path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
105195570/cell_21 | [
"image_output_1.png"
] | from scipy.stats import boxcox
from sklearn import preprocessing
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.rename(columns={'Pop': 'pop'})
list_of_cat_unordered_features = data[['site', 'pop', 'sex']]
for feature in list_of_cat_unordered_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.countplot(x = data[feature], data=data, order = data[feature].value_counts().index)
for container in ax.containers:
ax.bar_label(container)
plt.show()
list_of_num_features = data[['age','hdlngth', 'skullw', 'totlngth','taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']]
for feature in list_of_num_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
for container in ax.containers:
ax.bar_label(container)
plt.show()
#normalisation of skull width attribute
data['skullw'] = boxcox(data['skullw'])[0]
plt.figure(figsize=(12,6.5))
plt.title('Skull size', fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
corr = data.corr()
cat_columns = [feature for feature in data.columns if data[feature].dtype == 'object']
encoder = preprocessing.LabelEncoder()
for col in cat_columns:
data[col] = encoder.fit_transform(data[col])
data.head() | code |
105195570/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns | code |
105195570/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum() | code |
105195570/cell_19 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from scipy.stats import boxcox
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.rename(columns={'Pop': 'pop'})
list_of_cat_unordered_features = data[['site', 'pop', 'sex']]
for feature in list_of_cat_unordered_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.countplot(x = data[feature], data=data, order = data[feature].value_counts().index)
for container in ax.containers:
ax.bar_label(container)
plt.show()
list_of_num_features = data[['age','hdlngth', 'skullw', 'totlngth','taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']]
for feature in list_of_num_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
for container in ax.containers:
ax.bar_label(container)
plt.show()
#normalisation of skull width attribute
data['skullw'] = boxcox(data['skullw'])[0]
plt.figure(figsize=(12,6.5))
plt.title('Skull size', fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
corr = data.corr()
plt.figure(figsize=(20, 12))
sns.heatmap(corr, linewidths=4, annot=True, fmt='.2f', cmap='BrBG')
plt.show() | code |
105195570/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.head() | code |
105195570/cell_18 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy.stats import boxcox
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.rename(columns={'Pop': 'pop'})
list_of_cat_unordered_features = data[['site', 'pop', 'sex']]
for feature in list_of_cat_unordered_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.countplot(x = data[feature], data=data, order = data[feature].value_counts().index)
for container in ax.containers:
ax.bar_label(container)
plt.show()
list_of_num_features = data[['age','hdlngth', 'skullw', 'totlngth','taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']]
for feature in list_of_num_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
for container in ax.containers:
ax.bar_label(container)
plt.show()
data['skullw'] = boxcox(data['skullw'])[0]
plt.figure(figsize=(12, 6.5))
plt.title('Skull size', fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data) | code |
105195570/cell_8 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape | code |
105195570/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.rename(columns={'Pop': 'pop'})
list_of_cat_unordered_features = data[['site', 'pop', 'sex']]
for feature in list_of_cat_unordered_features:
plt.figure(figsize=(12, 6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.countplot(x=data[feature], data=data, order=data[feature].value_counts().index)
for container in ax.containers:
ax.bar_label(container)
plt.show() | code |
105195570/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.columns
data.replace(to_replace={'Vic': 'Victoria', 'm': 'male', 'f': 'female'}, inplace=True)
data = data.rename(columns={'Pop': 'pop'})
list_of_cat_unordered_features = data[['site', 'pop', 'sex']]
for feature in list_of_cat_unordered_features:
plt.figure(figsize=(12,6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.countplot(x = data[feature], data=data, order = data[feature].value_counts().index)
for container in ax.containers:
ax.bar_label(container)
plt.show()
list_of_num_features = data[['age', 'hdlngth', 'skullw', 'totlngth', 'taill', 'footlgth', 'earconch', 'eye', 'chest', 'belly']]
for feature in list_of_num_features:
plt.figure(figsize=(12, 6.5))
plt.title(feature, fontsize=15, fontweight='bold', fontname='Helvetica', ha='center')
ax = sns.boxplot(y=data[feature], data=data)
for container in ax.containers:
ax.bar_label(container)
plt.show() | code |
105195570/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.describe() | code |
105195570/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/openintro-possum/possum.csv')
data.shape
data.isna().sum()
data.dropna(axis=0, inplace=True)
data.info() | code |
129021961/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
X.iloc[0].shape
imag = X.iloc[9]
import numpy as np
imagenum = np.array(imag)
finalimage = imagenum.reshape(28, 28)
final_X = X / 255
final_y = to_categorical(y, num_classes=26)
final_y.shape
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(final_X, final_y, epochs=10, batch_size=32)
import pickle
pickle.dump(model, open('finalalpha.pkl', 'wb'))
pickled_model = pickle.load(open('finalalpha.pkl', 'rb')) | code |
