,1.96690
1.18643,3.06144
-0.17741,1.85984
0.57938,1.82967
-2.14926,0.62285
2.27700,3.63838
-1.05695,1.11807
1.68288,2.91735
-1.53513,1.99668
0.00099,1.76149
0.45520,2.31938
-0.37855,0.90172
1.35638,3.49432
0.01763,1.87838
2.21725,2.61171
-0.44442,2.06623
0.89583,3.04041
1.30499,2.42824
0.10883,0.63190
1.79466,2.95265
-0.00733,1.87546
0.79862,3.44953
-0.12353,1.53740
-1.34999,1.59958
-0.67825,1.57832
-0.17901,1.73312
0.12577,2.00244
1.11943,2.08990
-3.02296,1.09255
0.64965,1.28183
1.05994,2.32358
0.53360,1.75136
-0.73591,1.43076
-0.09569,2.81376
1.04694,2.56597
0.46511,2.36401
-0.75463,2.30161
-0.94159,1.94500
-0.09314,1.87619
-0.98641,1.46602
-0.92159,1.21538
0.76953,2.39377
0.03283,1.55730
-1.07619,0.70874
0.20174,1.76894
1 -0.72407 2.23863
2 -2.40724 -0.00156
3 2.64837 3.01665
4 0.36092 2.31019
5 0.67312 2.05950
6 -0.45460 1.24736
7 2.20168 2.82497
8 1.15605 2.21802
9 0.50694 1.43644
10 -0.85952 1.74980
11 -0.59970 1.63259
12 1.46804 2.43461
13 -1.05659 1.02226
14 1.29177 3.11769
15 -0.74565 0.81194
16 0.15033 2.81910
17 -1.49627 0.53105
18 -0.72071 1.64845
19 0.32924 1.91416
20 -0.28053 2.11376
21 -1.36115 1.70969
22 0.74678 2.92253
23 0.10621 3.29827
24 0.03256 1.58565
25 -0.98290 2.30455
26 -1.15661 1.79169
27 0.09024 1.54723
28 -1.03816 1.06893
29 -0.00604 1.78802
30 0.16278 1.84746
31 -0.69869 1.58732
32 1.03857 1.94799
33 -0.11783 3.09324
34 -0.95409 1.86155
35 -0.81839 1.88817
36 -1.28802 1.39474
37 0.62822 1.71526
38 -2.29674 1.75695
39 -0.85601 1.12981
40 -1.75223 1.67000
41 -1.19662 0.66711
42 0.97781 3.11987
43 -1.17110 0.56924
44 0.15835 2.28231
45 -0.58918 1.23798
46 -1.79678 1.35803
47 -0.95727 1.75579
48 0.64556 1.91470
49 0.24625 2.33029
50 0.45917 3.25263
51 1.21036 2.07602
52 -0.60116 1.54254
53 0.26851 2.79202
54 0.49594 1.96178
55 -2.67877 0.95898
56 0.49402 1.96690
57 1.18643 3.06144
58 -0.17741 1.85984
59 0.57938 1.82967
60 -2.14926 0.62285
61 2.27700 3.63838
62 -1.05695 1.11807
63 1.68288 2.91735
64 -1.53513 1.99668
65 0.00099 1.76149
66 0.45520 2.31938
67 -0.37855 0.90172
68 1.35638 3.49432
69 0.01763 1.87838
70 2.21725 2.61171
71 -0.44442 2.06623
72 0.89583 3.04041
73 1.30499 2.42824
74 0.10883 0.63190
75 1.79466 2.95265
76 -0.00733 1.87546
77 0.79862 3.44953
78 -0.12353 1.53740
79 -1.34999 1.59958
80 -0.67825 1.57832
81 -0.17901 1.73312
82 0.12577 2.00244
83 1.11943 2.08990
84 -3.02296 1.09255
85 0.64965 1.28183
86 1.05994 2.32358
87 0.53360 1.75136
88 -0.73591 1.43076
89 -0.09569 2.81376
90 1.04694 2.56597
91 0.46511 2.36401
92 -0.75463 2.30161
93 -0.94159 1.94500
94 -0.09314 1.87619
95 -0.98641 1.46602
96 -0.92159 1.21538
97 0.76953 2.39377
98 0.03283 1.55730
99 -1.07619 0.70874
100 0.20174 1.76894

25
python/Supervised Learning/Linear Regression/16 Quiz: Mini-Batch Gradient Descent/solution.py Normal file
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def MSEStep(X, y, W, b, learn_rate = 0.001):
"""
This function implements the gradient descent step for squared error as a
performance metric.
Parameters
X : array of predictor features
y : array of outcome values
W : predictor feature coefficients
b : regression function intercept
learn_rate : learning rate
Returns
W_new : predictor feature coefficients following gradient descent step
b_new : intercept following gradient descent step
"""
# compute errors
y_pred = np.matmul(X, W) + b
error = y - y_pred
# compute steps
W_new = W + learn_rate * np.matmul(error, X)
b_new = b + learn_rate * error.sum()
return W_new, b_new

