├── __init__.py
├── deep_layer
├── __init__.py
├── exceptions.py
├── utils.py
├── costs.py
├── activations.py
├── optimizers.py
├── sample.py
├── layers.py
└── models.py
├── examples
├── __init__.py
└── insurance_prediction
│ ├── __init__.py
│ ├── result.png
│ ├── insurance_prediction.py
│ ├── insurance.csv
│ └── test.ipynb
├── .gitignore
└── README.md
/__init__.py:
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1 |
2 |
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/deep_layer/__init__.py:
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1 |
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/examples/__init__.py:
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1 |
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/examples/insurance_prediction/__init__.py:
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1 |
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/deep_layer/exceptions.py:
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1 | class LayerNotFound(Exception):
2 | pass
3 |
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/.gitignore:
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1 | # project
2 | .idea
3 | .ipynb_checkpoints
4 |
5 |
6 | # Python
7 | *.pyc
8 | *.py~
9 |
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/examples/insurance_prediction/result.png:
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https://raw.githubusercontent.com/AndersonJo/deep-layer/master/examples/insurance_prediction/result.png
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/deep_layer/utils.py:
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1 | def _get_function(functions, name, func=None):
2 | if func is None:
3 | func = name
4 |
5 | f = None
6 | if callable(func):
7 | f = func
8 |
9 | if type(name) == str:
10 | f = functions.get(name, f)
11 |
12 | return f
13 |
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/deep_layer/costs.py:
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1 | def mean_squared_error(y_true, y_pred):
2 | """
3 | Mean Squared Error function
4 | """
5 | N = len(y_true)
6 | return ((y_pred - y_true) ** 2) / N
7 |
8 |
9 | def dmean_squared_error(y_true, y_pred):
10 | """
11 | Derivative of the mean squared error function
12 | """
13 | N = len(y_true)
14 | return 2 * (y_pred - y_true) / N
15 |
16 |
17 | losses = dict(mean_squared_error=mean_squared_error,
18 | dmean_squared_error=dmean_squared_error,
19 | mse=mean_squared_error,
20 | dmse=dmean_squared_error)
21 |
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/deep_layer/activations.py:
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1 | import numpy as np
2 |
3 |
4 | def linear(z):
5 | return z
6 |
7 |
8 | def dlinear(z):
9 | batch_size = z.shape[0]
10 | shapes = [1 for _ in range(len(z.shape) - 1)]
11 | return np.ones((batch_size, *shapes))
12 |
13 |
14 | def sigmoid(z: np.array) -> np.array:
15 | """
16 | :param z: sum of (w^T x + b)
17 | """
18 | return 1 / (1 + np.e ** (-z))
19 |
20 |
21 | def dsigmoid(phi: np.array):
22 | return phi * (1 - phi)
23 |
24 |
25 | activations = dict(
26 | linear=linear,
27 | dlinear=dlinear,
28 | sigmoid=sigmoid,
29 | dsigmoid=dsigmoid
30 | )
31 |
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/deep_layer/optimizers.py:
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1 | class BaseOptimizer(object):
2 | def __init__(self, lr: float = 0.001):
3 | """
4 | :param lr: Learning Rate
5 | """
6 | self.lr = lr
7 |
8 |
9 | class StochasticGradientDescent(BaseOptimizer):
10 | pass
11 |
12 |
13 | class Momentum(BaseOptimizer):
14 | def __init__(self, lr: float = 0.001, gamma: float = 0.5):
15 | super(Momentum, self).__init__(lr=lr)
16 | self.gamma = gamma
17 |
18 | def __call__(self, layer, delta_w, delta_b, n: int):
19 | layer.update_w = self.gamma * layer.update_w + - 2 / n * self.lr * delta_w
20 | layer.update_b = self.gamma * layer.update_b + - 2 / n * self.lr * delta_b
21 | return layer
22 |
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/README.md:
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1 | # Deep Layer
2 | Deep Learning Library with Numpy
3 | The library support the following features
4 |
5 | 1. Deep Neural Network
6 | 2. Mini-batch
7 | 3. provide optimizations and loss functions
8 |
9 |
10 | # Example
11 | The example below predicts insurance cost.
12 | The full code is [here](https://github.com/AndersonJo/deep-layer/blob/master/examples/insurance_prediction/insurance_prediction.py)
13 |
14 | ```
15 | >> import numpy as np
16 | >> from deep_layer.layers import Layer, InputLayer
17 | >> from deep_layer.models import Model
18 | >> from deep_layer.optimizers import Momentum
19 | >>
20 | >> np.random.seed(0)
21 | >>
22 | >> model = Model()
23 | >> model.add(InputLayer(9, 32, activation='sigmoid', batch_input_shape=(2, 9), name='input_layer'))
24 | >> model.add(Layer(32, 16, activation='sigmoid', name='hidden_layer1'))
25 | >> model.add(Layer(16, 1, name='output_layer'))
26 | >> model.compile(optimizer=Momentum(lr=0.0005), batch=128, loss='mean_squared_error')
27 | >> model.fit(data_x, data_y, epochs=15, shuffle=False)
28 |
29 | [Epoch 0] loss: 0.8041550229987101
30 | [Epoch 1] loss: 0.6849386180886644
31 | [Epoch 2] loss: 0.5852776321377485
32 | [Epoch 3] loss: 0.49581906221271954
33 | [Epoch 4] loss: 0.4161674836035164
34 | [Epoch 5] loss: 0.3604390973829448
35 | [Epoch 6] loss: 0.3008422335309807
36 | [Epoch 7] loss: 0.2576607232879699
37 | [Epoch 8] loss: 0.22932737798070446
38 | [Epoch 9] loss: 0.1782664127999551
39 | [Epoch 10] loss: 0.15898536579946815
40 | [Epoch 11] loss: 0.1288886044363849
41 | [Epoch 12] loss: 0.11173969296987162
42 | [Epoch 13] loss: 0.10028934050214187
43 | [Epoch 14] loss: 0.07579451850678974
44 | ```
45 |
46 |
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/deep_layer/sample.py:
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1 | import numpy as np
2 | import math
3 |
4 |
5 | class Sample(object):
6 | def __init__(self, x: np.array, y: np.array = None, batch: int = 32):
7 | self.x: np.array = x.copy()
8 | self.y: np.array = y.copy() if y is not None else None
9 |
10 | self.batch: int = batch
11 |
12 | self._n = len(x)
13 | self._n_feature = x.shape[-1]
14 |
15 | def samples(self, n=10000, train=True, shuffle=True):
16 | split_n = len(self.x) - self.batch
17 | if split_n - self.batch <= 0:
18 | raise Exception('batch size is bigger than data size')
19 |
20 | if train:
21 | iter_range = range(n)
22 | else:
23 | split_n = int(math.ceil(len(self.x) / self.batch))
24 | iter_range = range(0, len(self.x), self.batch)
25 |
26 | for step in iter_range:
27 | i = step % split_n
28 |
29 | if i == 0 and shuffle:
30 | self.shuffle()
31 |
32 | sample_x = self.get_sample(self.x, idx=i)
33 | sample_y = self.get_sample(self.y, idx=i) if self.y is not None else None
34 |
35 | # Return
36 | if self.y is not None:
37 | yield sample_x, sample_y
38 | else:
39 | yield sample_x
40 |
41 | def get_sample(self, data: np.array, idx: int):
42 |
43 | sample = data[idx:idx + self.batch]
44 | n = len(sample)
45 | if n != self.batch and n != 0:
46 | sample = self.make_same(data, sample)
47 | return sample
48 |
49 | def make_same(self, data, sample):
50 | need_n = self.batch - len(sample)
51 | n = len(data)
52 | indices = np.random.randint(0, n, size=need_n)
53 | con_sample = np.concatenate((sample, data[indices]), axis=0)
54 |
55 | return con_sample
56 |
57 | def zeropad(self, data):
58 | if data is None:
59 | return None
60 | padded_data = np.zeros((self.batch, self._n_feature))
61 | padded_data[:len(data)] = data
62 | return padded_data
63 |
64 | def shuffle(self):
65 | rands = np.random.permutation(self._n)
66 | self.x = self.x[rands]
67 | if self.y is not None:
68 | self.y = self.y[rands]
69 |
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/examples/insurance_prediction/insurance_prediction.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn.metrics import r2_score
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.preprocessing import StandardScaler, MinMaxScaler
6 |
7 | from deep_layer.layers import Layer, InputLayer
8 | from deep_layer.models import Model
9 | from deep_layer.optimizers import Momentum
10 | import pylab as plt
11 |
12 | np.random.seed(0)
13 |
14 | raw_data = pd.read_csv('insurance.csv')
15 |
16 | # Preprocessing
17 | bmi_scaler = StandardScaler()
18 | raw_data[['bmi']] = bmi_scaler.fit_transform(raw_data[['bmi']].values)
19 | raw_data['sex'] = raw_data['sex'].apply({'male': 1, 'female': 0}.get)
20 | raw_data['smoker'] = raw_data['smoker'].apply({'yes': 1, 'no': 0}.get)
21 |
22 | # Region
23 | onehot_smokers = pd.get_dummies(raw_data['region'], prefix='region')