129021961/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
final_y = to_categorical(y, num_classes=26)
final_y | code |
129021961/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
X.iloc[0].shape
imag = X.iloc[9]
import numpy as np
imagenum = np.array(imag)
finalimage = imagenum.reshape(28, 28)
import matplotlib.pyplot as plt
plt.imshow(finalimage) | code |
129021961/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape | code |
129021961/cell_20 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
X.iloc[0].shape
imag = X.iloc[9]
import numpy as np
imagenum = np.array(imag)
finalimage = imagenum.reshape(28, 28)
final_X = X / 255
final_y = to_categorical(y, num_classes=26)
final_y.shape
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(final_X, final_y, epochs=10, batch_size=32)
import pickle
pickle.dump(model, open('finalalpha.pkl', 'wb')) | code |
129021961/cell_11 | [
"text_html_output_1.png"
] | from tensorflow.keras.utils import to_categorical | code |
129021961/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
X.iloc[0].shape
imag = X.iloc[9]
import numpy as np
imagenum = np.array(imag)
finalimage = imagenum.reshape(28, 28)
final_X = X / 255
final_y = to_categorical(y, num_classes=26)
final_y.shape
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(final_X, final_y, epochs=10, batch_size=32) | code |
129021961/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129021961/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
X.iloc[0].shape | code |
129021961/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.head(5) | code |
129021961/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary() | code |
129021961/cell_24 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
import cv2
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
X.iloc[0].shape
imag = X.iloc[9]
import numpy as np
imagenum = np.array(imag)
finalimage = imagenum.reshape(28, 28)
final_X = X / 255
final_y = to_categorical(y, num_classes=26)
final_y.shape
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(26, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(final_X, final_y, epochs=10, batch_size=32)
import pickle
pickle.dump(model, open('finalalpha.pkl', 'wb'))
pickled_model = pickle.load(open('finalalpha.pkl', 'rb'))
def get(A):
A = cv2.cvtColor(A, cv2.COLOR_BGR2GRAY)
A = cv2.resize(A, (28, 28))
A = A.reshape(1, 784) / 255
return pickled_model.predict(A).argmax()
get(cv2.imread('/kaggle/input/imgfileb/imbB.png')) | code |
129021961/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/az-handwritten-alphabets-in-csv-format/A_Z Handwritten Data.csv')
df.shape
X = df.drop(['0'], axis=1)
y = df['0']
final_y = to_categorical(y, num_classes=26)
final_y.shape | code |
105173227/cell_21 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
l1 = []
for i in range(1, 6):
l1.append(i ** 2)
l2 = [i ** 2 for i in range(1, 6)]
l3 = []
for i in range(1, 11):
if i % 2 == 0:
l3.append(i)
l4 = [i for i in range(1, 11) if i % 2 == 0]
s1 = {i for i in range(1, 11) if i % 2 == 0}
s2 = {i ** 2 for i in range(1, 6)}
l1 = [1, 4, 65, 24, 83, 43, 21]
l1.sort()
def s(x):
return x[1]
def s1(x):
return x[0] + x[1]
l2 = [[1, 2], [3, 4], [2, 6], [7, 5]]
l2.sort()
l2.sort(key=s)
l2.sort(key=s1)
l3 = [[1, 2], [3, 4], [2, 6], [7, 5]]
l3.sort(key=lambda x: x[0] + x[1])
def check(x):
return 'even' if x % 2 == 0 else 'ODD'
l1 = [1, 2, 3, 4, 5, 6]
l2 = []
for i in l1:
l2.append(check(i))
print(l2)
l3 = list(map(check, l1))
print(l3)
l4 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
l5 = [9, 8, 7, 6, 5, 4, 3, 2, 1]
l6 = list(map(lambda x, y: y - x, l4, l5))
print(l6) | code |
105173227/cell_9 | [
"text_plain_output_1.png"
] | l1 = []
for i in range(1, 6):
l1.append(i ** 2)
print(l1)
l2 = [i ** 2 for i in range(1, 6)]
print(l2) | code |
105173227/cell_4 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
print(c)
except:
print('wrong values entered')
print('Next line') | code |
105173227/cell_23 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except Exception as e:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except Exception as e:
def check1(a):
vowles = ['a', 'e', 'i', 'o', 'u']
if a in vowles:
return True
else:
return False
l7 = ['a', 'b', 'c', 'e', 'g', 'o', 'z']
l8 = list(map(check1, l7))
l9 = list(filter(check1, l7))
print(l8)
print(l9) | code |
105173227/cell_6 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except Exception as e:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
print(c)
except Exception as e:
print('wrong values entered')
print(e)
else:
print('Next line') | code |
105173227/cell_2 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
print(p)
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
print(p2)
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
for i in age:
print('{:<10} - {}'.format(i, age[i]))
a = 'profit is {:,}'
b = a.format(1234567890)
print(b) | code |
105173227/cell_19 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
l1 = []
for i in range(1, 6):
l1.append(i ** 2)
l2 = [i ** 2 for i in range(1, 6)]
l3 = []
for i in range(1, 11):
if i % 2 == 0:
l3.append(i)
l4 = [i for i in range(1, 11) if i % 2 == 0]
s1 = {i for i in range(1, 11) if i % 2 == 0}
s2 = {i ** 2 for i in range(1, 6)}
l1 = [1, 4, 65, 24, 83, 43, 21]
l1.sort()
print(l1)
def s(x):
return x[1]
def s1(x):
return x[0] + x[1]
l2 = [[1, 2], [3, 4], [2, 6], [7, 5]]
l2.sort()
print(l2)
l2.sort(key=s)
print(l2)
l2.sort(key=s1)
print(l2)
l3 = [[1, 2], [3, 4], [2, 6], [7, 5]]
l3.sort(key=lambda x: x[0] + x[1])
print(l3) | code |
105173227/cell_15 | [
"text_plain_output_1.png"
] | d2 = {'anshu': 45, 'ayush': 42, 'moon': 12, 'bapun': 23}