164
python/Supervised Learning/Linear Regression/18. Linear Regression in scikit-learn/data.csv Normal file
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@@ -0,0 +1,164 @@
Country,Life expectancy,BMI
Afghanistan,52.8,20.62058
Albania,76.8,26.44657
Algeria,75.5,24.59620
Andorra,84.6,27.63048
Angola,56.7,22.25083
Armenia,72.3,25.355420000000002
Australia,81.6,27.56373
Austria,80.4,26.467409999999997
Azerbaijan,69.2,25.65117
Bahamas,72.2,27.24594
Bangladesh,68.3,20.39742
Barbados,75.3,26.38439
Belarus,70.0,26.16443
Belgium,79.6,26.75915
Belize,70.7,27.02255
Benin,59.7,22.41835
Bhutan,70.7,22.82180
Bolivia,71.2,24.43335
Bosnia and Herzegovina,77.5,26.61163
Botswana,53.2,22.12984
Brazil,73.2,25.78623
Bulgaria,73.2,26.54286
Burkina Faso,58.0,21.27157
Burundi,59.1,21.50291
Cambodia,66.1,20.80496
Cameroon,56.6,23.68173
Canada,80.8,27.45210
Cape Verde,70.4,23.51522
Chad,54.3,21.48569
Chile,78.5,27.01542
China,73.4,22.92176
Colombia,76.2,24.94041
Comoros,67.1,22.06131
"Congo, Dem. Rep.",57.5,19.86692
"Congo, Rep.",58.8,21.87134
Costa Rica,79.8,26.47897
Cote d'Ivoire,55.4,22.56469
Croatia,76.2,26.59629
Cuba,77.6,25.06867
Cyprus,80.0,27.41899
Denmark,78.9,26.13287
Djibouti,61.8,23.38403
Ecuador,74.7,25.58841
Egypt,70.2,26.73243
El Salvador,73.7,26.36751
Eritrea,60.1,20.88509
Estonia,74.2,26.26446
Ethiopia,60.0,20.24700
Fiji,64.9,26.53078
Finland,79.6,26.73339
France,81.1,25.85329
French Polynesia,75.11,30.86752
Gabon,61.7,24.07620
Gambia,65.7,21.65029
Georgia,71.8,25.54942
Germany,80.0,27.16509
Ghana,62.0,22.84247
Greece,80.2,26.33786
Greenland,70.3,26.01359
Grenada,70.8,25.17988
Guatemala,71.2,25.29947
Guinea,57.1,22.52449
Guinea-Bissau,53.6,21.64338
Guyana,65.0,23.68465
Haiti,61.0,23.66302
Honduras,71.8,25.10872
Hungary,73.9,27.11568
Iceland,82.4,27.20687
India,64.7,20.95956
Indonesia,69.4,21.85576
Iran,73.1,25.31003
Iraq,66.6,26.71017
Ireland,80.1,27.65325
Israel,80.6,27.13151
Jamaica,75.1,24.00421
Japan,82.5,23.50004
Jordan,76.9,27.47362
Kazakhstan,67.1,26.29078
Kenya,60.8,21.59258
Kuwait,77.3,29.17211
Latvia,72.4,26.45693
Lesotho,44.5,21.90157
Liberia,59.9,21.89537
Libya,75.6,26.54164
Lithuania,72.1,26.86102
Luxembourg,81.0,27.43404
"Macedonia, FYR",74.5,26.34473
Madagascar,62.2,21.40347
Malawi,52.4,22.03468
Malaysia,74.5,24.73069
Maldives,78.5,23.21991
Mali,58.5,21.78881
Malta,80.7,27.68361
Marshall Islands,65.3,29.37337
Mauritania,67.9,22.62295
Mauritius,72.9,25.15669
Mexico,75.4,27.42468
Moldova,70.4,24.23690
Mongolia,64.8,24.88385
Montenegro,76.0,26.55412
Morocco,73.3,25.63182
Mozambique,54.0,21.93536
Myanmar,59.4,21.44932
Namibia,59.1,22.65008
Nepal,68.4,20.76344
Netherlands,80.3,26.01541
Nicaragua,77.0,25.77291
Niger,58.0,21.21958
Nigeria,59.2,23.03322
Norway,80.8,26.93424
Oman,76.2,26.24109
Pakistan,64.1,22.29914
Panama,77.3,26.26959
Papua New Guinea,58.6,25.01506
Paraguay,74.0,25.54223
Peru,78.2,24.77041
Philippines,69.8,22.87263
Poland,75.4,26.67380
Portugal,79.4,26.68445
Qatar,77.9,28.13138
Romania,73.2,25.41069
Russia,67.9,26.01131
Rwanda,64.1,22.55453
Samoa,72.3,30.42475
Sao Tome and Principe,66.0,23.51233
Senegal,63.5,21.92743
Serbia,74.3,26.51495
Sierra Leone,53.6,22.53139
Singapore,80.6,23.83996
Slovak Republic,74.9,26.92717
Slovenia,78.7,27.43983
Somalia,52.6,21.96917
South Africa,53.4,26.85538
Spain,81.1,27.49975
Sri Lanka,74.0,21.96671
Sudan,65.5,22.40484
Suriname,70.2,25.49887
Swaziland,45.1,23.16969
Sweden,81.1,26.37629
Switzerland,82.0,26.20195
Syria,76.1,26.91969
Tajikistan,69.6,23.77966
Tanzania,60.4,22.47792
Thailand,73.9,23.00803
Timor-Leste,69.9,20.59082
Togo,57.5,21.87875
Tonga,70.3,30.99563
Trinidad and Tobago,71.7,26.39669
Tunisia,76.8,25.15699
Turkey,77.8,26.70371
Turkmenistan,67.2,25.24796
Uganda,56.0,22.35833
Ukraine,67.8,25.42379
United Arab Emirates,75.6,28.05359
United Kingdom,79.7,27.39249
United States,78.3,28.45698
Uruguay,76.0,26.39123
Uzbekistan,69.6,25.32054
Vanuatu,63.4,26.78926
West Bank and Gaza,74.1,26.57750
Vietnam,74.1,20.91630
Zambia,51.1,20.68321
Zimbabwe,47.3,22.02660
1 Country Life expectancy BMI
2 Afghanistan 52.8 20.62058
3 Albania 76.8 26.44657
4 Algeria 75.5 24.59620
5 Andorra 84.6 27.63048
6 Angola 56.7 22.25083
7 Armenia 72.3 25.355420000000002
8 Australia 81.6 27.56373
9 Austria 80.4 26.467409999999997
10 Azerbaijan 69.2 25.65117
11 Bahamas 72.2 27.24594
12 Bangladesh 68.3 20.39742
13 Barbados 75.3 26.38439
14 Belarus 70.0 26.16443
15 Belgium 79.6 26.75915
16 Belize 70.7 27.02255
17 Benin 59.7 22.41835
18 Bhutan 70.7 22.82180
19 Bolivia 71.2 24.43335
20 Bosnia and Herzegovina 77.5 26.61163
21 Botswana 53.2 22.12984
22 Brazil 73.2 25.78623
23 Bulgaria 73.2 26.54286
24 Burkina Faso 58.0 21.27157
25 Burundi 59.1 21.50291
26 Cambodia 66.1 20.80496
27 Cameroon 56.6 23.68173
28 Canada 80.8 27.45210
29 Cape Verde 70.4 23.51522
30 Chad 54.3 21.48569
31 Chile 78.5 27.01542
32 China 73.4 22.92176
33 Colombia 76.2 24.94041
34 Comoros 67.1 22.06131
35 Congo, Dem. Rep. 57.5 19.86692
36 Congo, Rep. 58.8 21.87134
37 Costa Rica 79.8 26.47897
38 Cote d'Ivoire 55.4 22.56469
39 Croatia 76.2 26.59629
40 Cuba 77.6 25.06867
41 Cyprus 80.0 27.41899
42 Denmark 78.9 26.13287
43 Djibouti 61.8 23.38403
44 Ecuador 74.7 25.58841
45 Egypt 70.2 26.73243
46 El Salvador 73.7 26.36751
47 Eritrea 60.1 20.88509
48 Estonia 74.2 26.26446
49 Ethiopia 60.0 20.24700
50 Fiji 64.9 26.53078
51 Finland 79.6 26.73339
52 France 81.1 25.85329
53 French Polynesia 75.11 30.86752
54 Gabon 61.7 24.07620
55 Gambia 65.7 21.65029
56 Georgia 71.8 25.54942
57 Germany 80.0 27.16509
58 Ghana 62.0 22.84247
59 Greece 80.2 26.33786
60 Greenland 70.3 26.01359
61 Grenada 70.8 25.17988
62 Guatemala 71.2 25.29947
63 Guinea 57.1 22.52449
64 Guinea-Bissau 53.6 21.64338
65 Guyana 65.0 23.68465
66 Haiti 61.0 23.66302
67 Honduras 71.8 25.10872
68 Hungary 73.9 27.11568
69 Iceland 82.4 27.20687
70 India 64.7 20.95956
71 Indonesia 69.4 21.85576
72 Iran 73.1 25.31003
73 Iraq 66.6 26.71017
74 Ireland 80.1 27.65325
75 Israel 80.6 27.13151
76 Jamaica 75.1 24.00421
77 Japan 82.5 23.50004
78 Jordan 76.9 27.47362
79 Kazakhstan 67.1 26.29078
80 Kenya 60.8 21.59258
81 Kuwait 77.3 29.17211
82 Latvia 72.4 26.45693
83 Lesotho 44.5 21.90157
84 Liberia 59.9 21.89537
85 Libya 75.6 26.54164
86 Lithuania 72.1 26.86102
87 Luxembourg 81.0 27.43404
88 Macedonia, FYR 74.5 26.34473
89 Madagascar 62.2 21.40347
90 Malawi 52.4 22.03468
91 Malaysia 74.5 24.73069
92 Maldives 78.5 23.21991
93 Mali 58.5 21.78881
94 Malta 80.7 27.68361
95 Marshall Islands 65.3 29.37337
96 Mauritania 67.9 22.62295
97 Mauritius 72.9 25.15669
98 Mexico 75.4 27.42468
99 Moldova 70.4 24.23690
100 Mongolia 64.8 24.88385
101 Montenegro 76.0 26.55412
102 Morocco 73.3 25.63182
103 Mozambique 54.0 21.93536
104 Myanmar 59.4 21.44932
105 Namibia 59.1 22.65008
106 Nepal 68.4 20.76344
107 Netherlands 80.3 26.01541
108 Nicaragua 77.0 25.77291
109 Niger 58.0 21.21958
110 Nigeria 59.2 23.03322
111 Norway 80.8 26.93424
112 Oman 76.2 26.24109
113 Pakistan 64.1 22.29914
114 Panama 77.3 26.26959
115 Papua New Guinea 58.6 25.01506
116 Paraguay 74.0 25.54223
117 Peru 78.2 24.77041
118 Philippines 69.8 22.87263
119 Poland 75.4 26.67380
120 Portugal 79.4 26.68445
121 Qatar 77.9 28.13138
122 Romania 73.2 25.41069
123 Russia 67.9 26.01131
124 Rwanda 64.1 22.55453
125 Samoa 72.3 30.42475
126 Sao Tome and Principe 66.0 23.51233
127 Senegal 63.5 21.92743
128 Serbia 74.3 26.51495
129 Sierra Leone 53.6 22.53139
130 Singapore 80.6 23.83996
131 Slovak Republic 74.9 26.92717
132 Slovenia 78.7 27.43983
133 Somalia 52.6 21.96917
134 South Africa 53.4 26.85538
135 Spain 81.1 27.49975
136 Sri Lanka 74.0 21.96671
137 Sudan 65.5 22.40484
138 Suriname 70.2 25.49887
139 Swaziland 45.1 23.16969
140 Sweden 81.1 26.37629
141 Switzerland 82.0 26.20195
142 Syria 76.1 26.91969
143 Tajikistan 69.6 23.77966
144 Tanzania 60.4 22.47792
145 Thailand 73.9 23.00803
146 Timor-Leste 69.9 20.59082
147 Togo 57.5 21.87875
148 Tonga 70.3 30.99563
149 Trinidad and Tobago 71.7 26.39669
150 Tunisia 76.8 25.15699
151 Turkey 77.8 26.70371
152 Turkmenistan 67.2 25.24796
153 Uganda 56.0 22.35833
154 Ukraine 67.8 25.42379
155 United Arab Emirates 75.6 28.05359
156 United Kingdom 79.7 27.39249
157 United States 78.3 28.45698
158 Uruguay 76.0 26.39123
159 Uzbekistan 69.6 25.32054
160 Vanuatu 63.4 26.78926
161 West Bank and Gaza 74.1 26.57750
162 Vietnam 74.1 20.91630
163 Zambia 51.1 20.68321
164 Zimbabwe 47.3 22.02660