24 | raw_data = pd.concat([raw_data, onehot_smokers], axis=1)
25 | # raw_data = raw_data.drop('region', axis=1)
26 |
27 | # Y Labels
28 | expense_scaler = MinMaxScaler()
29 | raw_data['expenses'] = expense_scaler.fit_transform(raw_data[['expenses']].values)
30 | data_y = raw_data.as_matrix(['expenses'])
31 |
32 | # X Labels
33 | data_x = raw_data.drop(['expenses', 'region'], axis=1)
34 | data_x = data_x.as_matrix()
35 | data_scaler = MinMaxScaler()
36 | data_x = data_scaler.fit_transform(data_x)
37 |
38 | # Split data into train and examples subset
39 | train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.3)
40 |
41 | # Create Model
42 | model = Model()
43 | model.add(InputLayer(9, 32, activation='sigmoid', batch_input_shape=(2, 9), name='input_layer'))
44 | model.add(Layer(32, 16, activation='sigmoid', name='hidden_layer1'))
45 | # model.add(Layer(16, 8, activation='sigmoid', name='hidden_layer2'))
46 | model.add(Layer(16, 1, activation='sigmoid', name='output_layer'))
47 | model.compile(optimizer=Momentum(lr=0.001), batch=64, loss='mean_squared_error')
48 | model.fit(data_x, data_y, epochs=98, shuffle=False)
49 |
50 | y_pred = model.predict(test_x)
51 |
52 | print('r^2 score:', r2_score(test_y, y_pred))
53 | # y_pred = expense_scaler.inverse_transform(y_pred)
54 | # y_true = expense_scaler.inverse_transform(test_y)
55 | N = len(y_pred)
56 | x = list(range(N))
57 |
58 | plt.scatter(x, test_y, color='#333333', label='y_true')
59 | plt.scatter(x, y_pred, color='red', label='y_pred')
60 | plt.grid()
61 | plt.legend()
62 | plt.savefig('result.png')
63 |
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/deep_layer/layers.py:
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1 | import numpy as np
2 |
3 | from deep_layer.activations import activations
4 | from deep_layer.utils import _get_function
5 |
6 |
7 | class BaseLayer(object):
8 | def __init__(self,
9 | n_in: int,
10 | n_out: int,
11 | name: str = None,
12 | batch_input_shape: tuple = None):
13 | self.n_in: int = n_in
14 | self.n_out: int = n_out
15 | self.name = name
16 | self.batch_input_shape: tuple = batch_input_shape
17 |
18 | def compile(self):
19 | raise NotImplementedError('compile method should be implemented')
20 |
21 | def is_input_layer(self):
22 | raise NotImplementedError('is_input_layer method should be implemented')
23 |
24 | def feedforward(self, tensor: np.array):
25 | raise NotImplementedError('feedforward method should be implemented')
26 |
27 | def __str__(self):
28 | return f''
29 |
30 |
31 | class Layer(BaseLayer):
32 | def __init__(self,
33 | n_in: int,
34 | n_out: int,
35 | activation: str = 'linear',
36 | batch_input_shape: tuple = None,
37 | name: str = None):
38 | super(Layer, self).__init__(n_in, n_out,
39 | name=name,
40 | batch_input_shape=batch_input_shape)
41 |
42 | self.activation = _get_function(activations, activation, activation)
43 | self.dactivation = _get_function(activations, f'd{activation}', activation)
44 |
45 | self.w: np.array = None
46 | self.b: np.array = None
47 |
48 | self.update_w = 0
49 | self.update_b = 0
50 |
51 | # Shape
52 | self._shape = None
53 |
54 | def compile(self, batch=32):
55 | self.w = np.random.randn(self.n_in, self.n_out)
56 | self.b = np.zeros((batch, self.n_out))
57 |
58 | def zero_grad(self):
59 | self.update_w = 0
60 | self.update_b = 0
61 |
62 | def update(self):
63 | self.w += self.update_w
64 | self.b += self.update_b
65 |
66 | def get_shape(self):
67 | return self._shape
68 |
69 | def get_weights(self):
70 | return self.w, self.b
71 |
72 | def is_input_layer(self):
73 | return False
74 |
75 | def predict(self, tensor):
76 | h = tensor.dot(self.w) + self.b
77 | if self.activation:
78 | h = self.activation(h)
79 | return h
80 |
81 | def feedforward(self, tensor: np.array):
82 | y_pred = self.predict(tensor)
83 | return y_pred
84 |
85 |
86 | class InputLayer(Layer):
87 | def is_input_layer(self):
88 | return True
89 |
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/deep_layer/models.py:
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1 | import numpy as np
2 |
3 | from deep_layer.exceptions import LayerNotFound
4 | from deep_layer.layers import Layer, BaseLayer
5 | from deep_layer.costs import losses
6 | from deep_layer.sample import Sample
7 | from deep_layer.utils import _get_function
8 |
9 |
10 | class BaseModel(object):
11 | def __init__(self):
12 | self.layers = list()
13 | self.x_train: np.array = None
14 | self.y_train: np.array = None
15 |
16 | self.batch_size = None
17 | self.optimizer = None
18 | self.loss = None
19 | self.dloss = None
20 |
21 | self.last_n_in = 0
22 | self.last_n_out = 0
23 |
24 | def compile(self, optimizer, loss: str, batch=32):
25 | if not self.layers:
26 | raise LayerNotFound('Layer is not found. you must add at least one layer to the model')
27 |
28 | self.batch_size = batch
29 | self.optimizer = optimizer
30 | self.loss = _get_function(losses, loss, loss)
31 | self.dloss = _get_function(losses, f'd{loss}', loss)
32 |
33 | for layer in self.layers:
34 | layer.compile(batch=batch)
35 |
36 | last_layer = self.layers[-1]
37 | self.last_n_in: int = last_layer.n_in
38 | self.last_n_out: int = last_layer.n_out
39 |
40 | def predict(self, x):
41 | n = len(x)
42 | sample = Sample(x, batch=self.batch_size)
43 |
44 | response = list()
45 | for tensor in sample.samples(train=False, shuffle=False):
46 | for layer in self.layers:
47 | tensor = layer.feedforward(tensor)
48 | response.append(tensor)
49 |
50 | response = np.array(response)
51 | response = response.reshape(-1, self.last_n_out)[:n]
52 | print('response:', response.shape)
53 | return response
54 |
55 | def feedforward(self, x: np.array):
56 | outputs = []
57 | tensor = x
58 | for layer in self.layers:
59 | tensor = layer.feedforward(tensor)
60 | outputs.append(tensor)
61 |
62 | return outputs
63 |
64 | def backpropagation(self,
65 | outputs: np.array,
66 | x: np.array,
67 | y: np.array,
68 | n_data: int = 2):
69 | outputs.insert(0, x)
70 | N = len(self.layers)
71 | deltas = []
72 | delta = None
73 | loss = np.nan
74 |
75 | for i in range(N, 0, -1):
76 | output: np.array = outputs[i]
77 | prev_output: np.array = outputs[i - 1]
78 | layer: Layer = self.layers[i - 1]
79 |
80 | if i == N:
81 | d1 = self.dloss(y, output)
82 | loss = np.sum(d1)
83 | else:
84 | layer2: Layer = self.layers[i]
85 | w2, b2 = layer2.get_weights()
86 | d1 = delta.dot(w2.T)
87 |
88 | delta = d1 * layer.dactivation(output)
89 | delta_w = prev_output.T.dot(delta)
90 | delta_b = delta
91 | deltas.append((delta_w, delta_b))
92 |
93 | layers = self.layers[::-1]
94 | for i in range(len(deltas) - 1, -1, -1):
95 | delta_w, delta_b = deltas[i]
96 | layer = layers[i]
97 |
98 | layer.zero_grad()
99 | layer = self.optimizer(layer, delta_w, delta_b, n_data)
100 | layer.update()
101 |
102 | return dict(loss=loss)
103 |
104 |
105 | class Model(BaseModel):
106 | def __init__(self):
107 | super(Model, self).__init__()
108 |
109 | def add(self, layer: BaseLayer):
110 | self.layers.append(layer)
111 |
112 | def fit(self,
113 | x_train: np.array,
114 | y_train: np.array,
115 | shuffle: bool = True,
116 | epochs=10):
117 |
118 | sample = Sample(x_train, y_train, batch=self.batch_size)
119 | N = len(x_train)
120 |
121 | for epoch in range(epochs):
122 | sample.shuffle()
123 |
124 | losses = list()
125 | for i, (sample_x, sample_y) in enumerate(sample.samples(n=30000, shuffle=True)):
126 | # Feedforward
127 | tensors = self.feedforward(sample_x)
128 |
129 | # Backpropagation
130 | result = self.backpropagation(tensors, sample_x, sample_y, n_data=N)
131 | losses.append(result['loss'])
132 |
133 | print(f'[Epoch {epoch}] loss: {np.mean(losses)}')
134 |
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/examples/insurance_prediction/insurance.csv:
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1 | age,sex,bmi,children,smoker,region,expenses
19,female,27.9,0,yes,southwest,16884.92
18,male,33.8,1,no,southeast,1725.55
28,male,33.0,3,no,southeast,4449.46
33,male,22.7,0,no,northwest,21984.47
32,male,28.9,0,no,northwest,3866.86
31,female,25.7,0,no,southeast,3756.62
46,female,33.4,1,no,southeast,8240.59
37,female,27.7,3,no,northwest,7281.51
37,male,29.8,2,no,northeast,6406.41
60,female,25.8,0,no,northwest,28923.14
25,male,26.2,0,no,northeast,2721.32
62,female,26.3,0,yes,southeast,27808.73
23,male,34.4,0,no,southwest,1826.84
56,female,39.8,0,no,southeast,11090.72
27,male,42.1,0,yes,southeast,39611.76
19,male,24.6,1,no,southwest,1837.24
52,female,30.8,1,no,northeast,10797.34
23,male,23.8,0,no,northeast,2395.17
56,male,40.3,0,no,southwest,10602.39
30,male,35.3,0,yes,southwest,36837.47
60,female,36.0,0,no,northeast,13228.85