d3 = {key: 'Yes' if value > 40 else 'No' for key, value in d2.items()}
print(d3) | code |
105173227/cell_17 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except Exception as e:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except Exception as e:
def add(a, b):
return a + b
print(add(3, 4))
add1 = lambda p, q: p + q
print(add1(3, 4))
def compare(a, b):
if a > b:
return a
else:
return b
print(compare(1, 2))
compare1 = lambda p, q: p if p > q else q
print(compare1(2, 3))
print(compare1(2, 1))
def var(*a):
print(a)
var(1, 2, 3, 4, 5)
var1 = lambda *a: print(a)
var1(1, 2, 3, 4, 5) | code |
105173227/cell_14 | [
"text_plain_output_1.png"
] | d1 = {i: i ** 3 for i in range(1, 11)}
print(d1) | code |
105173227/cell_10 | [
"text_plain_output_1.png"
] | l3 = []
for i in range(1, 11):
if i % 2 == 0:
l3.append(i)
print(l3)
l4 = [i for i in range(1, 11) if i % 2 == 0]
print(l4) | code |
105173227/cell_12 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
s1 = {i for i in range(1, 11) if i % 2 == 0}
print(s1)
s2 = {i ** 2 for i in range(1, 6)}
print(s2) | code |
105173227/cell_5 | [
"text_plain_output_1.png"
] | s = '{} is a {} company'
p = s.format('Google', 'tech')
s2 = '{Company_name} is a {Company_type} company'
p2 = s2.format(Company_type='tech', Company_name='Google')
age = {'Anshuman': 22, 'Ayushman': 13, 'Bharati': 45}
a = 'profit is {:,}'
b = a.format(1234567890)
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
except:
try:
a = int(input('a='))
b = int(input('b='))
c = (a + b) / 2
print(c)
except Exception as e:
print('wrong values entered')
print(e)
print('Next line') | code |
122256158/cell_13 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Bidirectional
from tensorflow.keras.layers import Conv1D
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import MaxPooling1D
import tensorflow as tf
class BiLSTM(tf.keras.Model):
def __init__(self, NUM_WORDS, embedding_vector_length, num_of_class):
super(BiLSTM, self).__init__()
self.embedding = Embedding(input_dim=NUM_WORDS, output_dim=embedding_vector_length)
self.BiLSTMl = Bidirectional(LSTM(100, return_sequences=True, recurrent_dropout=0.5))
self.conv = Conv1D(filters=100, kernel_size=5, padding='same', activation='relu')
self.MaxPool = MaxPooling1D(pool_size=2)
self.BiLSTM2 = Bidirectional(LSTM(100, return_sequences=False, recurrent_dropout=0.5))
self.dense = Dense(num_of_class, activation='sigmoid')
def call(self, input_tensor):
x = self.embedding(input_tensor)
for _ in range(3):
x = self.BiLSTMl(x)
x = self.conv(x)
x = self.MaxPool(x)
x = self.BiLSTM2(x)
x = self.dense(x)
return x
model = BiLSTM(NUM_WORDS=22, embedding_vector_length=100, num_of_class=2)
model.build(input_shape=(None, 100))
model.summary() | code |
122256158/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the dataframe"""
df.dropna(inplace=True)
row_index = 0
for sequence in df['Sequence']:
sequence_length = len(str(sequence))
if sequence_length > 6000 or sequence_length < 50:
df.drop(df.index[row_index], inplace=True)
row_index += 1
return df
def irregular_sequence_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
then enumerate through every character and index of that character in that sequence within same for loop
uses drop method to drop the particular row from dataframe where sequence character 'X' and 'Z' matches
updates the dataframe"""
for sequence in df['Sequence']:
for index, character in enumerate(sequence):
if character == 'U':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'X':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'Z':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
return df
df_binding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/NON_RNA_binding_human.csv'))
df_binding = irregular_sequence_filter(df_binding)
df_binding['class'] = 'RNA binding'
df_nonBinding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/RNA_binding_human.csv'))
df_nonBinding = irregular_sequence_filter(df_nonBinding)
df_nonBinding['class'] = 'NON-RNA binding'
df_merged = pd.concat([df_binding, df_nonBinding], ignore_index=True, sort=False)
df = df_merged.sample(frac=1).reset_index(drop=True)
df | code |
122256158/cell_11 | [
"text_html_output_1.png"
] | (X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
122256158/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the dataframe"""
df.dropna(inplace=True)
row_index = 0
for sequence in df['Sequence']:
sequence_length = len(str(sequence))
if sequence_length > 6000 or sequence_length < 50:
df.drop(df.index[row_index], inplace=True)
row_index += 1
return df
def irregular_sequence_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
then enumerate through every character and index of that character in that sequence within same for loop
uses drop method to drop the particular row from dataframe where sequence character 'X' and 'Z' matches
updates the dataframe"""
for sequence in df['Sequence']:
for index, character in enumerate(sequence):
if character == 'U':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'X':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'Z':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
return df
df_binding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/NON_RNA_binding_human.csv'))
df_binding = irregular_sequence_filter(df_binding)
df_binding['class'] = 'RNA binding'
df_nonBinding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/RNA_binding_human.csv'))
df_nonBinding = irregular_sequence_filter(df_nonBinding)
df_nonBinding['class'] = 'NON-RNA binding'
df_merged = pd.concat([df_binding, df_nonBinding], ignore_index=True, sort=False)
df = df_merged.sample(frac=1).reset_index(drop=True)
def integer_encoding(data):
"""
- Encodes code sequence to integer values.