19
python/Supervised Learning/Linear Regression/18. Linear Regression in scikit-learn/regression.py Normal file
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@@ -0,0 +1,19 @@
from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('data.csv')
bmi_life_data = df
bmi_life_model = LinearRegression()
# print(bmi_life_data[['Life expectancy']], bmi_life_data[['BMI']])
bmi_life_model.fit(bmi_life_data[['BMI']],
bmi_life_data[['Life expectancy']])
laos_life_exp = bmi_life_model.predict([[21.07931]])
print(laos_life_exp)

18
python/Supervised Learning/Linear Regression/20. Multiple Linear Regression/quiz.py Normal file
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from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
boston_data = load_boston()
x = boston_data['data']
y = boston_data['target']
model = LinearRegression()
sample_house = [[2.29690000e-01, 0.00000000e+00, 1.05900000e+01,
0.00000000e+00, 4.89000000e-01, 6.32600000e+00, 5.25000000e+01,
4.35490000e+00, 4.00000000e+00, 2.77000000e+02, 1.86000000e+01,
3.94870000e+02, 1.09700000e+01]]
model.fit(x, y)
prediction = model.predict(sample_house)
print(prediction)

21
python/Supervised Learning/Linear Regression/25. Polynomial Regression/data.csv Normal file
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@@ -0,0 +1,21 @@
Var_X,Var_Y
-0.33532,6.66854
0.02160,3.86398
-1.19438,5.16161
-0.65046,8.43823
-0.28001,5.57201
1.93258,-11.13270
1.22620,-5.31226
0.74727,-4.63725
3.32853,3.80650
2.87457,-6.06084
-1.48662,7.22328
0.37629,2.38887
1.43918,-7.13415
0.24183,2.00412
-2.79140,4.29794
1.08176,-5.86553
2.81555,-5.20711
0.54924,-3.52863
2.36449,-10.16202
-1.01925,5.31123
1 Var_X Var_Y
2 -0.33532 6.66854
3 0.02160 3.86398
4 -1.19438 5.16161
5 -0.65046 8.43823
6 -0.28001 5.57201
7 1.93258 -11.13270
8 1.22620 -5.31226
9 0.74727 -4.63725
10 3.32853 3.80650
11 2.87457 -6.06084
12 -1.48662 7.22328
13 0.37629 2.38887
14 1.43918 -7.13415
15 0.24183 2.00412
16 -2.79140 4.29794
17 1.08176 -5.86553
18 2.81555 -5.20711
19 0.54924 -3.52863
20 2.36449 -10.16202
21 -1.01925 5.31123

23
python/Supervised Learning/Linear Regression/25. Polynomial Regression/regression.py Normal file
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import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
df = pd.read_csv('data.csv')
# print(df)
X = df[['Var_X']]
y = df[['Var_Y']]
poly_feat = PolynomialFeatures(degree=2)
X_poly = poly_feat.fit_transform(X)
poly_model = LinearRegression(fit_intercept=False).fit(X_poly, y)
print(poly_model)
# sns.lineplot(x='Var_X', y='Var_Y', data=df)
# plt.show()

22
python/Supervised Learning/Linear Regression/25. Polynomial Regression/solution.py Normal file
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@@ -0,0 +1,22 @@
# TODO: Add import statements
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
# Assign the data to predictor and outcome variables
# TODO: Load the data
train_data = pd.read_csv('data.csv')
X = train_data['Var_X'].values.reshape(-1, 1)
y = train_data['Var_Y'].values
# Create polynomial features
# TODO: Create a PolynomialFeatures object, then fit and transform the
# predictor feature
poly_feat = PolynomialFeatures(degree = 4)
X_poly = poly_feat.fit_transform(X)
# Make and fit the polynomial regression model
# TODO: Create a LinearRegression object and fit it to the polynomial predictor
# features
poly_model = LinearRegression(fit_intercept = False).fit(X_poly, y)

100
python/Supervised Learning/Linear Regression/27. Regularization/data.csv Normal file
View File

@@ -0,0 +1,100 @@
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15
python/Supervised Learning/Linear Regression/27. Regularization/regularization.py Normal file
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@@ -0,0 +1,15 @@
import pandas as pd
import numpy as np
from sklearn.linear_model import Lasso
train_data = pd.read_csv('data.csv', header=None)
X = train_data.iloc[:, :-1]
y = train_data.iloc[:, -1:]
lasso_reg = Lasso()
lasso_reg.fit(X, y)
reg_coef = lasso_reg.coef_
print(reg_coef)

100
python/Supervised Learning/Linear Regression/28. Feature Scaling/data.csv Normal file
View File

@@ -0,0 +1,100 @@
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80 1.55873 0.25462 2.37448 16.04402 -0.06938 -0.36479 -0.67043
81 -0.66650 -2.27045 6.40325 7.64815 1.58676 -0.11790 -3.12393
82 4.58728 -2.90732 -0.05803 2.27259 2.29507 0.13907 -16.76419
83 -11.73607 -2.26595 1.63461 6.21257 0.73723 0.03777 -7.00464
84 -2.03125 1.83364 1.57590 5.52329 -3.64759 0.06059 23.96407
85 4.63339 1.37232 -0.62675 13.46151 3.69937 -0.09897 -13.66325
86 -0.93955 -1.39664 -4.69027 -5.30208 -2.70883 0.07360 -0.26176
87 3.19531 -1.43186 3.82859 -9.83963 -2.83611 0.09403 14.30309
88 -0.66991 -0.33925 -0.26224 -6.71810 0.52439 0.00654 -2.45750
89 3.32705 -0.20431 -0.61940 -5.82014 -3.30832 -0.13399 9.94820
90 -3.01400 -1.40133 7.13418 -15.85676 3.92442 0.29137 -0.19544
91 10.75129 -0.08744 4.35843 -9.89202 -0.71794 0.12349 12.68742
92 4.74271 -1.32895 -2.73218 9.15129 0.93902 -0.17934 -15.58698
93 3.96678 -1.93074 -1.98368 -12.52082 7.35129 -0.30941 -40.20406
94 2.98664 1.85034 2.54075 -2.98750 0.37193 0.16048 9.08819
95 -6.73878 -1.08637 -1.55835 -3.93097 -3.02271 0.11860 6.24185
96 -4.58240 -1.27825 7.55098 8.83930 -3.80318 0.04386 26.14768
97 -10.00364 2.66002 -4.26776 -3.73792 -0.72349 -0.24617 0.76214
98 -4.32624 -2.30314 -8.16044 4.46366 -3.33569 -0.01655 -10.05262
99 -1.90167 -0.15858 -10.43466 4.89762 -0.64606 -0.14519 -19.63970
100 2.43213 2.41613 2.49949 -8.03891 -1.64164 -0.63444 12.76193

