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

python/Supervised Learning/Decision Trees/16. Calculating information gain on a dataset/calculate.py

@@ -0,0 +1,13 @@
+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)

python/Supervised Learning/Decision Trees/16. Calculating information gain on a dataset/ml-bugs.csv

@@ -0,0 +1,25 @@
+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

python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/data.csv

@@ -0,0 +1,96 @@
+0.24539,0.81725,0
+0.21774,0.76462,0
+0.20161,0.69737,0
+0.20161,0.58041,0
+0.2477,0.49561,0
+0.32834,0.44883,0
+0.39516,0.48099,0
+0.39286,0.57164,0
+0.33525,0.62135,0
+0.33986,0.71199,0
+0.34447,0.81433,0
+0.28226,0.82602,0
+0.26613,0.75,0
+0.26613,0.63596,0
+0.32604,0.54825,0
+0.28917,0.65643,0
+0.80069,0.71491,0
+0.80069,0.64181,0
+0.80069,0.50146,0
+0.79839,0.36988,0
+0.73157,0.25,0
+0.63249,0.18275,0
+0.60023,0.27047,0
+0.66014,0.34649,0
+0.70161,0.42251,0
+0.70853,0.53947,0
+0.71544,0.63304,0
+0.74309,0.72076,0
+0.75,0.63596,0
+0.75,0.46345,0
+0.72235,0.35526,0
+0.66935,0.28509,0
+0.20622,0.94298,1
+0.26613,0.8962,1
+0.38134,0.8962,1
+0.42051,0.94591,1
+0.49885,0.86404,1
+0.31452,0.93421,1
+0.53111,0.72076,1
+0.45276,0.74415,1
+0.53571,0.6038,1
+0.60484,0.71491,1
+0.60945,0.58333,1
+0.51267,0.47807,1
+0.50806,0.59211,1
+0.46198,0.30556,1
+0.5288,0.41082,1
+0.38594,0.35819,1
+0.31682,0.31433,1
+0.29608,0.20906,1
+0.36982,0.27632,1
+0.42972,0.18275,1
+0.51498,0.10965,1
+0.53111,0.20906,1
+0.59793,0.095029,1
+0.73848,0.086257,1
+0.83065,0.18275,1
+0.8629,0.10965,1
+0.88364,0.27924,1
+0.93433,0.30848,1
+0.93433,0.19444,1
+0.92512,0.43421,1
+0.87903,0.43421,1
+0.87903,0.58626,1
+0.9182,0.71491,1
+0.85138,0.8348,1
+0.85599,0.94006,1
+0.70853,0.94298,1
+0.70853,0.87281,1
+0.59793,0.93129,1
+0.61175,0.83187,1
+0.78226,0.82895,1
+0.78917,0.8962,1
+0.90668,0.89912,1
+0.14862,0.92251,1
+0.15092,0.85819,1
+0.097926,0.85819,1
+0.079493,0.91374,1
+0.079493,0.77632,1
+0.10945,0.79678,1
+0.12327,0.67982,1
+0.077189,0.6886,1
+0.081797,0.58626,1
+0.14862,0.58041,1
+0.14862,0.5307,1
+0.14171,0.41959,1
+0.08871,0.49269,1
+0.095622,0.36696,1
+0.24539,0.3962,1
+0.1947,0.29678,1
+0.16935,0.22368,1
+0.15553,0.13596,1
+0.23848,0.12427,1
+0.33065,0.12427,1
+0.095622,0.2617,1
+0.091014,0.20322,1

python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/quiz.py

@@ -0,0 +1,29 @@
+# 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)

python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/titanic.py

@@ -0,0 +1,160 @@
+#!/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}')
+
+

python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic.py

@@ -0,0 +1,160 @@
+#!/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}')
+
+

python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_data.csv

@@ -0,0 +1,892 @@
+,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
+214,215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
+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
+656,657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
+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
+669,670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52.0,C126,S
+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
+672,673,0,2,"Mitchell, Mr. Henry Michael",male,70.0,0,0,C.A. 24580,10.5,,S
+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
+718,719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
+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
+741,742,0,1,"Cavendish, Mr. Tyrell William",male,36.0,1,0,19877,78.85,C46,S
+742,743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21.0,2,2,PC 17608,262.375,B57 B59 B63 B66,C
+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
+760,761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
+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
+766,767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
+767,768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
+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
+771,772,0,3,"Jensen, Mr. Niels Peder",male,48.0,0,0,350047,7.8542,,S
+772,773,0,2,"Mack, Mrs. (Mary)",female,57.0,0,0,S.O./P.P. 3,10.5,E77,S
+773,774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
+774,775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54.0,1,3,29105,23.0,,S
+775,776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18.0,0,0,347078,7.75,,S
+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
+778,779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
+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
+792,793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
+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
+796,797,1,1,"Leader, Dr. Alice (Farnham)",female,49.0,0,0,17465,25.9292,D17,S
+797,798,1,3,"Osman, Mrs. Mara",female,31.0,0,0,349244,8.6833,,S
+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
+801,802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31.0,1,1,C.A. 31921,26.25,,S
+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
+807,808,0,3,"Pettersson, Miss. Ellen Natalia",female,18.0,0,0,347087,7.775,,S
+808,809,0,2,"Meyer, Mr. August",male,39.0,0,0,248723,13.0,,S
+809,810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33.0,1,0,113806,53.1,E8,S
+810,811,0,3,"Alexander, Mr. William",male,26.0,0,0,3474,7.8875,,S
+811,812,0,3,"Lester, Mr. James",male,39.0,0,0,A/4 48871,24.15,,S
+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
+814,815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
+815,816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0.0,B102,S
+816,817,0,3,"Heininen, Miss. Wendla Maria",female,23.0,0,0,STON/O2. 3101290,7.925,,S
+817,818,0,2,"Mallet, Mr. Albert",male,31.0,1,1,S.C./PARIS 2079,37.0042,,C
+818,819,0,3,"Holm, Mr. John Fredrik Alexander",male,43.0,0,0,C 7075,6.45,,S
+819,820,0,3,"Skoog, Master. Karl Thorsten",male,10.0,3,2,347088,27.9,,S
+820,821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52.0,1,1,12749,93.5,B69,S
+821,822,1,3,"Lulic, Mr. Nikola",male,27.0,0,0,315098,8.6625,,S
+822,823,0,1,"Reuchlin, Jonkheer. John George",male,38.0,0,0,19972,0.0,,S
+823,824,1,3,"Moor, Mrs. (Beila)",female,27.0,0,1,392096,12.475,E121,S
+824,825,0,3,"Panula, Master. Urho Abraham",male,2.0,4,1,3101295,39.6875,,S
+825,826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
+826,827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
+827,828,1,2,"Mallet, Master. Andre",male,1.0,0,2,S.C./PARIS 2079,37.0042,,C
+828,829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
+829,830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62.0,0,0,113572,80.0,B28,
+830,831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15.0,1,0,2659,14.4542,,C
+831,832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
+832,833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
+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
+835,836,1,1,"Compton, Miss. Sara Rebecca",female,39.0,1,1,PC 17756,83.1583,E49,C
+836,837,0,3,"Pasic, Mr. Jakob",male,21.0,0,0,315097,8.6625,,S
+837,838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
+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

python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_solution.py

@@ -0,0 +1,160 @@
+#!/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}')
+
+

python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_survival_exploration.ipynb

@@ -0,0 +1,243 @@
+{
+ "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
+}

python/Supervised Learning/Decision Trees/19. Titanic Exploration/titanic_survival_exploration_solution.ipynb

@@ -0,0 +1,764 @@
+{
+ "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
+}