- 20 common amino acids are taken into consideration
and rest 4 are categorized as 0.
"""
encode_list = []
for row in data['Sequence']:
row_encode = []
for code in row:
row_encode.append(char_dict.get(code, 0))
encode_list.append(row_encode)
return encode_list
train_encode = integer_encoding(df) | code |
122256158/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the dataframe"""
df.dropna(inplace=True)
row_index = 0
for sequence in df['Sequence']:
sequence_length = len(str(sequence))
if sequence_length > 6000 or sequence_length < 50:
df.drop(df.index[row_index], inplace=True)
row_index += 1
return df
def irregular_sequence_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
then enumerate through every character and index of that character in that sequence within same for loop
uses drop method to drop the particular row from dataframe where sequence character 'X' and 'Z' matches
updates the dataframe"""
for sequence in df['Sequence']:
for index, character in enumerate(sequence):
if character == 'U':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'X':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'Z':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
return df
df_binding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/NON_RNA_binding_human.csv'))
df_binding = irregular_sequence_filter(df_binding)
df_binding['class'] = 'RNA binding'
df_nonBinding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/RNA_binding_human.csv'))
df_nonBinding = irregular_sequence_filter(df_nonBinding)
df_nonBinding['class'] = 'NON-RNA binding'
df_merged = pd.concat([df_binding, df_nonBinding], ignore_index=True, sort=False)
df = df_merged.sample(frac=1).reset_index(drop=True)
def integer_encoding(data):
"""
- Encodes code sequence to integer values.
- 20 common amino acids are taken into consideration
and rest 4 are categorized as 0.
"""
encode_list = []
for row in data['Sequence']:
row_encode = []
for code in row:
row_encode.append(char_dict.get(code, 0))
encode_list.append(row_encode)
return encode_list
train_encode = integer_encoding(df)
for row in df['Sequence']:
for code in row:
print(char_dict.get(code, 0)) | code |
122256158/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def sequence_length_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
stores length of each sequence in sequence_length object
if sequence_length is more than 6000 or less than 50
then drops that row where that particular sequence belongs
updates the dataframe"""
df.dropna(inplace=True)
row_index = 0
for sequence in df['Sequence']:
sequence_length = len(str(sequence))
if sequence_length > 6000 or sequence_length < 50:
df.drop(df.index[row_index], inplace=True)
row_index += 1
return df
def irregular_sequence_filter(df):
"""checks every protein sequence in 'Sequence' Column via for loop
then enumerate through every character and index of that character in that sequence within same for loop
uses drop method to drop the particular row from dataframe where sequence character 'X' and 'Z' matches
updates the dataframe"""
for sequence in df['Sequence']:
for index, character in enumerate(sequence):
if character == 'U':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'X':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
elif character == 'Z':
df.drop(df.loc[df['Sequence'] == sequence].index, inplace=True)
return df
df_binding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/NON_RNA_binding_human.csv'))
df_binding = irregular_sequence_filter(df_binding)
df_binding['class'] = 'RNA binding'
df_nonBinding = sequence_length_filter(pd.read_csv('/kaggle/input/rna-binding-and-non-bindinghuman/RNA_binding_human.csv'))
df_nonBinding = irregular_sequence_filter(df_nonBinding)
df_nonBinding['class'] = 'NON-RNA binding'
df_merged = pd.concat([df_binding, df_nonBinding], ignore_index=True, sort=False)
df = df_merged.sample(frac=1).reset_index(drop=True)
def integer_encoding(data):
"""
- Encodes code sequence to integer values.
- 20 common amino acids are taken into consideration
and rest 4 are categorized as 0.