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python/Supervised Learning/Linear Regression/28. Feature Scaling/feature_scaling.py Normal file
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import pandas as pd
import numpy as np
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
train_data = pd.read_csv('data.csv', header=None)
X = train_data.iloc[:, :-1]
y = train_data.iloc[:, -1]
# Create the standardization scaling object
scaler = StandardScaler()
# Scale and fit the standardization paramaeters
X_scaled = scaler.fit_transform(X)
# Create the LR model with Lasso regularization
lasso_reg = Lasso()
# Fit the model
lasso_reg.fit(X_scaled, y)
# Get the regression coeficients
reg_coef = lasso_reg.coef_
print(reg_coef)

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#!/usr/bin/env python
# coding: utf-8
# ## Our Mission ##
#
# Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'.
#
# In this mission we will be using the Naive Bayes algorithm to create a model that can classify SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like. Usually they have words like 'free', 'win', 'winner', 'cash', 'prize' and the like in them as these texts are designed to catch your eye and in some sense tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us!
#
# Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.
#
# # Overview
#
# This project has been broken down in to the following steps:
#
# - Step 0: Introduction to the Naive Bayes Theorem
# - Step 1.1: Understanding our dataset
# - Step 1.2: Data Preprocessing
# - Step 2.1: Bag of Words(BoW)
# - Step 2.2: Implementing BoW from scratch
# - Step 2.3: Implementing Bag of Words in scikit-learn
# - Step 3.1: Training and testing sets
# - Step 3.2: Applying Bag of Words processing to our dataset.
# - Step 4.1: Bayes Theorem implementation from scratch
# - Step 4.2: Naive Bayes implementation from scratch
# - Step 5: Naive Bayes implementation using scikit-learn
# - Step 6: Evaluating our model
# - Step 7: Conclusion
#
# ### Step 0: Introduction to the Naive Bayes Theorem ###
#
# Bayes Theorem is one of the earliest probabilistic inference algorithms. It was developed by Reverend Bayes (which he used to try and infer the existence of God no less), and still performs extremely well for certain use cases.
#
# It's best to understand this theorem using an example. Let's say you are a member of the Secret Service and you have been deployed to protect the Democratic presidential nominee during one of his/her campaign speeches. Being a public event that is open to all, your job is not easy and you have to be on the constant lookout for threats. So one place to start is to put a certain threat-factor for each person. So based on the features of an individual, like the age, sex, and other smaller factors like whether the person is carrying a bag, looks nervous, etc., you can make a judgment call as to whether that person is a viable threat.
#
# If an individual ticks all the boxes up to a level where it crosses a threshold of doubt in your mind, you can take action and remove that person from the vicinity. Bayes Theorem works in the same way, as we are computing the probability of an event (a person being a threat) based on the probabilities of certain related events (age, sex, presence of bag or not, nervousness of the person, etc.).
#
# One thing to consider is the independence of these features amongst each other. For example if a child looks nervous at the event then the likelihood of that person being a threat is not as much as say if it was a grown man who was nervous. To break this down a bit further, here there are two features we are considering, age AND nervousness. Say we look at these features individually, we could design a model that flags ALL persons that are nervous as potential threats. However, it is likely that we will have a lot of false positives as there is a strong chance that minors present at the event will be nervous. Hence by considering the age of a person along with the 'nervousness' feature we would definitely get a more accurate result as to who are potential threats and who aren't.
#
# This is the 'Naive' bit of the theorem where it considers each feature to be independent of each other which may not always be the case and hence that can affect the final judgement.
#
# In short, Bayes Theorem calculates the probability of a certain event happening (in our case, a message being spam) based on the joint probabilistic distributions of certain other events (in our case, the appearance of certain words in a message). We will dive into the workings of Bayes Theorem later in the mission, but first, let us understand the data we are going to work with.
# ### Step 1.1: Understanding our dataset ###
#
#
# We will be using a dataset originally compiled and posted on the UCI Machine Learning repository which has a very good collection of datasets for experimental research purposes. If you're interested, you can review the [abstract](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) and the original [compressed data file](https://archive.ics.uci.edu/ml/machine-learning-databases/00228/) on the UCI site. For this exercise, however, we've gone ahead and downloaded the data for you.
#
#
# **Here's a preview of the data:**
#
# <img src="images/dqnb.png" height="1242" width="1242">
#
# The columns in the data set are currently not named and as you can see, there are 2 columns.
#
# The first column takes two values, 'ham' which signifies that the message is not spam, and 'spam' which signifies that the message is spam.
#
# The second column is the text content of the SMS message that is being classified.
# >**Instructions:**
# * Import the dataset into a pandas dataframe using the **read_table** method. The file has already been downloaded, and you can access it using the filepath 'smsspamcollection/SMSSpamCollection'. Because this is a tab separated dataset we will be using '\\t' as the value for the 'sep' argument which specifies this format.
# * Also, rename the column names by specifying a list ['label', 'sms_message'] to the 'names' argument of read_table().
# * Print the first five values of the dataframe with the new column names.
# In[12]:
# '!' allows you to run bash commands from jupyter notebook.
print("List all the files in the current directory\n")
# get_ipython().system('ls')
# The required data table could be found under smsspamcollection/SMSSpamCollection
print("\n List all the files inside the smsspamcollection directory\n")
# get_ipython().system('ls smsspamcollection')
# >**Instructions:**
# * Convert the values in the 'label' column to numerical values using map method as follows:
# {'ham':0, 'spam':1} This maps the 'ham' value to 0 and the 'spam' value to 1.
# * Also, to get an idea of the size of the dataset we are dealing with, print out number of rows and columns using
# 'shape'.
# In[23]:
import pandas as pd
# Dataset available using filepath 'smsspamcollection/SMSSpamCollection
headers = ['label', 'sms_message']
df = pd.read_csv('smsspamcollection_SMSSpamCollection', sep='\t', names=headers)
# Output printing out first 5 rows
# df.head()
# ### Step 1.2: Data Preprocessing ###
#
# Now that we have a basic understanding of what our dataset looks like, let's convert our labels to binary variables, 0 to represent 'ham'(i.e. not spam) and 1 to represent 'spam' for ease of computation.
#
# You might be wondering why do we need to do this step? The answer to this lies in how scikit-learn handles inputs. Scikit-learn only deals with numerical values and hence if we were to leave our label values as strings, scikit-learn would do the conversion internally(more specifically, the string labels will be cast to unknown float values).
#
# Our model would still be able to make predictions if we left our labels as strings but we could have issues later when calculating performance metrics, for example when calculating our precision and recall scores. Hence, to avoid unexpected 'gotchas' later, it is good practice to have our categorical values be fed into our model as integers.
# In[24]:
'''
Solution
'''
print(df)
df['label'] = df.label.map(lambda x: dict(ham=0, spam=1)[x], df.label.values.tolist())
df.head()
print(f'Our data has {df.shape[0]} rows and {df.shape[1]} cols.')
# In[27]:
df.head()
# ### Step 2.1: Bag of Words ###
#
# What we have here in our data set is a large collection of text data (5,572 rows of data). Most ML algorithms rely on numerical data to be fed into them as input, and email/sms messages are usually text heavy.
#
# Here we'd like to introduce the Bag of Words (BoW) concept which is a term used to specify the problems that have a 'bag of words' or a collection of text data that needs to be worked with. The basic idea of BoW is to take a piece of text and count the frequency of the words in that text. It is important to note that the BoW concept treats each word individually and the order in which the words occur does not matter.
#
# Using a process which we will go through now, we can convert a collection of documents to a matrix, with each document being a row and each word (token) being the column, and the corresponding (row, column) values being the frequency of occurrence of each word or token in that document.
#
# For example:
#
# Let's say we have 4 documents, which are text messages
# in our case, as follows:
#
# `['Hello, how are you!',
# 'Win money, win from home.',
# 'Call me now',
# 'Hello, Call you tomorrow?']`
#
# Our objective here is to convert this set of texts to a frequency distribution matrix, as follows:
#
# <img src="images/countvectorizer.png" height="542" width="542">
#
# Here as we can see, the documents are numbered in the rows, and each word is a column name, with the corresponding value being the frequency of that word in the document.
#
# Let's break this down and see how we can do this conversion using a small set of documents.
#
# To handle this, we will be using sklearn's
# [count vectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer) method which does the following:
#
# * It tokenizes the string (separates the string into individual words) and gives an integer ID to each token.
# * It counts the occurrence of each of those tokens.
#
# **Please Note:**
#
# * The CountVectorizer method automatically converts all tokenized words to their lower case form so that it does not treat words like 'He' and 'he' differently. It does this using the `lowercase` parameter which is by default set to `True`.
#
# * It also ignores all punctuation so that words followed by a punctuation mark (for example: 'hello!') are not treated differently than the same words not prefixed or suffixed by a punctuation mark (for example: 'hello'). It does this using the `token_pattern` parameter which has a default regular expression which selects tokens of 2 or more alphanumeric characters.
#
# * The third parameter to take note of is the `stop_words` parameter. Stop words refer to the most commonly used words in a language. They include words like 'am', 'an', 'and', 'the' etc. By setting this parameter value to `english`, CountVectorizer will automatically ignore all words(from our input text) that are found in the built in list of english stop words in scikit-learn. This is extremely helpful as stop words can skew our calculations when we are trying to find certain key words that are indicative of spam.
#
# We will dive into the application of each of these into our model in a later step, but for now it is important to be aware of such preprocessing techniques available to us when dealing with textual data.
# ### Step 2.2: Implementing Bag of Words from scratch ###
#
# Before we dive into scikit-learn's Bag of Words (BoW) library to do the dirty work for us, let's implement it ourselves first so that we can understand what's happening behind the scenes.
#
# **Step 1: Convert all strings to their lower case form.**
#
# Let's say we have a document set:
#
# ```
# documents = ['Hello, how are you!',
# 'Win money, win from home.',
# 'Call me now.',
# 'Hello, Call hello you tomorrow?']
# ```
# >>**Instructions:**
# * Convert all the strings in the documents set to their lower case. Save them into a list called 'lower_case_documents'. You can convert strings to their lower case in python by using the lower() method.
#
# In[48]:
'''
Solution:
'''
documents = ['Hello, how are you!',
'Win money, win from home.',
'Call me now.',
'Hello, Call hello you tomorrow?']
lower_case_documents = []
for i in documents:
lower_case_documents.append(i.lower())
print(lower_case_documents)
# **Step 2: Removing all punctuation**
#
# >>**Instructions:**
# Remove all punctuation from the strings in the document set. Save the strings into a list called
# 'sans_punctuation_documents'.
# In[73]:
'''
Solution:
'''
sans_punctuation_documents = []
replace_chars = {',': '', '!': '', '.': '', '?': ''}
import string
# sans_punctuation_documents = [s.replace(',', '') for s in lower_case_documents]
for item in lower_case_documents:
for i, j in replace_chars.items():
item = item.replace(i, j)
sans_punctuation_documents.append(item)
print(sans_punctuation_documents)