30,female,32.4,1,no,southwest,4149.74
18,male,34.1,0,no,southeast,1137.01
34,female,31.9,1,yes,northeast,37701.88
37,male,28.0,2,no,northwest,6203.9
59,female,27.7,3,no,southeast,14001.13
63,female,23.1,0,no,northeast,14451.84
55,female,32.8,2,no,northwest,12268.63
23,male,17.4,1,no,northwest,2775.19
31,male,36.3,2,yes,southwest,38711
22,male,35.6,0,yes,southwest,35585.58
18,female,26.3,0,no,northeast,2198.19
19,female,28.6,5,no,southwest,4687.8
63,male,28.3,0,no,northwest,13770.1
28,male,36.4,1,yes,southwest,51194.56
19,male,20.4,0,no,northwest,1625.43
62,female,33.0,3,no,northwest,15612.19
26,male,20.8,0,no,southwest,2302.3
35,male,36.7,1,yes,northeast,39774.28
60,male,39.9,0,yes,southwest,48173.36
24,female,26.6,0,no,northeast,3046.06
31,female,36.6,2,no,southeast,4949.76
41,male,21.8,1,no,southeast,6272.48
37,female,30.8,2,no,southeast,6313.76
38,male,37.1,1,no,northeast,6079.67
55,male,37.3,0,no,southwest,20630.28
18,female,38.7,2,no,northeast,3393.36
28,female,34.8,0,no,northwest,3556.92
60,female,24.5,0,no,southeast,12629.9
36,male,35.2,1,yes,southeast,38709.18
18,female,35.6,0,no,northeast,2211.13
21,female,33.6,2,no,northwest,3579.83
48,male,28.0,1,yes,southwest,23568.27
36,male,34.4,0,yes,southeast,37742.58
40,female,28.7,3,no,northwest,8059.68
58,male,37.0,2,yes,northwest,47496.49
58,female,31.8,2,no,northeast,13607.37
18,male,31.7,2,yes,southeast,34303.17
53,female,22.9,1,yes,southeast,23244.79
34,female,37.3,2,no,northwest,5989.52
43,male,27.4,3,no,northeast,8606.22
25,male,33.7,4,no,southeast,4504.66
64,male,24.7,1,no,northwest,30166.62
28,female,25.9,1,no,northwest,4133.64
20,female,22.4,0,yes,northwest,14711.74
19,female,28.9,0,no,southwest,1743.21
61,female,39.1,2,no,southwest,14235.07
40,male,26.3,1,no,northwest,6389.38
40,female,36.2,0,no,southeast,5920.1
28,male,24.0,3,yes,southeast,17663.14
27,female,24.8,0,yes,southeast,16577.78
31,male,28.5,5,no,northeast,6799.46
53,female,28.1,3,no,southwest,11741.73
58,male,32.0,1,no,southeast,11946.63
44,male,27.4,2,no,southwest,7726.85
57,male,34.0,0,no,northwest,11356.66
29,female,29.6,1,no,southeast,3947.41
21,male,35.5,0,no,southeast,1532.47
22,female,39.8,0,no,northeast,2755.02
41,female,33.0,0,no,northwest,6571.02
31,male,26.9,1,no,northeast,4441.21
45,female,38.3,0,no,northeast,7935.29
22,male,37.6,1,yes,southeast,37165.16
48,female,41.2,4,no,northwest,11033.66
37,female,34.8,2,yes,southwest,39836.52
45,male,22.9,2,yes,northwest,21098.55
57,female,31.2,0,yes,northwest,43578.94
56,female,27.2,0,no,southwest,11073.18
46,female,27.7,0,no,northwest,8026.67
55,female,27.0,0,no,northwest,11082.58
21,female,39.5,0,no,southeast,2026.97
53,female,24.8,1,no,northwest,10942.13
59,male,29.8,3,yes,northeast,30184.94
35,male,34.8,2,no,northwest,5729.01
64,female,31.3,2,yes,southwest,47291.06
28,female,37.6,1,no,southeast,3766.88
54,female,30.8,3,no,southwest,12105.32
55,male,38.3,0,no,southeast,10226.28
56,male,20.0,0,yes,northeast,22412.65
38,male,19.3,0,yes,southwest,15820.7
41,female,31.6,0,no,southwest,6186.13
30,male,25.5,0,no,northeast,3645.09
18,female,30.1,0,no,northeast,21344.85
61,female,29.9,3,yes,southeast,30942.19
34,female,27.5,1,no,southwest,5003.85
20,male,28.0,1,yes,northwest,17560.38
19,female,28.4,1,no,southwest,2331.52
26,male,30.9,2,no,northwest,3877.3
29,male,27.9,0,no,southeast,2867.12
63,male,35.1,0,yes,southeast,47055.53
54,male,33.6,1,no,northwest,10825.25
55,female,29.7,2,no,southwest,11881.36
37,male,30.8,0,no,southwest,4646.76
21,female,35.7,0,no,northwest,2404.73
52,male,32.2,3,no,northeast,11488.32
60,male,28.6,0,no,northeast,30260
58,male,49.1,0,no,southeast,11381.33
29,female,27.9,1,yes,southeast,19107.78
49,female,27.2,0,no,southeast,8601.33
37,female,23.4,2,no,northwest,6686.43
44,male,37.1,2,no,southwest,7740.34
18,male,23.8,0,no,northeast,1705.62
20,female,29.0,0,no,northwest,2257.48
44,male,31.4,1,yes,northeast,39556.49
47,female,33.9,3,no,northwest,10115.01
26,female,28.8,0,no,northeast,3385.4
19,female,28.3,0,yes,southwest,17081.08
52,female,37.4,0,no,southwest,9634.54
32,female,17.8,2,yes,northwest,32734.19
38,male,34.7,2,no,southwest,6082.41
59,female,26.5,0,no,northeast,12815.44
61,female,22.0,0,no,northeast,13616.36
53,female,35.9,2,no,southwest,11163.57
19,male,25.6,0,no,northwest,1632.56
20,female,28.8,0,no,northeast,2457.21
22,female,28.1,0,no,southeast,2155.68
19,male,34.1,0,no,southwest,1261.44
22,male,25.2,0,no,northwest,2045.69
54,female,31.9,3,no,southeast,27322.73
22,female,36.0,0,no,southwest,2166.73
34,male,22.4,2,no,northeast,27375.9
26,male,32.5,1,no,northeast,3490.55
34,male,25.3,2,yes,southeast,18972.5
29,male,29.7,2,no,northwest,18157.88
30,male,28.7,3,yes,northwest,20745.99
29,female,38.8,3,no,southeast,5138.26
46,male,30.5,3,yes,northwest,40720.55
51,female,37.7,1,no,southeast,9877.61
53,female,37.4,1,no,northwest,10959.69
19,male,28.4,1,no,southwest,1842.52
35,male,24.1,1,no,northwest,5125.22
48,male,29.7,0,no,southeast,7789.64
32,female,37.1,3,no,northeast,6334.34
42,female,23.4,0,yes,northeast,19964.75
40,female,25.5,1,no,northeast,7077.19
44,male,39.5,0,no,northwest,6948.7
48,male,24.4,0,yes,southeast,21223.68
18,male,25.2,0,yes,northeast,15518.18
30,male,35.5,0,yes,southeast,36950.26
50,female,27.8,3,no,southeast,19749.38
42,female,26.6,0,yes,northwest,21348.71
18,female,36.9,0,yes,southeast,36149.48
54,male,39.6,1,no,southwest,10450.55
32,female,29.8,2,no,southwest,5152.13
37,male,29.6,0,no,northwest,5028.15
47,male,28.2,4,no,northeast,10407.09
20,female,37.0,5,no,southwest,4830.63
32,female,33.2,3,no,northwest,6128.8
19,female,31.8,1,no,northwest,2719.28
27,male,18.9,3,no,northeast,4827.9
63,male,41.5,0,no,southeast,13405.39
49,male,30.3,0,no,southwest,8116.68
18,male,16.0,0,no,northeast,1694.8
35,female,34.8,1,no,southwest,5246.05
24,female,33.3,0,no,northwest,2855.44
63,female,37.7,0,yes,southwest,48824.45
38,male,27.8,2,no,northwest,6455.86
54,male,29.2,1,no,southwest,10436.1
46,female,28.9,2,no,southwest,8823.28
41,female,33.2,3,no,northeast,8538.29
58,male,28.6,0,no,northwest,11735.88
18,female,38.3,0,no,southeast,1631.82
22,male,20.0,3,no,northeast,4005.42
44,female,26.4,0,no,northwest,7419.48
44,male,30.7,2,no,southeast,7731.43
36,male,41.9,3,yes,northeast,43753.34
26,female,29.9,2,no,southeast,3981.98
30,female,30.9,3,no,southwest,5325.65
41,female,32.2,1,no,southwest,6775.96
29,female,32.1,2,no,northwest,4922.92
61,male,31.6,0,no,southeast,12557.61
36,female,26.2,0,no,southwest,4883.87
25,male,25.7,0,no,southeast,2137.65
56,female,26.6,1,no,northwest,12044.34
18,male,34.4,0,no,southeast,1137.47
19,male,30.6,0,no,northwest,1639.56
39,female,32.8,0,no,southwest,5649.72
45,female,28.6,2,no,southeast,8516.83
51,female,18.1,0,no,northwest,9644.25
64,female,39.3,0,no,northeast,14901.52
19,female,32.1,0,no,northwest,2130.68
48,female,32.2,1,no,southeast,8871.15
60,female,24.0,0,no,northwest,13012.21
27,female,36.1,0,yes,southeast,37133.9
46,male,22.3,0,no,southwest,7147.11
28,female,28.9,1,no,northeast,4337.74
59,male,26.4,0,no,southeast,11743.3
35,male,27.7,2,yes,northeast,20984.09
63,female,31.8,0,no,southwest,13880.95
40,male,41.2,1,no,northeast,6610.11
20,male,33.0,1,no,southwest,1980.07
40,male,30.9,4,no,northwest,8162.72
24,male,28.5,2,no,northwest,3537.7
34,female,26.7,1,no,southeast,5002.78
45,female,30.9,2,no,southwest,8520.03
41,female,37.1,2,no,southwest,7371.77
53,female,26.6,0,no,northwest,10355.64
27,male,23.1,0,no,southeast,2483.74
26,female,29.9,1,no,southeast,3392.98
24,female,23.2,0,no,southeast,25081.77
34,female,33.7,1,no,southwest,5012.47
53,female,33.3,0,no,northeast,10564.88
32,male,30.8,3,no,southwest,5253.52
19,male,34.8,0,yes,southwest,34779.62
42,male,24.6,0,yes,southeast,19515.54
55,male,33.9,3,no,southeast,11987.17
28,male,38.1,0,no,southeast,2689.5
58,female,41.9,0,no,southeast,24227.34
41,female,31.6,1,no,northeast,7358.18
47,male,25.5,2,no,northeast,9225.26
42,female,36.2,1,no,northwest,7443.64
59,female,27.8,3,no,southeast,14001.29
19,female,17.8,0,no,southwest,1727.79
59,male,27.5,1,no,southwest,12333.83
39,male,24.5,2,no,northwest,6710.19
40,female,22.2,2,yes,southeast,19444.27
18,female,26.7,0,no,southeast,1615.77
31,male,38.4,2,no,southeast,4463.21
19,male,29.1,0,yes,northwest,17352.68
44,male,38.1,1,no,southeast,7152.67
23,female,36.7,2,yes,northeast,38511.63
33,female,22.1,1,no,northeast,5354.07
55,female,26.8,1,no,southwest,35160.13
40,male,35.3,3,no,southwest,7196.87
63,female,27.7,0,yes,northeast,29523.17
54,male,30.0,0,no,northwest,24476.48
60,female,38.1,0,no,southeast,12648.7
24,male,35.9,0,no,southeast,1986.93
19,male,20.9,1,no,southwest,1832.09
29,male,29.0,1,no,northeast,4040.56
18,male,17.3,2,yes,northeast,12829.46
63,female,32.2,2,yes,southwest,47305.31