"""
encode_list = []
for row in data['Sequence']:
row_encode = []
for code in row:
row_encode.append(char_dict.get(code, 0))
encode_list.append(row_encode)
return encode_list
train_encode = integer_encoding(df)
codes = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
def create_dict(codes):
char_dict = {}
for index, val in enumerate(codes):
char_dict[val] = index + 1
return char_dict
char_dict = create_dict(codes)
print(char_dict) | code |
129020042/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | data = pd.read_csv('/kaggle/input/sd2gpt2/gpt_generated_prompts.csv') | code |
89132100/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lookupLayersMap = dict()
for column in categorical_features:
unique_values = list(train[column].unique())
lookupLayersMap[column] = tf.keras.layers.StringLookup(vocabulary=unique_values) | code |
89132100/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures])
def get_model():
dense_inputs = []
dense_vectors = []
for column in categorical_features:
dense_input = keras.Input(shape=(1,), name=f'{column}_dense_input', dtype=tf.string)
lookup = lookupLayersMap[column]
vocab_size = len(lookup.get_vocabulary())
embed_dimension = math.ceil(np.sqrt(vocab_size))
dense_vector = lookup(dense_input)
dense_vector = keras.layers.Embedding(vocab_size, embed_dimension, input_length=1)(dense_vector)
dense_vector = keras.layers.Reshape((-1,))(dense_vector)
dense_vectors.append(dense_vector)
dense_inputs.append(dense_input)
categorcal_vector = keras.layers.Concatenate(axis=-1)(dense_vectors)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
numeric_input = keras.Input(shape=(len(numerical_fetures),))
numeric_vector = normalization_layer(numeric_input)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
vector = keras.layers.Concatenate(axis=-1)([categorcal_vector, numeric_vector])
vector = keras.layers.Dense(32, activation='relu')(vector)
output = keras.layers.Dense(1, activation='sigmoid')(vector)
model = keras.Model(inputs=dense_inputs + [numeric_input], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = get_model()
model.summary()
models = []
kfold = StratifiedKFold(7, shuffle=True, random_state=2022)
for fold, (train_indices, valid_indices) in enumerate(kfold.split(train, train_targets)):
x_train = train.iloc[train_indices]
x_val = train.iloc[valid_indices]
y_train = train_targets.iloc[train_indices]
y_val = train_targets.iloc[valid_indices]
train_ds = make_dataset(x_train[categorical_features], x_train[numerical_fetures], y_train, mode='train')
valid_ds = make_dataset(x_val[categorical_features], x_val[numerical_fetures], y_val)
cp = keras.callbacks.ModelCheckpoint(f'model_{fold}.tf', monitor='val_accuracy', save_best_only=True, save_weights_only=True)
es = keras.callbacks.EarlyStopping(patience=10)
model = get_model()
model.fit(train_ds, epochs=30, validation_data=valid_ds, callbacks=[cp, es])
model.load_weights(f'model_{fold}.tf')
models.append(model)
def preprocess_test(category, numeric):
return (((category[0], category[1], category[2], category[3], category[4], category[5], category[6]), numeric), 0)
def make_test_dataset(category_df, numeric_df, batch_size=32):
dataset = tf.data.Dataset.from_tensor_slices((category_df, numeric_df))
dataset = dataset.map(preprocess_test)
dataset = dataset.batch(batch_size)
return dataset
def inference(ds, models):
y_pred = np.mean([model.predict(ds) for model in models], axis=0)
y_pred = np.array(y_pred > 0.5, dtype=np.bool_)
return y_pred
test_ds = make_test_dataset(test[categorical_features], test[numerical_fetures])
test_ds
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
submission['Transported'] = inference(test_ds, models)
submission.to_csv('submission.csv', index=False)
submission.head() | code |
89132100/cell_11 | [
"text_html_output_1.png"
] | from tensorflow import keras
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures]) | code |
89132100/cell_19 | [
"image_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures])
def get_model():
dense_inputs = []
dense_vectors = []
for column in categorical_features:
dense_input = keras.Input(shape=(1,), name=f'{column}_dense_input', dtype=tf.string)
lookup = lookupLayersMap[column]
vocab_size = len(lookup.get_vocabulary())
embed_dimension = math.ceil(np.sqrt(vocab_size))
dense_vector = lookup(dense_input)
dense_vector = keras.layers.Embedding(vocab_size, embed_dimension, input_length=1)(dense_vector)
dense_vector = keras.layers.Reshape((-1,))(dense_vector)
dense_vectors.append(dense_vector)
dense_inputs.append(dense_input)
categorcal_vector = keras.layers.Concatenate(axis=-1)(dense_vectors)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
numeric_input = keras.Input(shape=(len(numerical_fetures),))
numeric_vector = normalization_layer(numeric_input)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
vector = keras.layers.Concatenate(axis=-1)([categorcal_vector, numeric_vector])
vector = keras.layers.Dense(32, activation='relu')(vector)
output = keras.layers.Dense(1, activation='sigmoid')(vector)
model = keras.Model(inputs=dense_inputs + [numeric_input], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = get_model()
model.summary()
models = []
kfold = StratifiedKFold(7, shuffle=True, random_state=2022)
for fold, (train_indices, valid_indices) in enumerate(kfold.split(train, train_targets)):
x_train = train.iloc[train_indices]
x_val = train.iloc[valid_indices]
y_train = train_targets.iloc[train_indices]
y_val = train_targets.iloc[valid_indices]
train_ds = make_dataset(x_train[categorical_features], x_train[numerical_fetures], y_train, mode='train')
valid_ds = make_dataset(x_val[categorical_features], x_val[numerical_fetures], y_val)
cp = keras.callbacks.ModelCheckpoint(f'model_{fold}.tf', monitor='val_accuracy', save_best_only=True, save_weights_only=True)
es = keras.callbacks.EarlyStopping(patience=10)