# **Step 3: Tokenization**
#
# Tokenizing a sentence in a document set means splitting up the sentence into individual words using a delimiter. The delimiter specifies what character we will use to identify the beginning and end of a word. Most commonly, we use a single space as the delimiter character for identifying words, and this is true in our documents in this case also.
# >>**Instructions:**
# Tokenize the strings stored in 'sans_punctuation_documents' using the split() method. Store the final document set
# in a list called 'preprocessed_documents'.
#
# In[75]:
'''
Solution:
'''
preprocessed_documents = []
for i in sans_punctuation_documents:
preprocessed_documents.append(i.split())
print(preprocessed_documents)
# **Step 4: Count frequencies**
#
# Now that we have our document set in the required format, we can proceed to counting the occurrence of each word in each document of the document set. We will use the `Counter` method from the Python `collections` library for this purpose.
#
# `Counter` counts the occurrence of each item in the list and returns a dictionary with the key as the item being counted and the corresponding value being the count of that item in the list.
# >>**Instructions:**
# Using the Counter() method and preprocessed_documents as the input, create a dictionary with the keys being each word in each document and the corresponding values being the frequency of occurrence of that word. Save each Counter dictionary as an item in a list called 'frequency_list'.
#
# In[78]:
'''
Solution
'''
frequency_list = []
import pprint
from collections import Counter
for i in preprocessed_documents:
frequency_list.append(Counter(i))
pprint.pprint(frequency_list)
# Congratulations! You have implemented the Bag of Words process from scratch! As we can see in our previous output, we have a frequency distribution dictionary which gives a clear view of the text that we are dealing with.
#
# We should now have a solid understanding of what is happening behind the scenes in the `sklearn.feature_extraction.text.CountVectorizer` method of scikit-learn.
#
# We will now implement `sklearn.feature_extraction.text.CountVectorizer` method in the next step.
# ### Step 2.3: Implementing Bag of Words in scikit-learn ###
#
# Now that we have implemented the BoW concept from scratch, let's go ahead and use scikit-learn to do this process in a clean and succinct way. We will use the same document set as we used in the previous step.
# In[ ]:
'''
Here we will look to create a frequency matrix on a smaller document set to make sure we understand how the
document-term matrix generation happens. We have created a sample document set 'documents'.
'''
documents = ['Hello, how are you!',
'Win money, win from home.',
'Call me now.',
'Hello, Call hello you tomorrow?']
# >>**Instructions:**
# Import the sklearn.feature_extraction.text.CountVectorizer method and create an instance of it called 'count_vector'.
# In[82]:
'''
Solution
'''
from sklearn.feature_extraction.text import CountVectorizer
count_vector = CountVectorizer()
# **Data preprocessing with CountVectorizer()**
#
# In Step 2.2, we implemented a version of the CountVectorizer() method from scratch that entailed cleaning our data first. This cleaning involved converting all of our data to lower case and removing all punctuation marks. CountVectorizer() has certain parameters which take care of these steps for us. They are:
#
# * `lowercase = True`
#
# The `lowercase` parameter has a default value of `True` which converts all of our text to its lower case form.
#
#
# * `token_pattern = (?u)\\b\\w\\w+\\b`
#
# The `token_pattern` parameter has a default regular expression value of `(?u)\\b\\w\\w+\\b` which ignores all punctuation marks and treats them as delimiters, while accepting alphanumeric strings of length greater than or equal to 2, as individual tokens or words.
#
#
# * `stop_words`
#
# The `stop_words` parameter, if set to `english` will remove all words from our document set that match a list of English stop words defined in scikit-learn. Considering the small size of our dataset and the fact that we are dealing with SMS messages and not larger text sources like e-mail, we will not use stop words, and we won't be setting this parameter value.
#
# You can take a look at all the parameter values of your `count_vector` object by simply printing out the object as follows:
# In[83]:
'''
Practice node:
Print the 'count_vector' object which is an instance of 'CountVectorizer()'
'''
# No need to revise this code
print(count_vector)
# >>**Instructions:**
# Fit your document dataset to the CountVectorizer object you have created using fit(), and get the list of words
# which have been categorized as features using the get_feature_names() method.
# In[84]:
'''
Solution:
'''
# No need to revise this code
count_vector.fit(documents)
count_vector.get_feature_names()
# The `get_feature_names()` method returns our feature names for this dataset, which is the set of words that make up our vocabulary for 'documents'.
# >>**Instructions:**
# Create a matrix with each row representing one of the 4 documents, and each column representing a word (feature name).
# Each value in the matrix will represent the frequency of the word in that column occurring in the particular document in that row.
# You can do this using the transform() method of CountVectorizer, passing in the document data set as the argument. The transform() method returns a matrix of NumPy integers, which you can convert to an array using
# toarray(). Call the array 'doc_array'.
#
# In[100]:
'''
Solution
'''
doc_array = count_vector.transform(documents)
doc_array = doc_array.toarray()
print(documents,'\n', doc_array)
# Now we have a clean representation of the documents in terms of the frequency distribution of the words in them. To make it easier to understand our next step is to convert this array into a dataframe and name the columns appropriately.
# >>**Instructions:**
# Convert the 'doc_array' we created into a dataframe, with the column names as the words (feature names). Call the dataframe 'frequency_matrix'.
#
# In[105]:
'''
Solution
'''
names = count_vector.get_feature_names()
frequency_matrix = pd.DataFrame(doc_array, columns=names)
frequency_matrix
# Congratulations! You have successfully implemented a Bag of Words problem for a document dataset that we created.
#
# One potential issue that can arise from using this method is that if our dataset of text is extremely large (say if we have a large collection of news articles or email data), there will be certain values that are more common than others simply due to the structure of the language itself. For example, words like 'is', 'the', 'an', pronouns, grammatical constructs, etc., could skew our matrix and affect our analyis.
#
# There are a couple of ways to mitigate this. One way is to use the `stop_words` parameter and set its value to `english`. This will automatically ignore all the words in our input text that are found in a built-in list of English stop words in scikit-learn.
#
# Another way of mitigating this is by using the [tfidf](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer) method. This method is out of scope for the context of this lesson.
# ### Step 3.1: Training and testing sets ###
#
# Now that we understand how to use the Bag of Words approach, we can return to our original, larger UCI dataset and proceed with our analysis. Our first step is to split our dataset into a training set and a testing set so we can first train, and then test our model.
#
# >>**Instructions:**
# Split the dataset into a training and testing set using the train_test_split method in sklearn, and print out the number of rows we have in each of our training and testing data. Split the data
# using the following variables:
# * `X_train` is our training data for the 'sms_message' column.
# * `y_train` is our training data for the 'label' column
# * `X_test` is our testing data for the 'sms_message' column.
# * `y_test` is our testing data for the 'label' column.
#
# In[106]:
'''
Solution
NOTE: sklearn.cross_validation was used here previously but it has been
deprecated in favor of sklearn.model_selection, and will be removed in v0.20.
'''
# split into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(df['sms_message'],
df['label'],
random_state=1)
print('Number of rows in the total set: {}'.format(df.shape[0]))
print('Number of rows in the training set: {}'.format(X_train.shape[0]))
print('Number of rows in the test set: {}'.format(X_test.shape[0]))
# ### Step 3.2: Applying Bag of Words processing to our dataset. ###
#
# Now that we have split the data, our next objective is to follow the steps from Step 2: Bag of words and convert our data into the desired matrix format. To do this we will be using CountVectorizer() as we did before. There are two steps to consider here:
#
# * First, we have to fit our training data (`X_train`) into `CountVectorizer()` and return the matrix.
# * Secondly, we have to transform our testing data (`X_test`) to return the matrix.
#
# Note that `X_train` is our training data for the 'sms_message' column in our dataset and we will be using this to train our model.
#
# `X_test` is our testing data for the 'sms_message' column and this is the data we will be using (after transformation to a matrix) to make predictions on. We will then compare those predictions with `y_test` in a later step.
#
# For now, we have provided the code that does the matrix transformations for you!
# In[ ]:
'''
[Practice Node]
The code for this segment is in 2 parts. First, we are learning a vocabulary dictionary for the training data
and then transforming the data into a document-term matrix; secondly, for the testing data we are only
transforming the data into a document-term matrix.
This is similar to the process we followed in Step 2.3.
We will provide the transformed data to students in the variables 'training_data' and 'testing_data'.
'''
# In[107]:
'''
Solution
'''
# Instantiate the CountVectorizer method
count_vector = CountVectorizer()
# Fit the training data and then return the matrix
training_data = count_vector.fit_transform(X_train)
# Transform testing data and return the matrix. Note we are not fitting the testing data into the CountVectorizer()
testing_data = count_vector.transform(X_test)
# ### Step 4.1: Bayes Theorem implementation from scratch ###
#
# Now that we have our dataset in the format that we need, we can move onto the next portion of our mission which is the algorithm we will use to make our predictions to classify a message as spam or not spam. Remember that at the start of the mission we briefly discussed the Bayes theorem but now we shall go into a little more detail. In layman's terms, the Bayes theorem calculates the probability of an event occurring, based on certain other probabilities that are related to the event in question. It is composed of a prior(the probabilities that we are aware of or that is given to us) and the posterior(the probabilities we are looking to compute using the priors).
#
# Let us implement the Bayes Theorem from scratch using a simple example. Let's say we are trying to find the odds of an individual having diabetes, given that he or she was tested for it and got a positive result.
# In the medical field, such probabilies play a very important role as it usually deals with life and death situations.
#
# We assume the following:
#
# `P(D)` is the probability of a person having Diabetes. It's value is `0.01` or in other words, 1% of the general population has diabetes(Disclaimer: these values are assumptions and are not reflective of any medical study).
#
# `P(Pos)` is the probability of getting a positive test result.
#
# `P(Neg)` is the probability of getting a negative test result.
#
# `P(Pos|D)` is the probability of getting a positive result on a test done for detecting diabetes, given that you have diabetes. This has a value `0.9`. In other words the test is correct 90% of the time. This is also called the Sensitivity or True Positive Rate.
#
# `P(Neg|~D)` is the probability of getting a negative result on a test done for detecting diabetes, given that you do not have diabetes. This also has a value of `0.9` and is therefore correct, 90% of the time. This is also called the Specificity or True Negative Rate.
#
# The Bayes formula is as follows:
#
# <img src="images/bayes_formula.png" height="242" width="242">
#
# * `P(A)` is the prior probability of A occurring independently. In our example this is `P(D)`. This value is given to us.
#
# * `P(B)` is the prior probability of B occurring independently. In our example this is `P(Pos)`.
#
# * `P(A|B)` is the posterior probability that A occurs given B. In our example this is `P(D|Pos)`. That is, **the probability of an individual having diabetes, given that, that individual got a positive test result. This is the value that we are looking to calculate.**
#
# * `P(B|A)` is the likelihood probability of B occurring, given A. In our example this is `P(Pos|D)`. This value is given to us.
# Putting our values into the formula for Bayes theorem we get:
#
# `P(D|Pos) = P(D) * P(Pos|D) / P(Pos)`
#
# The probability of getting a positive test result `P(Pos)` can be calculated using the Sensitivity and Specificity as follows:
#
# `P(Pos) = [P(D) * Sensitivity] + [P(~D) * (1-Specificity))]`
# In[ ]:
'''
Instructions:
Calculate probability of getting a positive test result, P(Pos)
'''
# In[108]:
'''
Solution (skeleton code will be provided)
'''