54,male,34.2,2,yes,southeast,44260.75
27,male,30.3,3,no,southwest,4260.74
50,male,31.8,0,yes,northeast,41097.16
55,female,25.4,3,no,northeast,13047.33
56,male,33.6,0,yes,northwest,43921.18
38,female,40.2,0,no,southeast,5400.98
51,male,24.4,4,no,northwest,11520.1
19,male,31.9,0,yes,northwest,33750.29
58,female,25.2,0,no,southwest,11837.16
20,female,26.8,1,yes,southeast,17085.27
52,male,24.3,3,yes,northeast,24869.84
19,male,37.0,0,yes,northwest,36219.41
53,female,38.1,3,no,southeast,20463
46,male,42.4,3,yes,southeast,46151.12
40,male,19.8,1,yes,southeast,17179.52
59,female,32.4,3,no,northeast,14590.63
45,male,30.2,1,no,southwest,7441.05
49,male,25.8,1,no,northeast,9282.48
18,male,29.4,1,no,southeast,1719.44
50,male,34.2,2,yes,southwest,42856.84
41,male,37.1,2,no,northwest,7265.7
50,male,27.5,1,no,northeast,9617.66
25,male,27.6,0,no,northwest,2523.17
47,female,26.6,2,no,northeast,9715.84
19,male,20.6,2,no,northwest,2803.7
22,female,24.3,0,no,southwest,2150.47
59,male,31.8,2,no,southeast,12928.79
51,female,21.6,1,no,southeast,9855.13
40,female,28.1,1,yes,northeast,22331.57
54,male,40.6,3,yes,northeast,48549.18
30,male,27.6,1,no,northeast,4237.13
55,female,32.4,1,no,northeast,11879.1
52,female,31.2,0,no,southwest,9625.92
46,male,26.6,1,no,southeast,7742.11
46,female,48.1,2,no,northeast,9432.93
63,female,26.2,0,no,northwest,14256.19
59,female,36.8,1,yes,northeast,47896.79
52,male,26.4,3,no,southeast,25992.82
28,female,33.4,0,no,southwest,3172.02
29,male,29.6,1,no,northeast,20277.81
25,male,45.5,2,yes,southeast,42112.24
22,female,28.8,0,no,southeast,2156.75
25,male,26.8,3,no,southwest,3906.13
18,male,23.0,0,no,northeast,1704.57
19,male,27.7,0,yes,southwest,16297.85
47,male,25.4,1,yes,southeast,21978.68
31,male,34.4,3,yes,northwest,38746.36
48,female,28.9,1,no,northwest,9249.5
36,male,27.6,3,no,northeast,6746.74
53,female,22.6,3,yes,northeast,24873.38
56,female,37.5,2,no,southeast,12265.51
28,female,33.0,2,no,southeast,4349.46
57,female,38.0,2,no,southwest,12646.21
29,male,33.3,2,no,northwest,19442.35
28,female,27.5,2,no,southwest,20177.67
30,female,33.3,1,no,southeast,4151.03
58,male,34.9,0,no,northeast,11944.59
41,female,33.1,2,no,northwest,7749.16
50,male,26.6,0,no,southwest,8444.47
19,female,24.7,0,no,southwest,1737.38
43,male,36.0,3,yes,southeast,42124.52
49,male,35.9,0,no,southeast,8124.41
27,female,31.4,0,yes,southwest,34838.87
52,male,33.3,0,no,northeast,9722.77
50,male,32.2,0,no,northwest,8835.26
54,male,32.8,0,no,northeast,10435.07
44,female,27.6,0,no,northwest,7421.19
32,male,37.3,1,no,northeast,4667.61
34,male,25.3,1,no,northwest,4894.75
26,female,29.6,4,no,northeast,24671.66
34,male,30.8,0,yes,southwest,35491.64
57,male,40.9,0,no,northeast,11566.3
29,male,27.2,0,no,southwest,2866.09
40,male,34.1,1,no,northeast,6600.21
27,female,23.2,1,no,southeast,3561.89
45,male,36.5,2,yes,northwest,42760.5
64,female,33.8,1,yes,southwest,47928.03
52,male,36.7,0,no,southwest,9144.57
61,female,36.4,1,yes,northeast,48517.56
52,male,27.4,0,yes,northwest,24393.62
61,female,31.2,0,no,northwest,13429.04
56,female,28.8,0,no,northeast,11658.38
43,female,35.7,2,no,northeast,19144.58
64,male,34.5,0,no,southwest,13822.8
60,male,25.7,0,no,southeast,12142.58
62,male,27.6,1,no,northwest,13937.67
50,male,32.3,1,yes,northeast,41919.1
46,female,27.7,1,no,southeast,8232.64
24,female,27.6,0,no,southwest,18955.22
62,male,30.0,0,no,northwest,13352.1
60,female,27.6,0,no,northeast,13217.09
63,male,36.8,0,no,northeast,13981.85
49,female,41.5,4,no,southeast,10977.21
34,female,29.3,3,no,southeast,6184.3
33,male,35.8,2,no,southeast,4890
46,male,33.3,1,no,northeast,8334.46
36,female,29.9,1,no,southeast,5478.04
19,male,27.8,0,no,northwest,1635.73
57,female,23.2,0,no,northwest,11830.61
50,female,25.6,0,no,southwest,8932.08
30,female,27.7,0,no,southwest,3554.2
33,male,35.2,0,no,northeast,12404.88
18,female,38.3,0,no,southeast,14133.04
46,male,27.6,0,no,southwest,24603.05
46,male,43.9,3,no,southeast,8944.12
47,male,29.8,3,no,northwest,9620.33
23,male,41.9,0,no,southeast,1837.28
18,female,20.8,0,no,southeast,1607.51
48,female,32.3,2,no,northeast,10043.25
35,male,30.5,1,no,southwest,4751.07
19,female,21.7,0,yes,southwest,13844.51
21,female,26.4,1,no,southwest,2597.78
21,female,21.9,2,no,southeast,3180.51
49,female,30.8,1,no,northeast,9778.35
56,female,32.3,3,no,northeast,13430.27
42,female,25.0,2,no,northwest,8017.06
44,male,32.0,2,no,northwest,8116.27
18,male,30.4,3,no,northeast,3481.87
61,female,21.1,0,no,northwest,13415.04
57,female,22.2,0,no,northeast,12029.29
42,female,33.2,1,no,northeast,7639.42
26,male,32.9,2,yes,southwest,36085.22
20,male,33.3,0,no,southeast,1391.53
23,female,28.3,0,yes,northwest,18033.97
39,female,24.9,3,yes,northeast,21659.93
24,male,40.2,0,yes,southeast,38126.25
64,female,30.1,3,no,northwest,16455.71
62,male,31.5,1,no,southeast,27000.98
27,female,18.0,2,yes,northeast,15006.58
55,male,30.7,0,yes,northeast,42303.69
55,male,33.0,0,no,southeast,20781.49
35,female,43.3,2,no,southeast,5846.92
44,male,22.1,2,no,northeast,8302.54
19,male,34.4,0,no,southwest,1261.86
58,female,39.1,0,no,southeast,11856.41
50,male,25.4,2,no,northwest,30284.64
26,female,22.6,0,no,northwest,3176.82
24,female,30.2,3,no,northwest,4618.08
48,male,35.6,4,no,northeast,10736.87
19,female,37.4,0,no,northwest,2138.07
48,male,31.4,1,no,northeast,8964.06
49,male,31.4,1,no,northeast,9290.14
46,female,32.3,2,no,northeast,9411.01
46,male,19.9,0,no,northwest,7526.71
43,female,34.4,3,no,southwest,8522
21,male,31.0,0,no,southeast,16586.5
64,male,25.6,2,no,southwest,14988.43
18,female,38.2,0,no,southeast,1631.67
51,female,20.6,0,no,southwest,9264.8
47,male,47.5,1,no,southeast,8083.92
64,female,33.0,0,no,northwest,14692.67
49,male,32.3,3,no,northwest,10269.46
31,male,20.4,0,no,southwest,3260.2
52,female,38.4,2,no,northeast,11396.9
33,female,24.3,0,no,southeast,4185.1
47,female,23.6,1,no,southwest,8539.67
38,male,21.1,3,no,southeast,6652.53
32,male,30.0,1,no,southeast,4074.45
19,male,17.5,0,no,northwest,1621.34
44,female,20.2,1,yes,northeast,19594.81
26,female,17.2,2,yes,northeast,14455.64
25,male,23.9,5,no,southwest,5080.1
19,female,35.2,0,no,northwest,2134.9
43,female,35.6,1,no,southeast,7345.73
52,male,34.1,0,no,southeast,9140.95
36,female,22.6,2,yes,southwest,18608.26
64,male,39.2,1,no,southeast,14418.28
63,female,27.0,0,yes,northwest,28950.47
64,male,33.9,0,yes,southeast,46889.26
61,male,35.9,0,yes,southeast,46599.11
40,male,32.8,1,yes,northeast,39125.33
25,male,30.6,0,no,northeast,2727.4
48,male,30.2,2,no,southwest,8968.33
45,male,24.3,5,no,southeast,9788.87
38,female,27.3,1,no,northeast,6555.07
18,female,29.2,0,no,northeast,7323.73
21,female,16.8,1,no,northeast,3167.46
27,female,30.4,3,no,northwest,18804.75
19,male,33.1,0,no,southwest,23082.96
29,female,20.2,2,no,northwest,4906.41
42,male,26.9,0,no,southwest,5969.72
60,female,30.5,0,no,southwest,12638.2
31,male,28.6,1,no,northwest,4243.59
60,male,33.1,3,no,southeast,13919.82
22,male,31.7,0,no,northeast,2254.8
35,male,28.9,3,no,southwest,5926.85
52,female,46.8,5,no,southeast,12592.53
26,male,29.5,0,no,northeast,2897.32
31,female,32.7,1,no,northwest,4738.27
33,female,33.5,0,yes,southwest,37079.37
18,male,43.0,0,no,southeast,1149.4
59,female,36.5,1,no,southeast,28287.9
56,male,26.7,1,yes,northwest,26109.33
45,female,33.1,0,no,southwest,7345.08
60,male,29.6,0,no,northeast,12731
56,female,25.7,0,no,northwest,11454.02
40,female,29.6,0,no,southwest,5910.94
35,male,38.6,1,no,southwest,4762.33
39,male,29.6,4,no,southwest,7512.27
30,male,24.1,1,no,northwest,4032.24
24,male,23.4,0,no,southwest,1969.61
20,male,29.7,0,no,northwest,1769.53
32,male,46.5,2,no,southeast,4686.39
59,male,37.4,0,no,southwest,21797
55,female,30.1,2,no,southeast,11881.97
57,female,30.5,0,no,northwest,11840.78
56,male,39.6,0,no,southwest,10601.41
40,female,33.0,3,no,southeast,7682.67
49,female,36.6,3,no,southeast,10381.48
42,male,30.0,0,yes,southwest,22144.03
62,female,38.1,2,no,northeast,15230.32
56,male,25.9,0,no,northeast,11165.42
19,male,25.2,0,no,northwest,1632.04
30,female,28.4,1,yes,southeast,19521.97
60,female,28.7,1,no,southwest,13224.69
56,female,33.8,2,no,northwest,12643.38
28,female,24.3,1,no,northeast,23288.93
18,female,24.1,1,no,southeast,2201.1
27,male,32.7,0,no,southeast,2497.04
18,female,30.1,0,no,northeast,2203.47
19,female,29.8,0,no,southwest,1744.47
47,female,33.3,0,no,northeast,20878.78
54,male,25.1,3,yes,southwest,25382.3
61,male,28.3,1,yes,northwest,28868.66
24,male,28.5,0,yes,northeast,35147.53
25,male,35.6,0,no,northwest,2534.39
21,male,36.9,0,no,southeast,1534.3
23,male,32.6,0,no,southeast,1824.29
63,male,41.3,3,no,northwest,15555.19
49,male,37.5,2,no,southeast,9304.7
18,female,31.4,0,no,southeast,1622.19