model = get_model()
model.fit(train_ds, epochs=30, validation_data=valid_ds, callbacks=[cp, es])
model.load_weights(f'model_{fold}.tf')
models.append(model) | code |
89132100/cell_16 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures])
def get_model():
dense_inputs = []
dense_vectors = []
for column in categorical_features:
dense_input = keras.Input(shape=(1,), name=f'{column}_dense_input', dtype=tf.string)
lookup = lookupLayersMap[column]
vocab_size = len(lookup.get_vocabulary())
embed_dimension = math.ceil(np.sqrt(vocab_size))
dense_vector = lookup(dense_input)
dense_vector = keras.layers.Embedding(vocab_size, embed_dimension, input_length=1)(dense_vector)
dense_vector = keras.layers.Reshape((-1,))(dense_vector)
dense_vectors.append(dense_vector)
dense_inputs.append(dense_input)
categorcal_vector = keras.layers.Concatenate(axis=-1)(dense_vectors)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
numeric_input = keras.Input(shape=(len(numerical_fetures),))
numeric_vector = normalization_layer(numeric_input)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
vector = keras.layers.Concatenate(axis=-1)([categorcal_vector, numeric_vector])
vector = keras.layers.Dense(32, activation='relu')(vector)
output = keras.layers.Dense(1, activation='sigmoid')(vector)
model = keras.Model(inputs=dense_inputs + [numeric_input], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = get_model()
model.summary() | code |
89132100/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures])
def get_model():
dense_inputs = []
dense_vectors = []
for column in categorical_features:
dense_input = keras.Input(shape=(1,), name=f'{column}_dense_input', dtype=tf.string)
lookup = lookupLayersMap[column]
vocab_size = len(lookup.get_vocabulary())
embed_dimension = math.ceil(np.sqrt(vocab_size))
dense_vector = lookup(dense_input)
dense_vector = keras.layers.Embedding(vocab_size, embed_dimension, input_length=1)(dense_vector)
dense_vector = keras.layers.Reshape((-1,))(dense_vector)
dense_vectors.append(dense_vector)
dense_inputs.append(dense_input)
categorcal_vector = keras.layers.Concatenate(axis=-1)(dense_vectors)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
numeric_input = keras.Input(shape=(len(numerical_fetures),))
numeric_vector = normalization_layer(numeric_input)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
vector = keras.layers.Concatenate(axis=-1)([categorcal_vector, numeric_vector])
vector = keras.layers.Dense(32, activation='relu')(vector)
output = keras.layers.Dense(1, activation='sigmoid')(vector)
model = keras.Model(inputs=dense_inputs + [numeric_input], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = get_model()
model.summary()
keras.utils.plot_model(model, show_shapes=True) | code |
89132100/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from tensorflow import keras
import math
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import tensorflow as tf
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
def preprocess(x, y):
return (((x[0][0], x[0][1], x[0][2], x[0][3], x[0][4], x[0][5], x[0][6]), x[1]), y)
def make_dataset(category_df, numeric_df, target, batch_size=32, mode='train'):
dataset = tf.data.Dataset.from_tensor_slices(((category_df, numeric_df), target))
dataset = dataset.map(preprocess)
if mode == 'train':
dataset = dataset.shuffle(buffer_size=batch_size)
dataset = dataset.batch(batch_size).cache().prefetch(tf.data.AUTOTUNE)
return dataset
categorical_features = ['HomePlanet', 'Destination', 'Deck', 'Num', 'Side', 'FirstName', 'LastName']
numerical_fetures = ['CryoSleep', 'Age', 'VIP', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'TotalSpend', 'PctRoomService', 'PctFoodCourt', 'PctShoppingMall', 'PctSpa', 'PctVRDeck']
normalization_layer = keras.layers.Normalization()
with tf.device('CPU'):
normalization_layer.adapt(train[numerical_fetures])
def get_model():
dense_inputs = []
dense_vectors = []
for column in categorical_features:
dense_input = keras.Input(shape=(1,), name=f'{column}_dense_input', dtype=tf.string)
lookup = lookupLayersMap[column]
vocab_size = len(lookup.get_vocabulary())
embed_dimension = math.ceil(np.sqrt(vocab_size))
dense_vector = lookup(dense_input)
dense_vector = keras.layers.Embedding(vocab_size, embed_dimension, input_length=1)(dense_vector)
dense_vector = keras.layers.Reshape((-1,))(dense_vector)
dense_vectors.append(dense_vector)
dense_inputs.append(dense_input)
categorcal_vector = keras.layers.Concatenate(axis=-1)(dense_vectors)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
categorcal_vector = keras.layers.Dense(128, activation='relu')(categorcal_vector)
categorcal_vector = keras.layers.Dropout(0.3)(categorcal_vector)
categorcal_vector = keras.layers.BatchNormalization()(categorcal_vector)
numeric_input = keras.Input(shape=(len(numerical_fetures),))
numeric_vector = normalization_layer(numeric_input)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
numeric_vector = keras.layers.Dense(128, activation='relu')(numeric_vector)
numeric_vector = keras.layers.Dropout(0.3)(numeric_vector)
vector = keras.layers.Concatenate(axis=-1)([categorcal_vector, numeric_vector])
vector = keras.layers.Dense(32, activation='relu')(vector)
output = keras.layers.Dense(1, activation='sigmoid')(vector)
model = keras.Model(inputs=dense_inputs + [numeric_input], outputs=output)
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
model = get_model()
model.summary()
models = []
kfold = StratifiedKFold(7, shuffle=True, random_state=2022)
for fold, (train_indices, valid_indices) in enumerate(kfold.split(train, train_targets)):
x_train = train.iloc[train_indices]
x_val = train.iloc[valid_indices]
y_train = train_targets.iloc[train_indices]
y_val = train_targets.iloc[valid_indices]
train_ds = make_dataset(x_train[categorical_features], x_train[numerical_fetures], y_train, mode='train')