# P(D)
p_diabetes = 0.01
# P(~D)
p_no_diabetes = 0.99
# Sensitivity or P(Pos|D)
p_pos_diabetes = 0.9
# Specificity or P(Neg|~D)
p_neg_no_diabetes = 0.9
# P(Pos)
p_pos = (p_diabetes * p_pos_diabetes) + (p_no_diabetes * (1 - p_neg_no_diabetes))
print('The probability of getting a positive test result P(Pos) is: {}',format(p_pos))
# **Using all of this information we can calculate our posteriors as follows:**
#
# The probability of an individual having diabetes, given that, that individual got a positive test result:
#
# `P(D|Pos) = (P(D) * Sensitivity)) / P(Pos)`
#
# The probability of an individual not having diabetes, given that, that individual got a positive test result:
#
# `P(~D|Pos) = (P(~D) * (1-Specificity)) / P(Pos)`
#
# The sum of our posteriors will always equal `1`.
# In[ ]:
'''
Instructions:
Compute the probability of an individual having diabetes, given that, that individual got a positive test result.
In other words, compute P(D|Pos).
The formula is: P(D|Pos) = (P(D) * P(Pos|D) / P(Pos)
'''
# In[109]:
'''
Solution
'''
# P(D|Pos)
p_diabetes_pos = (p_diabetes * p_pos_diabetes) / p_pos
print('Probability of an individual having diabetes, given that that individual got a positive test result is:',format(p_diabetes_pos))
# In[ ]:
'''
Instructions:
Compute the probability of an individual not having diabetes, given that, that individual got a positive test result.
In other words, compute P(~D|Pos).
The formula is: P(~D|Pos) = P(~D) * P(Pos|~D) / P(Pos)
Note that P(Pos|~D) can be computed as 1 - P(Neg|~D).
Therefore:
P(Pos|~D) = p_pos_no_diabetes = 1 - 0.9 = 0.1
'''
# In[113]:
'''
Solution
'''
# P(Pos|~D)
p_pos_no_diabetes = 0.1
# P(~D|Pos)
p_no_diabetes_pos = (p_no_diabetes * p_pos_no_diabetes) / p_pos
print('Probability of an individual not having diabetes, given that that individual got a positive test result is:',p_no_diabetes_pos)
# Congratulations! You have implemented Bayes Theorem from scratch. Your analysis shows that even if you get a positive test result, there is only an 8.3% chance that you actually have diabetes and a 91.67% chance that you do not have diabetes. This is of course assuming that only 1% of the entire population has diabetes which is only an assumption.
# **What does the term 'Naive' in 'Naive Bayes' mean ?**
#
# The term 'Naive' in Naive Bayes comes from the fact that the algorithm considers the features that it is using to make the predictions to be independent of each other, which may not always be the case. So in our Diabetes example, we are considering only one feature, that is the test result. Say we added another feature, 'exercise'. Let's say this feature has a binary value of `0` and `1`, where the former signifies that the individual exercises less than or equal to 2 days a week and the latter signifies that the individual exercises greater than or equal to 3 days a week. If we had to use both of these features, namely the test result and the value of the 'exercise' feature, to compute our final probabilities, Bayes' theorem would fail. Naive Bayes' is an extension of Bayes' theorem that assumes that all the features are independent of each other.
# ### Step 4.2: Naive Bayes implementation from scratch ###
#
#
# Now that you have understood the ins and outs of Bayes Theorem, we will extend it to consider cases where we have more than feature.
#
# Let's say that we have two political parties' candidates, 'Jill Stein' of the Green Party and 'Gary Johnson' of the Libertarian Party and we have the probabilities of each of these candidates saying the words 'freedom', 'immigration' and 'environment' when they give a speech:
#
# * Probability that Jill Stein says 'freedom': 0.1 ---------> `P(F|J)`
# * Probability that Jill Stein says 'immigration': 0.1 -----> `P(I|J)`
# * Probability that Jill Stein says 'environment': 0.8 -----> `P(E|J)`
#
#
# * Probability that Gary Johnson says 'freedom': 0.7 -------> `P(F|G)`
# * Probability that Gary Johnson says 'immigration': 0.2 ---> `P(I|G)`
# * Probability that Gary Johnson says 'environment': 0.1 ---> `P(E|G)`
#
#
# And let us also assume that the probability of Jill Stein giving a speech, `P(J)` is `0.5` and the same for Gary Johnson, `P(G) = 0.5`.
#
#
# Given this, what if we had to find the probabilities of Jill Stein saying the words 'freedom' and 'immigration'? This is where the Naive Bayes' theorem comes into play as we are considering two features, 'freedom' and 'immigration'.
#
# Now we are at a place where we can define the formula for the Naive Bayes' theorem:
#
# <img src="images/naivebayes.png" height="342" width="342">
#
# Here, `y` is the class variable (in our case the name of the candidate) and `x1` through `xn` are the feature vectors (in our case the individual words). The theorem makes the assumption that each of the feature vectors or words (`xi`) are independent of each other.
# To break this down, we have to compute the following posterior probabilities:
#
# * `P(J|F,I)`: Given the words freedom and immigration were said, what's the probability that it was said by Jill?
#
# Using the formula and our knowledge of Bayes' theorem, we can compute this as follows: `P(J|F,I)` = `(P(J) * P(F|J) * P(I|J)) / P(F,I)`. Here `P(F,I)` is the probability of the words 'freedom' and 'immigration' being said in a speech.
#
#
# * `P(G|F,I)`: Probability that words Freedom and Immigration are said by Gary Johnson `
#
# Using the formula, we can compute this as follows: `P(G|F,I)` = `(P(G) * P(F|G) * P(I|G)) / P(F,I)`
# In[ ]:
'''
Instructions: Compute the probability of the words 'freedom' and 'immigration' being said in a speech, or
P(F,I).
The first step is multiplying the probabilities of Jill Stein giving a speech with her individual
probabilities of saying the words 'freedom' and 'immigration'. Store this in a variable called p_j_text.
The second step is multiplying the probabilities of Gary Johnson giving a speech with his individual
probabilities of saying the words 'freedom' and 'immigration'. Store this in a variable called p_g_text.
The third step is to add both of these probabilities and you will get P(F,I).
'''
# In[143]:
'''
Solution: Step 1
'''
# P(J)
p_j = 0.5
# P(F|J)
p_f_j = 0.1
# P(I|J)
p_i_j = 0.1
p_j_text = p_f_j * p_i_j * p_j
print(p_j_text)
# In[144]:
'''
Solution: Step 2
'''
# P(G)
p_g = 0.5
# P(F|G)
p_f_g = 0.7
# P(I|G)
p_i_g = 0.2
p_g_text = p_f_g * p_i_g * p_g
print(p_g_text)
# In[145]:
'''
Solution: Step 3: Compute P(F,I) and store in p_f_i
'''
p_f_i = p_j_text + p_g_text
print(f'Probability of words freedom and immigration being said are: {p_f_i:.2%}')
# Now we can compute the probability of `P(J|F,I)`, that is the probability of Jill Stein saying the words Freedom and Immigration and `P(G|F,I)`, that is the probability of Gary Johnson saying the words Freedom and Immigration.
# In[146]:
'''
Instructions:
Compute P(J|F,I) using the formula P(J|F,I) = (P(J) * P(F|J) * P(I|J)) / P(F,I) and store it in a variable p_j_fi
'''
# In[147]:
'''
Solution
'''
p_j_fi = (p_j * p_f_j * p_i_j) / p_f_i
print(f'The probability of Jill Stein saying the words Freedom and Immigration: {p_j_fi:.2%}')
# In[148]:
'''
Instructions:
Compute P(G|F,I) using the formula P(G|F,I) = (P(G) * P(F|G) * P(I|G)) / P(F,I) and store it in a variable p_g_fi
'''
# In[149]:
'''
Solution
'''
p_g_fi = (p_g * p_f_g * p_i_g) / p_f_i
print(f'The probability of Gary Johnson saying the words Freedom and Immigration: {p_g_fi:.2%}')
# And as we can see, just like in the Bayes' theorem case, the sum of our posteriors is equal to 1. Congratulations! You have implemented the Naive Bayes' theorem from scratch. Our analysis shows that there is only a 6.6% chance that Jill Stein of the Green Party uses the words 'freedom' and 'immigration' in her speech as compared the the 93.3% chance for Gary Johnson of the Libertarian party.
# Another more generic example of Naive Bayes' in action is as when we search for the term 'Sacramento Kings' in a search engine. In order for us to get the results pertaining to the Scramento Kings NBA basketball team, the search engine needs to be able to associate the two words together and not treat them individually, in which case we would get results of images tagged with 'Sacramento' like pictures of city landscapes and images of 'Kings' which could be pictures of crowns or kings from history when what we are looking to get are images of the basketball team. This is a classic case of the search engine treating the words as independent entities and hence being 'naive' in its approach.
#
#
# Applying this to our problem of classifying messages as spam, the Naive Bayes algorithm *looks at each word individually and not as associated entities* with any kind of link between them. In the case of spam detectors, this usually works as there are certain red flag words which can almost guarantee its classification as spam, for example emails with words like 'viagra' are usually classified as spam.
# ### Step 5: Naive Bayes implementation using scikit-learn ###
#
# Thankfully, sklearn has several Naive Bayes implementations that we can use and so we do not have to do the math from scratch. We will be using sklearn's `sklearn.naive_bayes` method to make predictions on our dataset.
#
# Specifically, we will be using the multinomial Naive Bayes implementation. This particular classifier is suitable for classification with discrete features (such as in our case, word counts for text classification). It takes in integer word counts as its input. On the other hand Gaussian Naive Bayes is better suited for continuous data as it assumes that the input data has a Gaussian(normal) distribution.
# In[ ]:
'''
Instructions:
We have loaded the training data into the variable 'training_data' and the testing data into the
variable 'testing_data'.
Import the MultinomialNB classifier and fit the training data into the classifier using fit(). Name your classifier
'naive_bayes'. You will be training the classifier using 'training_data' and y_train' from our split earlier.
'''
# In[156]:
'''
Solution
'''
from sklearn.naive_bayes import MultinomialNB
naive_bayes = MultinomialNB()
naive_bayes.fit(training_data, y_train)
# In[ ]:
'''
Instructions:
Now that our algorithm has been trained using the training data set we can now make some predictions on the test data
stored in 'testing_data' using predict(). Save your predictions into the 'predictions' variable.
'''
# In[159]:
'''
Solution
'''
predictions = naive_bayes.predict(testing_data)
print(predictions)
# Now that predictions have been made on our test set, we need to check the accuracy of our predictions.
# ### Step 6: Evaluating our model ###
#
# Now that we have made predictions on our test set, our next goal is to evaluate how well our model is doing. There are various mechanisms for doing so, but first let's do quick recap of them.
#
# **Accuracy** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).
#
# **Precision** tells us what proportion of messages we classified as spam, actually were spam.
# It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classification), in other words it is the ratio of
#
# `[True Positives/(True Positives + False Positives)]`
#
# **Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.
# It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of
#
# `[True Positives/(True Positives + False Negatives)]`
#
# For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam (including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam (all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is the weighted average of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score.
# We will be using all 4 metrics to make sure our model does well. For all 4 metrics whose values can range from 0 to 1, having a score as close to 1 as possible is a good indicator of how well our model is doing.
# In[ ]:
'''
Instructions:
Compute the accuracy, precision, recall and F1 scores of your model using your test data 'y_test' and the predictions
you made earlier stored in the 'predictions' variable.
'''
# In[161]:
'''
Solution
'''
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy score: ', format(accuracy_score(y_test, predictions)))
print('Precision score: ', format(precision_score(y_test, predictions)))
print('Recall score: ', format(recall_score(y_test, predictions)))
print('F1 score: ', format(f1_score(y_test, predictions)))
# ### Step 7: Conclusion ###
#
# One of the major advantages that Naive Bayes has over other classification algorithms is its ability to handle an extremely large number of features. In our case, each word is treated as a feature and there are thousands of different words. Also, it performs well even with the presence of irrelevant features and is relatively unaffected by them. The other major advantage it has is its relative simplicity. Naive Bayes' works well right out of the box and tuning it's parameters is rarely ever necessary, except usually in cases where the distribution of the data is known.
# It rarely ever overfits the data. Another important advantage is that its model training and prediction times are very fast for the amount of data it can handle. All in all, Naive Bayes' really is a gem of an algorithm!
#
# Congratulations! You have successfully designed a model that can efficiently predict if an SMS message is spam or not!
#
# Thank you for learning with us!