51,female,39.5,1,no,southwest,9880.07
48,male,34.3,3,no,southwest,9563.03
31,female,31.1,0,no,northeast,4347.02
54,female,21.5,3,no,northwest,12475.35
19,male,28.7,0,no,southwest,1253.94
44,female,38.1,0,yes,southeast,48885.14
53,male,31.2,1,no,northwest,10461.98
19,female,32.9,0,no,southwest,1748.77
61,female,25.1,0,no,southeast,24513.09
18,female,25.1,0,no,northeast,2196.47
61,male,43.4,0,no,southwest,12574.05
21,male,25.7,4,yes,southwest,17942.11
20,male,27.9,0,no,northeast,1967.02
31,female,23.6,2,no,southwest,4931.65
45,male,28.7,2,no,southwest,8027.97
44,female,24.0,2,no,southeast,8211.1
62,female,39.2,0,no,southwest,13470.86
29,male,34.4,0,yes,southwest,36197.7
43,male,26.0,0,no,northeast,6837.37
51,male,23.2,1,yes,southeast,22218.11
19,male,30.3,0,yes,southeast,32548.34
38,female,28.9,1,no,southeast,5974.38
37,male,30.9,3,no,northwest,6796.86
22,male,31.4,1,no,northwest,2643.27
21,male,23.8,2,no,northwest,3077.1
24,female,25.3,0,no,northeast,3044.21
57,female,28.7,0,no,southwest,11455.28
56,male,32.1,1,no,northeast,11763
27,male,33.7,0,no,southeast,2498.41
51,male,22.4,0,no,northeast,9361.33
19,male,30.4,0,no,southwest,1256.3
39,male,28.3,1,yes,southwest,21082.16
58,male,35.7,0,no,southwest,11362.76
20,male,35.3,1,no,southeast,27724.29
45,male,30.5,2,no,northwest,8413.46
35,female,31.0,1,no,southwest,5240.77
31,male,30.9,0,no,northeast,3857.76
50,female,27.4,0,no,northeast,25656.58
32,female,44.2,0,no,southeast,3994.18
51,female,33.9,0,no,northeast,9866.3
38,female,37.7,0,no,southeast,5397.62
42,male,26.1,1,yes,southeast,38245.59
18,female,33.9,0,no,southeast,11482.63
19,female,30.6,2,no,northwest,24059.68
51,female,25.8,1,no,southwest,9861.03
46,male,39.4,1,no,northeast,8342.91
18,male,25.5,0,no,northeast,1708
57,male,42.1,1,yes,southeast,48675.52
62,female,31.7,0,no,northeast,14043.48
59,male,29.7,2,no,southeast,12925.89
37,male,36.2,0,no,southeast,19214.71
64,male,40.5,0,no,southeast,13831.12
38,male,28.0,1,no,northeast,6067.13
33,female,38.9,3,no,southwest,5972.38
46,female,30.2,2,no,southwest,8825.09
46,female,28.1,1,no,southeast,8233.1
53,male,31.4,0,no,southeast,27346.04
34,female,38.0,3,no,southwest,6196.45
20,female,31.8,2,no,southeast,3056.39
63,female,36.3,0,no,southeast,13887.2
54,female,47.4,0,yes,southeast,63770.43
54,male,30.2,0,no,northwest,10231.5
49,male,25.8,2,yes,northwest,23807.24
28,male,35.4,0,no,northeast,3268.85
54,female,46.7,2,no,southwest,11538.42
25,female,28.6,0,no,northeast,3213.62
43,female,46.2,0,yes,southeast,45863.21
63,male,30.8,0,no,southwest,13390.56
32,female,28.9,0,no,southeast,3972.92
62,male,21.4,0,no,southwest,12957.12
52,female,31.7,2,no,northwest,11187.66
25,female,41.3,0,no,northeast,17878.9
28,male,23.8,2,no,southwest,3847.67
46,male,33.4,1,no,northeast,8334.59
34,male,34.2,0,no,southeast,3935.18
35,female,34.1,3,yes,northwest,39983.43
19,male,35.5,0,no,northwest,1646.43
46,female,20.0,2,no,northwest,9193.84
54,female,32.7,0,no,northeast,10923.93
27,male,30.5,0,no,southwest,2494.02
50,male,44.8,1,no,southeast,9058.73
18,female,32.1,2,no,southeast,2801.26
19,female,30.5,0,no,northwest,2128.43
38,female,40.6,1,no,northwest,6373.56
41,male,30.6,2,no,northwest,7256.72
49,female,31.9,5,no,southwest,11552.9
48,male,40.6,2,yes,northwest,45702.02
31,female,29.1,0,no,southwest,3761.29
18,female,37.3,1,no,southeast,2219.45
30,female,43.1,2,no,southeast,4753.64
62,female,36.9,1,no,northeast,31620
57,female,34.3,2,no,northeast,13224.06
58,female,27.2,0,no,northwest,12222.9
22,male,26.8,0,no,southeast,1665
31,female,38.1,1,yes,northeast,58571.07
52,male,30.2,1,no,southwest,9724.53
25,female,23.5,0,no,northeast,3206.49
59,male,25.5,1,no,northeast,12913.99
19,male,30.6,0,no,northwest,1639.56
39,male,45.4,2,no,southeast,6356.27
32,female,23.7,1,no,southeast,17626.24
19,male,20.7,0,no,southwest,1242.82
33,female,28.3,1,no,southeast,4779.6
21,male,20.2,3,no,northeast,3861.21
34,female,30.2,1,yes,northwest,43943.88
61,female,35.9,0,no,northeast,13635.64
38,female,30.7,1,no,southeast,5976.83
58,female,29.0,0,no,southwest,11842.44
47,male,19.6,1,no,northwest,8428.07
20,male,31.1,2,no,southeast,2566.47
21,female,21.9,1,yes,northeast,15359.1
41,male,40.3,0,no,southeast,5709.16
46,female,33.7,1,no,northeast,8823.99
42,female,29.5,2,no,southeast,7640.31
34,female,33.3,1,no,northeast,5594.85
43,male,32.6,2,no,southwest,7441.5
52,female,37.5,2,no,northwest,33471.97
18,female,39.2,0,no,southeast,1633.04
51,male,31.6,0,no,northwest,9174.14
56,female,25.3,0,no,southwest,11070.54
64,female,39.1,3,no,southeast,16085.13
19,female,28.3,0,yes,northwest,17468.98
51,female,34.1,0,no,southeast,9283.56
27,female,25.2,0,no,northeast,3558.62
59,female,23.7,0,yes,northwest,25678.78
28,male,27.0,2,no,northeast,4435.09
30,male,37.8,2,yes,southwest,39241.44
47,female,29.4,1,no,southeast,8547.69
38,female,34.8,2,no,southwest,6571.54
18,female,33.2,0,no,northeast,2207.7
34,female,19.0,3,no,northeast,6753.04
20,female,33.0,0,no,southeast,1880.07
47,female,36.6,1,yes,southeast,42969.85
56,female,28.6,0,no,northeast,11658.12
49,male,25.6,2,yes,southwest,23306.55
19,female,33.1,0,yes,southeast,34439.86
55,female,37.1,0,no,southwest,10713.64
30,male,31.4,1,no,southwest,3659.35
37,male,34.1,4,yes,southwest,40182.25
49,female,21.3,1,no,southwest,9182.17
18,male,33.5,0,yes,northeast,34617.84
59,male,28.8,0,no,northwest,12129.61
29,female,26.0,0,no,northwest,3736.46
36,male,28.9,3,no,northeast,6748.59
33,male,42.5,1,no,southeast,11326.71
58,male,38.0,0,no,southwest,11365.95
44,female,39.0,0,yes,northwest,42983.46
53,male,36.1,1,no,southwest,10085.85
24,male,29.3,0,no,southwest,1977.82
29,female,35.5,0,no,southeast,3366.67
40,male,22.7,2,no,northeast,7173.36
51,male,39.7,1,no,southwest,9391.35
64,male,38.2,0,no,northeast,14410.93
19,female,24.5,1,no,northwest,2709.11
35,female,38.1,2,no,northeast,24915.05
39,male,26.4,0,yes,northeast,20149.32
56,male,33.7,4,no,southeast,12949.16
33,male,42.4,5,no,southwest,6666.24
42,male,28.3,3,yes,northwest,32787.46
61,male,33.9,0,no,northeast,13143.86
23,female,35.0,3,no,northwest,4466.62
43,male,35.3,2,no,southeast,18806.15
48,male,30.8,3,no,northeast,10141.14
39,male,26.2,1,no,northwest,6123.57
40,female,23.4,3,no,northeast,8252.28
18,male,28.5,0,no,northeast,1712.23
58,female,33.0,0,no,northeast,12430.95
49,female,42.7,2,no,southeast,9800.89
53,female,39.6,1,no,southeast,10579.71
48,female,31.1,0,no,southeast,8280.62
45,female,36.3,2,no,southeast,8527.53
59,female,35.2,0,no,southeast,12244.53
52,female,25.3,2,yes,southeast,24667.42
26,female,42.4,1,no,southwest,3410.32
27,male,33.2,2,no,northwest,4058.71
48,female,35.9,1,no,northeast,26392.26
57,female,28.8,4,no,northeast,14394.4
37,male,46.5,3,no,southeast,6435.62
57,female,24.0,1,no,southeast,22192.44
32,female,31.5,1,no,northeast,5148.55
18,male,33.7,0,no,southeast,1136.4
64,female,23.0,0,yes,southeast,27037.91
43,male,38.1,2,yes,southeast,42560.43
49,male,28.7,1,no,southwest,8703.46
40,female,32.8,2,yes,northwest,40003.33
62,male,32.0,0,yes,northeast,45710.21
40,female,29.8,1,no,southeast,6500.24
30,male,31.6,3,no,southeast,4837.58
29,female,31.2,0,no,northeast,3943.6
36,male,29.7,0,no,southeast,4399.73
41,female,31.0,0,no,southeast,6185.32
44,female,43.9,2,yes,southeast,46200.99
45,male,21.4,0,no,northwest,7222.79
55,female,40.8,3,no,southeast,12485.8
60,male,31.4,3,yes,northwest,46130.53
56,male,36.1,3,no,southwest,12363.55
49,female,23.2,2,no,northwest,10156.78
21,female,17.4,1,no,southwest,2585.27
19,male,20.3,0,no,southwest,1242.26
39,male,35.3,2,yes,southwest,40103.89
53,male,24.3,0,no,northwest,9863.47
33,female,18.5,1,no,southwest,4766.02
53,male,26.4,2,no,northeast,11244.38
42,male,26.1,2,no,northeast,7729.65
40,male,41.7,0,no,southeast,5438.75
47,female,24.1,1,no,southwest,26236.58
27,male,31.1,1,yes,southeast,34806.47
21,male,27.4,0,no,northeast,2104.11
47,male,36.2,1,no,southwest,8068.19
20,male,32.4,1,no,northwest,2362.23
24,male,23.7,0,no,northwest,2352.97
27,female,34.8,1,no,southwest,3578
26,female,40.2,0,no,northwest,3201.25
53,female,32.3,2,no,northeast,29186.48
41,male,35.8,1,yes,southeast,40273.65
56,male,33.7,0,no,northwest,10976.25
23,female,39.3,2,no,southeast,3500.61
21,female,34.9,0,no,southeast,2020.55
50,female,44.7,0,no,northeast,9541.7
53,male,41.5,0,no,southeast,9504.31
34,female,26.4,1,no,northwest,5385.34
47,female,29.5,1,no,northwest,8930.93
33,female,32.9,2,no,southwest,5375.04
51,female,38.1,0,yes,southeast,44400.41
49,male,28.7,3,no,northwest,10264.44
31,female,30.5,3,no,northeast,6113.23
36,female,27.7,0,no,northeast,5469.01
18,male,35.2,1,no,southeast,1727.54
50,female,23.5,2,no,southeast,10107.22
43,female,30.7,2,no,northwest,8310.84
20,male,40.5,0,no,northeast,1984.45