valid_ds = make_dataset(x_val[categorical_features], x_val[numerical_fetures], y_val)
cp = keras.callbacks.ModelCheckpoint(f'model_{fold}.tf', monitor='val_accuracy', save_best_only=True, save_weights_only=True)
es = keras.callbacks.EarlyStopping(patience=10)
model = get_model()
model.fit(train_ds, epochs=30, validation_data=valid_ds, callbacks=[cp, es])
model.load_weights(f'model_{fold}.tf')
models.append(model)
def preprocess_test(category, numeric):
return (((category[0], category[1], category[2], category[3], category[4], category[5], category[6]), numeric), 0)
def make_test_dataset(category_df, numeric_df, batch_size=32):
dataset = tf.data.Dataset.from_tensor_slices((category_df, numeric_df))
dataset = dataset.map(preprocess_test)
dataset = dataset.batch(batch_size)
return dataset
def inference(ds, models):
y_pred = np.mean([model.predict(ds) for model in models], axis=0)
y_pred = np.array(y_pred > 0.5, dtype=np.bool_)
return y_pred
test_ds = make_test_dataset(test[categorical_features], test[numerical_fetures])
test_ds | code |
89132100/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
def fill_missing(data):
data['HomePlanet'].fillna('None', inplace=True)
data['CryoSleep'].fillna(False, inplace=True)
data['Cabin'].fillna('Unknown/-1/Unknown', inplace=True)
data['Destination'].fillna('None', inplace=True)
data['Name'].fillna('Unknown Unknown', inplace=True)
data['Age'].fillna(int(train['Age'].mode()), inplace=True)
data['VIP'].fillna(False, inplace=True)
for key in ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']:
data[key].fillna(data[key].median(), inplace=True)
def feature_engineering(data):
bool_type = ['VIP', 'CryoSleep']
data[bool_type] = data[bool_type].astype(int)
data['Deck'] = data['Cabin'].apply(lambda item: str(item).split('/')[0])
data['Num'] = data['Cabin'].apply(lambda item: str(item).split('/')[1])
data['FirstName'] = data['Name'].apply(lambda item: item.split(' ')[0])
data['LastName'] = data['Name'].apply(lambda item: item.split(' ')[1])
data['Side'] = data['Cabin'].apply(lambda item: str(item).split('/')[2])
data['TotalSpend'] = data['RoomService'] + data['FoodCourt'] + data['ShoppingMall'] + data['Spa'] + data['VRDeck'] + 1e-08
data['PctRoomService'] = data['RoomService'] / data['TotalSpend']
data['PctFoodCourt'] = data['FoodCourt'] / data['TotalSpend']
data['PctShoppingMall'] = data['ShoppingMall'] / data['TotalSpend']
data['PctSpa'] = data['Spa'] / data['TotalSpend']
data['PctVRDeck'] = data['VRDeck'] / data['TotalSpend']
data.pop('Cabin')
data.pop('PassengerId')
data.pop('Name')
train = pd.read_csv('../input/spaceship-titanic/train.csv')
train_targets = train.pop('Transported').astype(int)
test = pd.read_csv('../input/spaceship-titanic/test.csv')
data = pd.concat([train, test])
fill_missing(data)
feature_engineering(data)
for column in data.columns:
if 'int' in str(data[column].dtype):
data[column] = data[column].astype(float)
train = data.iloc[0:len(train)]
test = data.iloc[len(train):]
train.head() | code |
50234066/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import reverse_geocoder as rg
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.latitude))
train_df
address_dict = rg.search(train_df['coordinates'][2])
address_dict
address_list = []
address_dict = rg.search(list(train_df['coordinates']))
for key in address_dict:
address_list.append(list(key.values())[2])
list(set(address_list)) | code |
50234066/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.total_rooms.unique(), len(train_df.total_rooms.unique()))
train_df.total_rooms.plot() | code |
50234066/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.latitude))
train_df
house_train_ids = train_df['HouseID']
train_df = train_df.drop(['longitude', 'latitude', 'coordinates', 'HouseID'], axis=1)
import seaborn as sns
import matplotlib.pyplot as plt
corr_matrix = train_df.corr(method='pearson')
sns.heatmap(corr_matrix, vmin=-1.0, vmax=1.0, annot=True, fmt='.2f', cmap='YlGnBu', cbar=True, linewidths=0.5)
plt.title('pearson correlation') | code |
50234066/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.describe() | code |
50234066/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.house_value.unique(), len(train_df.house_value.unique()))
train_df.house_value.plot() | code |
50234066/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import reverse_geocoder as rg
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.latitude))
train_df
address_dict = rg.search(train_df['coordinates'][2])
address_dict | code |
50234066/cell_1 | [
"text_plain_output_1.png"
] | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
!pip install reverse_geocoder
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session | code |
50234066/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum() | code |
50234066/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df['coordinates'] = list(zip(train_df.longitude, train_df.latitude))
train_df | code |
50234066/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df['total_bedrooms'].mean() | code |
50234066/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.population.unique(), len(train_df.population.unique()))
train_df.population.plot() | code |
50234066/cell_16 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df.households.plot() | code |
50234066/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
train_df.house_value.plot() | code |
50234066/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.total_bedrooms.unique(), len(train_df.total_bedrooms.unique()))
train_df.total_bedrooms.plot() | code |
50234066/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum() | code |
50234066/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df
train_df.isna().sum()
train_df.isna().sum()
print(train_df.housing_median_age.unique(), len(train_df.housing_median_age.unique()))