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29
python/Supervised Learning/Perceptron Algoritm/7 Perceptrons as Logical Operators/perceptron.py Normal file
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import pandas as pd
# TODO: Set weight1, weight2, and bias
weight1 = 0.0
weight2 = 0.0
bias = 0.0
# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [False, False, False, True]
outputs = []
# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
output = int(linear_combination >= 0)
is_correct_string = 'Yes' if output == correct_output else 'No'
outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=['Input 1', ' Input 2', ' Linear Combination', ' Activation Output', ' Is Correct'])
if not num_wrong:
print('Nice! You got it all correct.\n')
else:
print('You got {} wrong. Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))

29
python/Supervised Learning/Perceptron Algoritm/7 Perceptrons as Logical Operators/perceptron_and.py Normal file
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@@ -0,0 +1,29 @@
import pandas as pd
# TODO: Set weight1, weight2, and bias
weight1 = 1.0
weight2 = 1.0
bias = -1.25
# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [False, False, False, True]
outputs = []
# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
output = int(linear_combination >= 0)
is_correct_string = 'Yes' if output == correct_output else 'No'
outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=['Input 1', ' Input 2', ' Linear Combination', ' Activation Output', ' Is Correct'])
if not num_wrong:
print('Nice! You got it all correct.\n')
else:
print('You got {} wrong. Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))