24,female,22.6,0,no,southwest,2457.5
60,male,28.9,0,no,southwest,12146.97
49,female,22.6,1,no,northwest,9566.99
60,male,24.3,1,no,northwest,13112.6
51,female,36.7,2,no,northwest,10848.13
58,female,33.4,0,no,northwest,12231.61
51,female,40.7,0,no,northeast,9875.68
53,male,36.6,3,no,southwest,11264.54
62,male,37.4,0,no,southwest,12979.36
19,male,35.4,0,no,southwest,1263.25
50,female,27.1,1,no,northeast,10106.13
30,female,39.1,3,yes,southeast,40932.43
41,male,28.4,1,no,northwest,6664.69
29,female,21.8,1,yes,northeast,16657.72
18,female,40.3,0,no,northeast,2217.6
41,female,36.1,1,no,southeast,6781.35
35,male,24.4,3,yes,southeast,19362
53,male,21.4,1,no,southwest,10065.41
24,female,30.1,3,no,southwest,4234.93
48,female,27.3,1,no,northeast,9447.25
59,female,32.1,3,no,southwest,14007.22
49,female,34.8,1,no,northwest,9583.89
37,female,38.4,0,yes,southeast,40419.02
26,male,23.7,2,no,southwest,3484.33
23,male,31.7,3,yes,northeast,36189.1
29,male,35.5,2,yes,southwest,44585.46
45,male,24.0,2,no,northeast,8604.48
27,male,29.2,0,yes,southeast,18246.5
53,male,34.1,0,yes,northeast,43254.42
31,female,26.6,0,no,southeast,3757.84
50,male,26.4,0,no,northwest,8827.21
50,female,30.1,1,no,northwest,9910.36
34,male,27.0,2,no,southwest,11737.85
19,male,21.8,0,no,northwest,1627.28
47,female,36.0,1,no,southwest,8556.91
28,male,30.9,0,no,northwest,3062.51
37,female,26.4,0,yes,southeast,19539.24
21,male,29.0,0,no,northwest,1906.36
64,male,37.9,0,no,northwest,14210.54
58,female,22.8,0,no,southeast,11833.78
24,male,33.6,4,no,northeast,17128.43
31,male,27.6,2,no,northeast,5031.27
39,female,22.8,3,no,northeast,7985.82
47,female,27.8,0,yes,southeast,23065.42
30,male,37.4,3,no,northeast,5428.73
18,male,38.2,0,yes,southeast,36307.8
22,female,34.6,2,no,northeast,3925.76
23,male,35.2,1,no,southwest,2416.96
33,male,27.1,1,yes,southwest,19040.88
27,male,26.0,0,no,northeast,3070.81
45,female,25.2,2,no,northeast,9095.07
57,female,31.8,0,no,northwest,11842.62
47,male,32.3,1,no,southwest,8062.76
42,female,29.0,1,no,southwest,7050.64
64,female,39.7,0,no,southwest,14319.03
38,female,19.5,2,no,northwest,6933.24
61,male,36.1,3,no,southwest,27941.29
53,female,26.7,2,no,southwest,11150.78
44,female,36.5,0,no,northeast,12797.21
19,female,28.9,0,yes,northwest,17748.51
41,male,34.2,2,no,northwest,7261.74
51,male,33.3,3,no,southeast,10560.49
40,male,32.3,2,no,northwest,6986.7
45,male,39.8,0,no,northeast,7448.4
35,male,34.3,3,no,southeast,5934.38
53,male,28.9,0,no,northwest,9869.81
30,male,24.4,3,yes,southwest,18259.22
18,male,41.1,0,no,southeast,1146.8
51,male,36.0,1,no,southeast,9386.16
50,female,27.6,1,yes,southwest,24520.26
31,female,29.3,1,no,southeast,4350.51
35,female,27.7,3,no,southwest,6414.18
60,male,37.0,0,no,northeast,12741.17
21,male,36.9,0,no,northwest,1917.32
29,male,22.5,3,no,northeast,5209.58
62,female,29.9,0,no,southeast,13457.96
39,female,41.8,0,no,southeast,5662.23
19,male,27.6,0,no,southwest,1252.41
22,female,23.2,0,no,northeast,2731.91
53,male,20.9,0,yes,southeast,21195.82
39,female,31.9,2,no,northwest,7209.49
27,male,28.5,0,yes,northwest,18310.74
30,male,44.2,2,no,southeast,4266.17
30,female,22.9,1,no,northeast,4719.52
58,female,33.1,0,no,southwest,11848.14
33,male,24.8,0,yes,northeast,17904.53
42,female,26.2,1,no,southeast,7046.72
64,female,36.0,0,no,southeast,14313.85
21,male,22.3,1,no,southwest,2103.08
18,female,42.2,0,yes,southeast,38792.69
23,male,26.5,0,no,southeast,1815.88
45,female,35.8,0,no,northwest,7731.86
40,female,41.4,1,no,northwest,28476.73
19,female,36.6,0,no,northwest,2136.88
18,male,30.1,0,no,southeast,1131.51
25,male,25.8,1,no,northeast,3309.79
46,female,30.8,3,no,southwest,9414.92
33,female,42.9,3,no,northwest,6360.99
54,male,21.0,2,no,southeast,11013.71
28,male,22.5,2,no,northeast,4428.89
36,male,34.4,2,no,southeast,5584.31
20,female,31.5,0,no,southeast,1877.93
24,female,24.2,0,no,northwest,2842.76
23,male,37.1,3,no,southwest,3597.6
47,female,26.1,1,yes,northeast,23401.31
33,female,35.5,0,yes,northwest,55135.4
45,male,33.7,1,no,southwest,7445.92
26,male,17.7,0,no,northwest,2680.95
18,female,31.1,0,no,southeast,1621.88
44,female,29.8,2,no,southeast,8219.2
60,male,24.3,0,no,northwest,12523.6
64,female,31.8,2,no,northeast,16069.08
56,male,31.8,2,yes,southeast,43813.87
36,male,28.0,1,yes,northeast,20773.63
41,male,30.8,3,yes,northeast,39597.41
39,male,21.9,1,no,northwest,6117.49
63,male,33.1,0,no,southwest,13393.76
36,female,25.8,0,no,northwest,5266.37
28,female,23.8,2,no,northwest,4719.74
58,male,34.4,0,no,northwest,11743.93
36,male,33.8,1,no,northwest,5377.46
42,male,36.0,2,no,southeast,7160.33
36,male,31.5,0,no,southwest,4402.23
56,female,28.3,0,no,northeast,11657.72
35,female,23.5,2,no,northeast,6402.29
59,female,31.4,0,no,northwest,12622.18
21,male,31.1,0,no,southwest,1526.31
59,male,24.7,0,no,northeast,12323.94
23,female,32.8,2,yes,southeast,36021.01
57,female,29.8,0,yes,southeast,27533.91
53,male,30.5,0,no,northeast,10072.06
60,female,32.5,0,yes,southeast,45008.96
51,female,34.2,1,no,southwest,9872.7
23,male,50.4,1,no,southeast,2438.06
27,female,24.1,0,no,southwest,2974.13
55,male,32.8,0,no,northwest,10601.63
37,female,30.8,0,yes,northeast,37270.15
61,male,32.3,2,no,northwest,14119.62
46,female,35.5,0,yes,northeast,42111.66
53,female,23.8,2,no,northeast,11729.68
49,female,23.8,3,yes,northeast,24106.91
20,female,29.6,0,no,southwest,1875.34
48,female,33.1,0,yes,southeast,40974.16
25,male,24.1,0,yes,northwest,15817.99
25,female,32.2,1,no,southeast,18218.16
57,male,28.1,0,no,southwest,10965.45
37,female,47.6,2,yes,southwest,46113.51
38,female,28.0,3,no,southwest,7151.09
55,female,33.5,2,no,northwest,12269.69
36,female,19.9,0,no,northeast,5458.05
51,male,25.4,0,no,southwest,8782.47
40,male,29.9,2,no,southwest,6600.36
18,male,37.3,0,no,southeast,1141.45
57,male,43.7,1,no,southwest,11576.13
61,male,23.7,0,no,northeast,13129.6
25,female,24.3,3,no,southwest,4391.65
50,male,36.2,0,no,southwest,8457.82
26,female,29.5,1,no,southeast,3392.37
42,male,24.9,0,no,southeast,5966.89
43,male,30.1,1,no,southwest,6849.03
44,male,21.9,3,no,northeast,8891.14
23,female,28.1,0,no,northwest,2690.11
49,female,27.1,1,no,southwest,26140.36
33,male,33.4,5,no,southeast,6653.79
41,male,28.8,1,no,southwest,6282.24
37,female,29.5,2,no,southwest,6311.95
22,male,34.8,3,no,southwest,3443.06
23,male,27.4,1,no,northwest,2789.06
21,female,22.1,0,no,northeast,2585.85
51,female,37.1,3,yes,northeast,46255.11
25,male,26.7,4,no,northwest,4877.98
32,male,28.9,1,yes,southeast,19719.69
57,male,29.0,0,yes,northeast,27218.44
36,female,30.0,0,no,northwest,5272.18
22,male,39.5,0,no,southwest,1682.6
57,male,33.6,1,no,northwest,11945.13
64,female,26.9,0,yes,northwest,29330.98
36,female,29.0,4,no,southeast,7243.81
54,male,24.0,0,no,northeast,10422.92
47,male,38.9,2,yes,southeast,44202.65
62,male,32.1,0,no,northeast,13555
61,female,44.0,0,no,southwest,13063.88
43,female,20.0,2,yes,northeast,19798.05
19,male,25.6,1,no,northwest,2221.56
18,female,40.3,0,no,southeast,1634.57
19,female,22.5,0,no,northwest,2117.34
49,male,22.5,0,no,northeast,8688.86
60,male,40.9,0,yes,southeast,48673.56
26,male,27.3,3,no,northeast,4661.29
49,male,36.9,0,no,southeast,8125.78
60,female,35.1,0,no,southwest,12644.59
26,female,29.4,2,no,northeast,4564.19
27,male,32.6,3,no,northeast,4846.92
44,female,32.3,1,no,southeast,7633.72
63,male,39.8,3,no,southwest,15170.07
32,female,24.6,0,yes,southwest,17496.31
22,male,28.3,1,no,northwest,2639.04
18,male,31.7,0,yes,northeast,33732.69
59,female,26.7,3,no,northwest,14382.71
44,female,27.5,1,no,southwest,7626.99
33,male,24.6,2,no,northwest,5257.51
24,female,34.0,0,no,southeast,2473.33
43,female,26.9,0,yes,northwest,21774.32
45,male,22.9,0,yes,northeast,35069.37
61,female,28.2,0,no,southwest,13041.92
35,female,34.2,1,no,southeast,5245.23
62,female,25.0,0,no,southwest,13451.12
62,female,33.2,0,no,southwest,13462.52
38,male,31.0,1,no,southwest,5488.26
34,male,35.8,0,no,northwest,4320.41
43,male,23.2,0,no,southwest,6250.44
50,male,32.1,2,no,northeast,25333.33
19,female,23.4,2,no,southwest,2913.57
57,female,20.1,1,no,southwest,12032.33
62,female,39.2,0,no,southeast,13470.8
41,male,34.2,1,no,southeast,6289.75
26,male,46.5,1,no,southeast,2927.06
39,female,32.5,1,no,southwest,6238.3
46,male,25.8,5,no,southwest,10096.97
45,female,35.3,0,no,southwest,7348.14
32,male,37.2,2,no,southeast,4673.39
59,female,27.5,0,no,southwest,12233.83
44,male,29.7,2,no,northeast,32108.66
39,female,24.2,5,no,northwest,8965.8
18,male,26.2,2,no,southeast,2304
53,male,29.5,0,no,southeast,9487.64
18,male,23.2,0,no,southeast,1121.87
50,female,46.1,1,no,southeast,9549.57
18,female,40.2,0,no,northeast,2217.47
19,male,22.6,0,no,northwest,1628.47
62,male,39.9,0,no,southeast,12982.87