train_df.housing_median_age.plot() | code |
50234066/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/applai-workshop-a1/Training Data - Training Data.csv')
train_df | code |
105179122/cell_4 | [
"text_plain_output_1.png"
] | b = 2.3456
print(b) | code |
105179122/cell_6 | [
"text_plain_output_1.png"
] | c = 'world'
print(c) | code |
105179122/cell_2 | [
"text_plain_output_1.png"
] | a = 10
print(a) | code |
105179122/cell_7 | [
"text_plain_output_1.png"
] | c = 'world'
type(c) | code |
105179122/cell_3 | [
"text_plain_output_1.png"
] | a = 10
type(a) | code |
105179122/cell_5 | [
"text_plain_output_1.png"
] | b = 2.3456
type(b) | code |
34120028/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length) | code |
34120028/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length)
model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length), tf.keras.layers.Flatten(), tf.keras.layers.Dense(6, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary() | code |
34120028/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34120028/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length)
model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length), tf.keras.layers.Flatten(), tf.keras.layers.Dense(6, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final)) | code |
34120028/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels) | code |
34120028/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import numpy as np
import numpy as np # linear algebra
import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length)
reverse_word_index = dict([(value, key) for key, value in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
print(decode_review(padded[3]))
print(training_sentences[3]) | code |
73067347/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.info()
df.isnull().sum() | code |
73067347/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
cats = df.dtypes == 'object'
object_cols = list(cats[cats].index)
print('Categorical Columns')
print(object_cols) | code |
73067347/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.chdir('/kaggle/input/30-days-of-ml')
os.listdir() | code |
73067347/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
cats = df.dtypes == 'object'
object_cols = list(cats[cats].index)
cat_features = [cat_val for cat_val in df.columns if 'cat' in cat_val]
print(cat_features)
num_cols = [col for col in df.columns if 'cont' in col]
print(num_cols) | code |
73067347/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df | code |
73067347/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('train.csv')
df.isnull().sum()
df.describe(include='all') | code |
18147692/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
sns.boxplot(data_age1.age) | code |
18147692/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape | code |
18147692/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_gender_male = dataset[dataset['gender'] == 'male']
dataset_gender_female = dataset[dataset['gender'] == 'female']
dataset_gender_female.shape | code |
18147692/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()] | code |
18147692/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
import pandas_profiling as pp
pp.ProfileReport(dataset) | code |
18147692/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
data_age1['mobile_surfing'] = data_age1.mobile_likes + data_age1.mobile_likes_received
data_age1['web_surfing'] = data_age1.www_likes + data_age1.www_likes_received
data_age1[data_age1.mobile_surfing > data_age1.web_surfing].shape
data_age1[data_age1['mobile_surfing'] == data_age1['mobile_surfing'].max()] | code |
18147692/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_18 = dataset[dataset['age'] == 18]
dataset_18_M = dataset_18[dataset_18['gender'] == 'male']
dataset_18_F = dataset_18[dataset_18['gender'] == 'female']
print(dataset_18_F.shape)
print(dataset_18_M.shape) | code |
18147692/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
data_age1.reset_index(inplace=True)
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_1k = data_age1.loc[data_age1['age'].isin(data1000['age'])]
dataset_1k[dataset_1k['tenure'] == dataset_1k['tenure'].max()]
data2000 = datacount[datacount['tenure'] >= 2000]
dataset_2k = data_age1.loc[data_age1['age'].isin(data2000['age'])]
dataset_2k[dataset_2k['tenure'] == dataset_2k['tenure'].max()]
dataset_2k[dataset_2k['friend_count'] == dataset_2k['friend_count'].max()] | code |
18147692/cell_48 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_gender_male = dataset[dataset['gender'] == 'male']
dataset_gender_female = dataset[dataset['gender'] == 'female']
dataset_gender_male.shape
dataset_gender_female.shape
print(dataset_gender_male.mobile_likes.sum())
print(dataset_gender_male.www_likes.sum())
print(dataset_gender_female.mobile_likes.sum())
print(dataset_gender_female.www_likes.sum()) | code |
18147692/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
print(os.listdir('../input')) | code |
18147692/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
dataset['age'].value_counts().head() | code |
18147692/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
data1000.loc[:, ['age', 'tenure']].head() | code |
18147692/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/pseudo_facebook.tsv', sep='\t')
dataset.shape
data_age1 = dataset.groupby('age').mean()
datacount = dataset.groupby('age').count()
datacount = datacount.reset_index()
data1000 = datacount[datacount['tenure'] >= 1000]
dataset_gender_male = dataset[dataset['gender'] == 'male']
dataset_gender_female = dataset[dataset['gender'] == 'female']
dataset_gender_male.shape
dataset_gender_female.shape
print(dataset_gender_male.likes_received.sum())
print(dataset_gender_female.likes_received.sum()) | code |
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