29
python/Supervised Learning/Perceptron Algoritm/7 Perceptrons as Logical Operators/perceptron_not.py Normal file
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import pandas as pd
# TODO: Set weight1, weight2, and bias
weight1 = 0.0
weight2 = -1.0
bias = 0.8
# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [True, False, True, False]
outputs = []
# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
output = int(linear_combination >= 0)
is_correct_string = 'Yes' if output == correct_output else 'No'
outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=['Input 1', ' Input 2', ' Linear Combination', ' Activation Output', ' Is Correct'])
if not num_wrong:
print('Nice! You got it all correct.\n')
else:
print('You got {} wrong. Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))

100
python/Supervised Learning/Perceptron Algoritm/9. Perceptron Algorithm/data.csv Normal file
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@@ -0,0 +1,100 @@
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66
python/Supervised Learning/Perceptron Algoritm/9. Perceptron Algorithm/perceptron.py Normal file
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import numpy as np
# Setting the random seed, feel free to change it and see different solutions.
np.random.seed(42)
import pandas as pd
def stepFunction(t):
if t >= 0:
return 1
return 0
def prediction(X, W, b):
return stepFunction((np.matmul(X, W) + b)[0])
# TODO: Fill in the code below to implement the perceptron trick.
# The function should receive as inputs the data X, the labels y,
# the weights W (as an array), and the bias b,
# update the weights and bias W, b, according to the perceptron algorithm,
# and return W and b.
def perceptronStep(X, y, W, b, learn_rate=0.01):
for i in range(len(X)):
y_hat = prediction(X[i], W, b)
if y[i] - y_hat == 1:
W[0] += X[i][0] * learn_rate
W[1] += X[i][1] * learn_rate
b += learn_rate
elif y[i] - y_hat == -1:
W[0] -= X[i][0] * learn_rate
W[1] -= X[i][1] * learn_rate
b -= learn_rate
return W, b
# This function runs the perceptron algorithm repeatedly on the dataset,
# and returns a few of the boundary lines obtained in the iterations,
# for plotting purposes.
# Feel free to play with the learning rate and the num_epochs,
# and see your results plotted below.
def trainPerceptronAlgorithm(X, y, learn_rate=0.01, num_epochs=25):
x_min, x_max = min(X.T[0]), max(X.T[0])
y_min, y_max = min(X.T[1]), max(X.T[1])
W = np.array(np.random.rand(2, 1))
b = np.random.rand(1)[0] + x_max
# These are the solution lines that get plotted below.
boundary_lines = []
for i in range(num_epochs):
# In each epoch, we apply the perceptron step.
W, b = perceptronStep(X, y, W, b, learn_rate)
boundary_lines.append((-W[0] / W[1], -b / W[1]))
return boundary_lines
df = pd.read_csv('data.csv', header=None)
X = df.iloc[:, :-1]
X = np.array(X)
y = df.iloc[:, 1]
y = np.array(y)
print(trainPerceptronAlgorithm(X, y))
# print(X)

96
python/Supervised Learning/Support Vector Machines/data.csv Normal file
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0.21774,0.76462,0
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96 0.091014 0.20322 1

30
python/Supervised Learning/Support Vector Machines/quiz.py Normal file
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# Import statements
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Read the data.
data = np.asarray(pd.read_csv('data.csv', header=None))
# Assign the features to the variable X, and the labels to the variable y.
X = data[:, 0:2]
y = data[:, 2]
# TODO: Create the model and assign it to the variable model.
# Find the right parameters for this model to achieve 100% accuracy
# on the dataset.
model = SVC(kernel='rbf', gamma=27)
# TODO: Fit the model.
model.fit(X, y)
# TODO: Make predictions. Store them in the variable y_pred.
y_pred = model.predict(X)
# TODO: Calculate the accuracy and assign it to the variable acc.
acc = accuracy_score(y, y_pred)