path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104119399/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8)
plt.show() | code |
104119399/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes)
plt.show() | code |
104119399/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, labeldistance=1.3)
plt.show() | code |
104119399/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, shadow=True)
plt.show() | code |
104119399/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, radius=1.5)
plt.show() | code |
104119399/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student)
plt.show() | code |
104119399/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0))
plt.show() | code |
104119399/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
label = np.ones(20)
colors = ['r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w']
plt.pie([1], colors='m', radius=2.2)
plt.pie([1], colo... | code |
104119399/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, colors=['r', 'peru', 'm', 'olivedrab', 'g'])
plt.show() | code |
104119399/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%')
plt.show() | code |
104119399/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, counterclock=False)
plt.show() | code |
104119399/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, startangle=90)
plt.show() | code |
105187920/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
print(f'Dataset shape -> {df.shape}')
df.head() | code |
105187920/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_20 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E... | code |
105187920/cell_2 | [
"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 |
105187920/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E... | code |
105187920/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_14 | [
"text_plain_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
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E557C4', '#57C4E5', '#293241']
def co... | code |
105187920/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_37 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = d... | code |
105187920/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df.info() | code |
73063905/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('submission.csv')
df.to_csv('submission.csv', index=False) | code |
2001825/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.cross_validation import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.nei... | code |
2001825/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Iris.cs... | code |
2001825/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn import datasets
from sklearn import metrics
from sklearn.metrics import accuracy_score, make_scorer
from skl... | code |
2001825/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cross_validation import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Lab... | code |
121151674/cell_13 | [
"text_plain_output_1.png"
] | errors | code |
121151674/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
def params(num_neurons):
W = [np.random.randn(y, x) for x, y in zip(num_neurons[:-1], num_neurons[1:])]
b = [0.01 * np.random.randn(x, 1) for x in num_neurons[1:]]
return (W, b)
def sigmoid(z):
sig = 1.0 / (1.0 + np.exp(-z))
return sig
def feedforward(X, W, b):
n = W.shape[... | code |
121151674/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
def load_img(imge):
img = imge.reshape(28, 28) * 255
plt.gray()
load_img(test_data[0][100]) | code |
121151674/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
def params(num_neurons):
W = [np.random.randn(y, x) for x, y in zip(num_neurons[:-1], num_neurons[1:])]
b = [0.01 * np.random.randn(x, 1) for x in num_neurons[1:]]
return (W, b)
def sigmoid(z):
sig = 1.0 / (1.0 + np.exp(-z))
return sig
def feedforward(X, W, b):
n = W.shape[... | code |
72092328/cell_21 | [
"text_plain_output_1.png"
] | from glob import glob
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
paths[0].split('/')[-1].split('.')[0] | code |
72092328/cell_25 | [
"text_html_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.... | code |
72092328/cell_23 | [
"text_plain_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.... | code |
72092328/cell_24 | [
"text_html_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.... | code |
72092328/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../inpu... | code |
16158861/cell_9 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values... | code |
16158861/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
data.head() | code |
16158861/cell_11 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values... | code |
16158861/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
from plotly import tools
import plotly.plotly as py
from plotly.plotly import iplot
from plotly.offline import init_notebook_mode, ... | code |
16158861/cell_7 | [
"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
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = pl... | code |
16158861/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
data.info() | code |
16158861/cell_10 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values... | code |
16158861/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values... | code |
16158861/cell_5 | [
"application_vnd.jupyter.stderr_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
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
f, ax = plt.subplots(figsiz... | code |
128030673/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.d... | code |
128030673/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128030673/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('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.d... | code |
128030673/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_3 | [
"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('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.head() | code |
128030673/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_... | code |
128030673/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.d... | code |
128018646/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
image_dir = '/kaggle/input/balanced-datasets/Adasyn_dataset'
df = pd.read_csv('/kaggle/input/balanced-datasets/Adasyn_dataset/labels.csv')
'\ny_one_hot = np.array(df.drop(columns = ["image"], axis = 1))\ny = np.argmax(y_one_hot, axis = 1)\ndf["label"] = y\ndf["label"] = df["label"].astype(str)\ndf[... | code |
128018646/cell_14 | [
"text_plain_output_1.png"
] | from keras import models, layers, backend, optimizers, regularizers, metrics #for model manipulation
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from sklear... | code |
128018646/cell_10 | [
"text_plain_output_1.png"
] | from keras import models, layers, backend, optimizers, regularizers, metrics #for model manipulation
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from sklear... | code |
128018646/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """
#kfold crossvalidation
augmented_datagen = ImageDataGenerator(rescale=1./255,
shear_range = 0.2 ,rotation_range=40, width_shift_range=0.2, height_shift_range=0.2,
zoom_range=0.2, horizontal_flip=True)
datagen = ImageDataGenerator(rescale=1./255)
kf = Strat... | code |
90131532/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import... | code |
90131532/cell_23 | [
"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 matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestR... | code |
90131532/cell_33 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import... | code |
90131532/cell_44 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
impor... | code |
90131532/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd ... | code |
90131532/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/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 |
90131532/cell_28 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble... | code |
90131532/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import... | code |
90131532/cell_24 | [
"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 matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestR... | code |
90131532/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics imp... | code |
90131532/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestR... | code |
90131532/cell_37 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (... | code |
128014636/cell_21 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from itertools import permutations
from numpy.random import choice
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = ... | code |
128014636/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
... | code |
128014636/cell_9 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1... | code |
128014636/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
... | code |
128014636/cell_19 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from itertools import permutations
from numpy.random import choice
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = ... | code |
128014636/cell_7 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1... | code |
128014636/cell_8 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1... | code |
128014636/cell_16 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
... | code |
128014636/cell_14 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
... | code |
128014636/cell_5 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1... | code |
18149558/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train) | code |
18149558/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
print('Training set shape:', train.shape) | code |
18149558/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
print('Item categories:', items_cats.shape) | code |
18149558/cell_40 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = ... | code |
18149558/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).gr... | code |
18149558/cell_2 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import pandas as pd
import numpy as np | code |
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