56,female,35.8,1,no,southwest,11674.13
42,male,35.8,2,no,southwest,7160.09
37,male,34.2,1,yes,northeast,39047.29
42,male,31.3,0,no,northwest,6358.78
25,male,29.7,3,yes,southwest,19933.46
57,male,18.3,0,no,northeast,11534.87
51,male,42.9,2,yes,southeast,47462.89
30,female,28.4,1,no,northwest,4527.18
44,male,30.2,2,yes,southwest,38998.55
34,male,27.8,1,yes,northwest,20009.63
31,male,39.5,1,no,southeast,3875.73
54,male,30.8,1,yes,southeast,41999.52
24,male,26.8,1,no,northwest,12609.89
43,male,35.0,1,yes,northeast,41034.22
48,male,36.7,1,no,northwest,28468.92
19,female,39.6,1,no,northwest,2730.11
29,female,25.9,0,no,southwest,3353.28
63,female,35.2,1,no,southeast,14474.68
46,male,24.8,3,no,northeast,9500.57
52,male,36.8,2,no,northwest,26467.1
35,male,27.1,1,no,southwest,4746.34
51,male,24.8,2,yes,northwest,23967.38
44,male,25.4,1,no,northwest,7518.03
21,male,25.7,2,no,northeast,3279.87
39,female,34.3,5,no,southeast,8596.83
50,female,28.2,3,no,southeast,10702.64
34,female,23.6,0,no,northeast,4992.38
22,female,20.2,0,no,northwest,2527.82
19,female,40.5,0,no,southwest,1759.34
26,male,35.4,0,no,southeast,2322.62
29,male,22.9,0,yes,northeast,16138.76
48,male,40.2,0,no,southeast,7804.16
26,male,29.2,1,no,southeast,2902.91
45,female,40.0,3,no,northeast,9704.67
36,female,29.9,0,no,southeast,4889.04
54,male,25.5,1,no,northeast,25517.11
34,male,21.4,0,no,northeast,4500.34
31,male,25.9,3,yes,southwest,19199.94
27,female,30.6,1,no,northeast,16796.41
20,male,30.1,5,no,northeast,4915.06
44,female,25.8,1,no,southwest,7624.63
43,male,30.1,3,no,northwest,8410.05
45,female,27.6,1,no,northwest,28340.19
34,male,34.7,0,no,northeast,4518.83
24,female,20.5,0,yes,northeast,14571.89
26,female,19.8,1,no,southwest,3378.91
38,female,27.8,2,no,northeast,7144.86
50,female,31.6,2,no,southwest,10118.42
38,male,28.3,1,no,southeast,5484.47
27,female,20.0,3,yes,northwest,16420.49
39,female,23.3,3,no,northeast,7986.48
39,female,34.1,3,no,southwest,7418.52
63,female,36.9,0,no,southeast,13887.97
33,female,36.3,3,no,northeast,6551.75
36,female,26.9,0,no,northwest,5267.82
30,male,23.0,2,yes,northwest,17361.77
24,male,32.7,0,yes,southwest,34472.84
24,male,25.8,0,no,southwest,1972.95
48,male,29.6,0,no,southwest,21232.18
47,male,19.2,1,no,northeast,8627.54
29,male,31.7,2,no,northwest,4433.39
28,male,29.3,2,no,northeast,4438.26
47,male,28.2,3,yes,northwest,24915.22
25,male,25.0,2,no,northeast,23241.47
51,male,27.7,1,no,northeast,9957.72
48,female,22.8,0,no,southwest,8269.04
43,male,20.1,2,yes,southeast,18767.74
61,female,33.3,4,no,southeast,36580.28
48,male,32.3,1,no,northwest,8765.25
38,female,27.6,0,no,southwest,5383.54
59,male,25.5,0,no,northwest,12124.99
19,female,24.6,1,no,northwest,2709.24
26,female,34.2,2,no,southwest,3987.93
54,female,35.8,3,no,northwest,12495.29
21,female,32.7,2,no,northwest,26018.95
51,male,37.0,0,no,southwest,8798.59
22,female,31.0,3,yes,southeast,35595.59
47,male,36.1,1,yes,southeast,42211.14
18,male,23.3,1,no,southeast,1711.03
47,female,45.3,1,no,southeast,8569.86
21,female,34.6,0,no,southwest,2020.18
19,male,26.0,1,yes,northwest,16450.89
23,male,18.7,0,no,northwest,21595.38
54,male,31.6,0,no,southwest,9850.43
37,female,17.3,2,no,northeast,6877.98
46,female,23.7,1,yes,northwest,21677.28
55,female,35.2,0,yes,southeast,44423.8
30,female,27.9,0,no,northeast,4137.52
18,male,21.6,0,yes,northeast,13747.87
61,male,38.4,0,no,northwest,12950.07
54,female,23.0,3,no,southwest,12094.48
22,male,37.1,2,yes,southeast,37484.45
45,female,30.5,1,yes,northwest,39725.52
22,male,28.9,0,no,northeast,2250.84
19,male,27.3,2,no,northwest,22493.66
35,female,28.0,0,yes,northwest,20234.85
18,male,23.1,0,no,northeast,1704.7
20,male,30.7,0,yes,northeast,33475.82
28,female,25.8,0,no,southwest,3161.45
55,male,35.2,1,no,northeast,11394.07
43,female,24.7,2,yes,northwest,21880.82
43,female,25.1,0,no,northeast,7325.05
22,male,52.6,1,yes,southeast,44501.4
25,female,22.5,1,no,northwest,3594.17
49,male,30.9,0,yes,southwest,39727.61
44,female,37.0,1,no,northwest,8023.14
64,male,26.4,0,no,northeast,14394.56
49,male,29.8,1,no,northeast,9288.03
47,male,29.8,3,yes,southwest,25309.49
27,female,21.5,0,no,northwest,3353.47
55,male,27.6,0,no,northwest,10594.5
48,female,28.9,0,no,southwest,8277.52
45,female,31.8,0,no,southeast,17929.3
24,female,39.5,0,no,southeast,2480.98
32,male,33.8,1,no,northwest,4462.72
24,male,32.0,0,no,southeast,1981.58
57,male,27.9,1,no,southeast,11554.22
59,male,41.1,1,yes,southeast,48970.25
36,male,28.6,3,no,northwest,6548.2
29,female,25.6,4,no,southwest,5708.87
42,female,25.3,1,no,southwest,7045.5
48,male,37.3,2,no,southeast,8978.19
39,male,42.7,0,no,northeast,5757.41
63,male,21.7,1,no,northwest,14349.85
54,female,31.9,1,no,southeast,10928.85
37,male,37.1,1,yes,southeast,39871.7
63,male,31.4,0,no,northeast,13974.46
21,male,31.3,0,no,northwest,1909.53
54,female,28.9,2,no,northeast,12096.65
60,female,18.3,0,no,northeast,13204.29
32,female,29.6,1,no,southeast,4562.84
47,female,32.0,1,no,southwest,8551.35
21,male,26.0,0,no,northeast,2102.26
28,male,31.7,0,yes,southeast,34672.15
63,male,33.7,3,no,southeast,15161.53
18,male,21.8,2,no,southeast,11884.05
32,male,27.8,1,no,northwest,4454.4
38,male,20.0,1,no,northwest,5855.9
32,male,31.5,1,no,southwest,4076.5
62,female,30.5,2,no,northwest,15019.76
39,female,18.3,5,yes,southwest,19023.26
55,male,29.0,0,no,northeast,10796.35
57,male,31.5,0,no,northwest,11353.23
52,male,47.7,1,no,southeast,9748.91
56,male,22.1,0,no,southwest,10577.09
47,male,36.2,0,yes,southeast,41676.08
55,female,29.8,0,no,northeast,11286.54
23,male,32.7,3,no,southwest,3591.48
22,female,30.4,0,yes,northwest,33907.55
50,female,33.7,4,no,southwest,11299.34
18,female,31.4,4,no,northeast,4561.19
51,female,35.0,2,yes,northeast,44641.2
22,male,33.8,0,no,southeast,1674.63
52,female,30.9,0,no,northeast,23045.57
25,female,34.0,1,no,southeast,3227.12
33,female,19.1,2,yes,northeast,16776.3
53,male,28.6,3,no,southwest,11253.42
29,male,38.9,1,no,southeast,3471.41
58,male,36.1,0,no,southeast,11363.28
37,male,29.8,0,no,southwest,20420.6
54,female,31.2,0,no,southeast,10338.93
49,female,29.9,0,no,northwest,8988.16
50,female,26.2,2,no,northwest,10493.95
26,male,30.0,1,no,southwest,2904.09
45,male,20.4,3,no,southeast,8605.36
54,female,32.3,1,no,northeast,11512.41
38,male,38.4,3,yes,southeast,41949.24
48,female,25.9,3,yes,southeast,24180.93
28,female,26.3,3,no,northwest,5312.17
23,male,24.5,0,no,northeast,2396.1
55,male,32.7,1,no,southeast,10807.49
41,male,29.6,5,no,northeast,9222.4
25,male,33.3,2,yes,southeast,36124.57
33,male,35.8,1,yes,southeast,38282.75
30,female,20.0,3,no,northwest,5693.43
23,female,31.4,0,yes,southwest,34166.27
46,male,38.2,2,no,southeast,8347.16
53,female,36.9,3,yes,northwest,46661.44
27,female,32.4,1,no,northeast,18903.49
23,female,42.8,1,yes,northeast,40904.2
63,female,25.1,0,no,northwest,14254.61
55,male,29.9,0,no,southwest,10214.64
35,female,35.9,2,no,southeast,5836.52
34,male,32.8,1,no,southwest,14358.36
19,female,18.6,0,no,southwest,1728.9
39,female,23.9,5,no,southeast,8582.3
27,male,45.9,2,no,southwest,3693.43
57,male,40.3,0,no,northeast,20709.02
52,female,18.3,0,no,northwest,9991.04
28,male,33.8,0,no,northwest,19673.34
50,female,28.1,3,no,northwest,11085.59
44,female,25.0,1,no,southwest,7623.52
26,female,22.2,0,no,northwest,3176.29
33,male,30.3,0,no,southeast,3704.35
19,female,32.5,0,yes,northwest,36898.73
50,male,37.1,1,no,southeast,9048.03
41,female,32.6,3,no,southwest,7954.52
52,female,24.9,0,no,southeast,27117.99
39,male,32.3,2,no,southeast,6338.08
50,male,32.3,2,no,southwest,9630.4
52,male,32.8,3,no,northwest,11289.11
60,male,32.8,0,yes,southwest,52590.83
20,female,31.9,0,no,northwest,2261.57
55,male,21.5,1,no,southwest,10791.96
42,male,34.1,0,no,southwest,5979.73
18,female,30.3,0,no,northeast,2203.74
58,female,36.5,0,no,northwest,12235.84
43,female,32.6,3,yes,southeast,40941.29
35,female,35.8,1,no,northwest,5630.46
48,female,27.9,4,no,northwest,11015.17
36,female,22.1,3,no,northeast,7228.22
19,male,44.9,0,yes,southeast,39722.75
23,female,23.2,2,no,northwest,14426.07
20,female,30.6,0,no,northeast,2459.72
32,female,41.1,0,no,southwest,3989.84
43,female,34.6,1,no,northwest,7727.25
34,male,42.1,2,no,southeast,5124.19
30,male,38.8,1,no,southeast,18963.17
18,female,28.2,0,no,northeast,2200.83
41,female,28.3,1,no,northwest,7153.55
35,female,26.1,0,no,northeast,5227.99
57,male,40.4,0,no,southeast,10982.5
29,female,24.6,2,no,southwest,4529.48
32,male,35.2,2,no,southwest,4670.64
37,female,34.1,1,no,northwest,6112.35
18,male,27.4,1,yes,northeast,17178.68
43,female,26.7,2,yes,southwest,22478.6
56,female,41.9,0,no,southeast,11093.62
38,male,29.3,2,no,northwest,6457.84
29,male,32.1,2,no,northwest,4433.92
22,female,27.1,0,no,southwest,2154.36
52,female,24.1,1,yes,northwest,23887.66
40,female,27.4,1,no,southwest,6496.89
23,female,34.9,0,no,northeast,2899.49
31,male,29.8,0,yes,southeast,19350.37
42,female,41.3,1,no,northeast,7650.77
24,female,29.9,0,no,northwest,2850.68
25,female,30.3,0,no,southwest,2632.99
48,female,27.4,1,no,northeast,9447.38
23,female,28.5,1,yes,southeast,18328.24
45,male,23.6,2,no,northeast,8603.82
20,male,35.6,3,yes,northwest,37465.34
62,female,32.7,0,no,northwest,13844.8
43,female,25.3,1,yes,northeast,21771.34
23,female,28.0,0,no,southwest,13126.68
31,female,32.8,2,no,northwest,5327.4
41,female,21.8,1,no,northeast,13725.47
58,female,32.4,1,no,northeast,13019.16
48,female,36.6,0,no,northwest,8671.19
31,female,21.8,0,no,northwest,4134.08
19,female,27.9,3,no,northwest,18838.7
19,female,30.0,0,yes,northwest,33307.55
41,male,33.6,0,no,southeast,5699.84
40,male,29.4,1,no,northwest,6393.6
31,female,25.8,2,no,southwest,4934.71
37,male,24.3,2,no,northwest,6198.75
46,male,40.4,2,no,northwest,8733.23
22,male,32.1,0,no,northwest,2055.32
51,male,32.3,1,no,northeast,9964.06
18,female,27.3,3,yes,southeast,18223.45
35,male,17.9,1,no,northwest,5116.5
59,female,34.8,2,no,southwest,36910.61
36,male,33.4,2,yes,southwest,38415.47
37,female,25.6,1,yes,northeast,20296.86
59,male,37.1,1,no,southwest,12347.17
36,male,30.9,1,no,northwest,5373.36
39,male,34.1,2,no,southeast,23563.02
18,male,21.5,0,no,northeast,1702.46
52,female,33.3,2,no,southwest,10806.84
27,female,31.3,1,no,northwest,3956.07
18,male,39.1,0,no,northeast,12890.06
40,male,25.1,0,no,southeast,5415.66
29,male,37.3,2,no,southeast,4058.12
46,female,34.6,1,yes,southwest,41661.6
38,female,30.2,3,no,northwest,7537.16
30,female,21.9,1,no,northeast,4718.2
40,male,25.0,2,no,southeast,6593.51
50,male,25.3,0,no,southeast,8442.67
20,female,24.4,0,yes,southeast,26125.67
41,male,23.9,1,no,northeast,6858.48
33,female,39.8,1,no,southeast,4795.66
38,male,16.8,2,no,northeast,6640.54
42,male,37.2,2,no,southeast,7162.01
56,male,34.4,0,no,southeast,10594.23
58,male,30.3,0,no,northeast,11938.26
52,male,34.5,3,yes,northwest,60021.4
20,female,21.8,0,yes,southwest,20167.34
54,female,24.6,3,no,northwest,12479.71
58,male,23.3,0,no,southwest,11345.52
45,female,27.8,2,no,southeast,8515.76
26,male,31.1,0,no,northwest,2699.57
63,female,21.7,0,no,northeast,14449.85
58,female,28.2,0,no,northwest,12224.35
37,male,22.7,3,no,northeast,6985.51
25,female,42.1,1,no,southeast,3238.44
52,male,41.8,2,yes,southeast,47269.85
64,male,37.0,2,yes,southeast,49577.66
22,female,21.3,3,no,northwest,4296.27
28,female,33.1,0,no,southeast,3171.61
18,male,33.3,0,no,southeast,1135.94
28,male,24.3,5,no,southwest,5615.37
45,female,25.7,3,no,southwest,9101.8
33,male,29.4,4,no,southwest,6059.17
18,female,39.8,0,no,southeast,1633.96
32,male,33.6,1,yes,northeast,37607.53
24,male,29.8,0,yes,northeast,18648.42
19,male,19.8,0,no,southwest,1241.57
20,male,27.3,0,yes,southwest,16232.85
40,female,29.3,4,no,southwest,15828.82
34,female,27.7,0,no,southeast,4415.16
42,female,37.9,0,no,southwest,6474.01
51,female,36.4,3,no,northwest,11436.74
54,female,27.6,1,no,northwest,11305.93
55,male,37.7,3,no,northwest,30063.58
52,female,23.2,0,no,northeast,10197.77
32,female,20.5,0,no,northeast,4544.23
28,male,37.1,1,no,southwest,3277.16
41,female,28.1,1,no,southeast,6770.19
43,female,29.9,1,no,southwest,7337.75
49,female,33.3,2,no,northeast,10370.91
64,male,23.8,0,yes,southeast,26926.51
55,female,30.5,0,no,southwest,10704.47
24,male,31.1,0,yes,northeast,34254.05
20,female,33.3,0,no,southwest,1880.49
45,male,27.5,3,no,southwest,8615.3
26,male,33.9,1,no,northwest,3292.53
25,female,34.5,0,no,northwest,3021.81
43,male,25.5,5,no,southeast,14478.33
35,male,27.6,1,no,southeast,4747.05
26,male,27.1,0,yes,southeast,17043.34
57,male,23.7,0,no,southwest,10959.33
22,female,30.4,0,no,northeast,2741.95
32,female,29.7,0,no,northwest,4357.04
39,male,29.9,1,yes,northeast,22462.04
25,female,26.8,2,no,northwest,4189.11
48,female,33.3,0,no,southeast,8283.68
47,female,27.6,2,yes,northwest,24535.7
18,female,21.7,0,yes,northeast,14283.46
18,male,30.0,1,no,southeast,1720.35
61,male,36.3,1,yes,southwest,47403.88
47,female,24.3,0,no,northeast,8534.67
28,female,17.3,0,no,northeast,3732.63
36,female,25.9,1,no,southwest,5472.45
20,male,39.4,2,yes,southwest,38344.57
44,male,34.3,1,no,southeast,7147.47
38,female,20.0,2,no,northeast,7133.9
19,male,34.9,0,yes,southwest,34828.65
21,male,23.2,0,no,southeast,1515.34
46,male,25.7,3,no,northwest,9301.89
58,male,25.2,0,no,northeast,11931.13
20,male,22.0,1,no,southwest,1964.78
18,male,26.1,0,no,northeast,1708.93
28,female,26.5,2,no,southeast,4340.44
33,male,27.5,2,no,northwest,5261.47
19,female,25.7,1,no,northwest,2710.83
45,male,30.4,0,yes,southeast,62592.87
62,male,30.9,3,yes,northwest,46718.16
25,female,20.8,1,no,southwest,3208.79
43,male,27.8,0,yes,southwest,37829.72
42,male,24.6,2,yes,northeast,21259.38
24,female,27.7,0,no,southeast,2464.62
29,female,21.9,0,yes,northeast,16115.3
32,male,28.1,4,yes,northwest,21472.48
25,female,30.2,0,yes,southwest,33900.65
41,male,32.2,2,no,southwest,6875.96
42,male,26.3,1,no,northwest,6940.91
33,female,26.7,0,no,northwest,4571.41
34,male,42.9,1,no,southwest,4536.26
19,female,34.7,2,yes,southwest,36397.58
30,female,23.7,3,yes,northwest,18765.88
18,male,28.3,1,no,northeast,11272.33
19,female,20.6,0,no,southwest,1731.68
18,male,53.1,0,no,southeast,1163.46
35,male,39.7,4,no,northeast,19496.72
39,female,26.3,2,no,northwest,7201.7
31,male,31.1,3,no,northwest,5425.02
62,male,26.7,0,yes,northeast,28101.33
62,male,38.8,0,no,southeast,12981.35
42,female,40.4,2,yes,southeast,43896.38
31,male,25.9,1,no,northwest,4239.89
61,male,33.5,0,no,northeast,13143.34
42,female,32.9,0,no,northeast,7050.02
51,male,30.0,1,no,southeast,9377.9
23,female,24.2,2,no,northeast,22395.74
52,male,38.6,2,no,southwest,10325.21
57,female,25.7,2,no,southeast,12629.17
23,female,33.4,0,no,southwest,10795.94
52,female,44.7,3,no,southwest,11411.69
50,male,31.0,3,no,northwest,10600.55
18,female,31.9,0,no,northeast,2205.98
18,female,36.9,0,no,southeast,1629.83
21,female,25.8,0,no,southwest,2007.95
61,female,29.1,0,yes,northwest,29141.36
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/examples/insurance_prediction/test.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 12,
6 | "metadata": {
7 | "collapsed": false,
8 | "deletable": true,
9 | "editable": true
10 | },
11 | "outputs": [
12 | {
13 | "name": "stdout",
14 | "output_type": "stream",
15 | "text": [
16 | "Populating the interactive namespace from numpy and matplotlib\n"
17 | ]
18 | }
19 | ],
20 | "source": [
21 | "%pylab inline\n",
22 | "import pandas as pd\n",
23 | "from sklearn.model_selection import train_test_split\n",
24 | "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
25 | "from sklearn.metrics import r2_score\n",
26 | "\n",
27 | "from deep_learning.layers import Layer, InputLayer\n",
28 | "from deep_learning.models import Model\n",
29 | "\n",
30 | "\n",
31 | "import matplotlib.pyplot as plt\n",
32 | "\n",
33 | "\n",
34 | "raw_data = pd.read_csv('examples/insurance_prediction/insurance.csv')\n",
35 | "\n",
36 | "# Preprocessing\n",
37 | "bmi_scaler = MinMaxScaler()\n",
38 | "raw_data[['bmi']] = bmi_scaler.fit_transform(raw_data[['bmi']].values)\n",
39 | "raw_data['sex'] = raw_data['sex'].apply({'male': 1, 'female': 0}.get)\n",
40 | "raw_data['smoker'] = raw_data['smoker'].apply({'yes': 1, 'no': 0}.get)\n",
41 | "\n",
42 | "# Region\n",
43 | "onehot_smokers = pd.get_dummies(raw_data['region'], prefix='region')\n",
44 | "raw_data = pd.concat([raw_data, onehot_smokers], axis=1)\n",
45 | "# raw_data = raw_data.drop('region', axis=1)\n",
46 | "\n",
47 | "# Y Labels\n",
48 | "expense_scaler = StandardScaler()\n",
49 | "raw_data['expenses'] = expense_scaler.fit_transform(raw_data[['expenses']].values)\n",
50 | "data_y = raw_data.as_matrix(['expenses'])\n",
51 | "\n",
52 | "# X Labels\n",
53 | "data_x = raw_data.drop(['expenses', 'region'], axis=1)\n",
54 | "data_x = data_x.as_matrix()\n",
55 | "data_scaler = StandardScaler()\n",
56 | "data_x = data_scaler.fit_transform(data_x)\n",
57 | "\n",
58 | "# Split data into train and examples subset\n",
59 | "train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.3)\n",
60 | "\n",
61 | "# print('[TRAIN]')\n",
62 | "# print('train_x:', train_x.shape)\n",
63 | "# print('train_y:', train_y.shape)\n",
64 | "# print('\\n[TEST]')\n",
65 | "# print('test_x:', test_x.shape)\n",
66 | "# print('test_y:', test_y.shape)"
67 | ]
68 | },
69 | {
70 | "cell_type": "code",
71 | "execution_count": 2,
72 | "metadata": {
73 | "collapsed": false
74 | },
75 | "outputs": [
76 | {
77 | "data": {
78 | "text/html": [
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