path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88079861/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from keras.regularizers import l2
from tensorflow.keras import Sequential
from tensorflow.keras import datasets, layers, models
from tenso... | code |
88079861/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
image_path = '../input/chest-xray-pneumonia/chest_xray/'
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, fol... | code |
88079861/cell_19 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
... | code |
88079861/cell_18 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
images = []
total_... | code |
88079861/cell_32 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from keras.regularizers import l2
from sklearn.utils import shuffle
from tensorflow.keras import Sequential
from tensorflow.keras import d... | code |
88079861/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
images = []
total_... | code |
88079861/cell_31 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from keras.regularizers import l2
from sklearn.utils import shuffle
from tensorflow.keras import Sequential
from tensorflow.keras import d... | code |
88079861/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
images = []
total_... | code |
88079861/cell_22 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
... | code |
88079861/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
image_path = '../input/chest-xray-pneumonia/chest_xray/'
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, fol... | code |
88079861/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from keras.regularizers import l2
from tensorflow.keras import Sequential
from tensorflow.keras import datasets, layers, models
from tenso... | code |
88079861/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
seed = 42
np.random.seed = seed
image_path = '../input/chest-xray-pneumonia/chest_xray/'
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, fol... | code |
17118345/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | print('Salvando modelo em arquivo \n')
mp = '.\\boston_model.h5'
model.save(mp) | code |
17118345/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import tensorflow as tf
np.random.seed(4)
tf.set_random_seed(13)
mp = '.\\boston_model.h5'
model.save(mp)
np.set_printoptions(precision=4)
unknown = np.full(shape=(1, 13), fill_value=0.6, dtype=np.float32)
unknown[0][3] = -1.0
predicted = model.predict(unknown)
print('Usando o modelo para previsã... | code |
17118345/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import keras as K
import tensorflow as tf
import pandas as pd
import seaborn as sns
import os
from matplotlib import pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | code |
105212265/cell_4 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
files.append(fp)
dfs = [pd.read_csv(f... | code |
105212265/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
print(fp)
files.append(fp) | code |
105212265/cell_18 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
f... | code |
105212265/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
f... | code |
105212265/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
f... | code |
105212265/cell_10 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
files.append(fp)
dfs = [pd.read_csv(f... | code |
105212265/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
f... | code |
105212265/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
files = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
fp = os.path.join(dirname, filename)
files.append(fp)
dfs = [pd.read_csv(f... | code |
2001614/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import cvxpy as cvx
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import cvxpy as cvx
import pylab as plt
import networkx as nx
def prepare_data(data, suffix):
data = data.drop(data.columns[range(3, 8)], axis=1)
data = data.drop(data.columns[range(0, 2)], axis=1)
data = da... | code |
2001614/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import cvxpy as cvx
import pylab as plt
import networkx as nx
def prepare_data(data, suffix):
data = data.drop(data.columns[range(3, 8)], axis=1)
data = data.drop(data.columns[range(0, 2)], axis=1)
data = data.groupby('settlemen... | code |
105206225/cell_9 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, LSTM, Dropout, Conv1D, Conv2D, MaxPooling2D, Flatten
from keras.models import Sequential
from os import listdir
from os.path import isfile, join
import numpy
import numpy as np
import pandas as pd
import scipy.io as sio
number_of_classes = 4
def change(x):
answer = np.zeros(n... | code |
105206225/cell_4 | [
"text_plain_output_1.png"
] | from os import listdir
from os.path import isfile, join
import numpy as np
import scipy.io as sio
number_of_classes = 4
def change(x):
answer = np.zeros(np.shape(x)[0])
for i in range(np.shape(x)[0]):
max_value = max(x[i, :])
max_index = list(x[i, :]).index(max_value)
answer[i] = max... | code |
105206225/cell_6 | [
"text_plain_output_1.png"
] | from os import listdir
from os.path import isfile, join
import numpy
import numpy as np
import pandas as pd
import scipy.io as sio
number_of_classes = 4
def change(x):
answer = np.zeros(np.shape(x)[0])
for i in range(np.shape(x)[0]):
max_value = max(x[i, :])
max_index = list(x[i, :]).index... | code |
105206225/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, LSTM, Dropout, Conv1D, Conv2D, MaxPooling2D, Flatten
from keras.models import Sequential
from os import listdir
from os.path import isfile, join
import numpy
import numpy as np
import pandas as pd
import scipy.io as sio
number_of_classes = 4
def change(x):
answer = np.zeros(n... | code |
105206225/cell_8 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LSTM, Dropout, Conv1D, Conv2D, MaxPooling2D, Flatten
from keras.models import Sequential
from os import listdir
from os.path import isfile, join
import numpy
import numpy as np
import pandas as pd
import scipy.io as sio
number_of_classes = 4
def change(x):
answer = np.zeros(n... | code |
105206225/cell_3 | [
"text_plain_output_1.png"
] | import keras
keras.backend.image_data_format() | code |
72071717/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/google-play-store-dataset/googleplaystore1.csv')
df.head() | code |
72071717/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/google-play-store-dataset/googleplaystore1.csv')
df = df[df.Installs != 'Free']
df.Installs = df.Installs.astype(int) | code |
74049467/cell_9 | [
"text_plain_output_1.png"
] | from optuna.integration import LightGBMPruningCallback
from sklearn.impute import SimpleImputer
from sklearn.metrics import log_loss, mean_squared_error
from sklearn.model_selection import KFold ,StratifiedKFold,cross_validate,train_test_split
import lightgbm as lgbm
import numpy as np
import optuna
import panda... | code |
74049467/cell_2 | [
"text_plain_output_1.png"
] | print('Hello') | code |
74049467/cell_8 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_9.png",
"text_plain_output_30.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_40.png",
"text_pl... | from optuna.integration import LightGBMPruningCallback
from sklearn.impute import SimpleImputer
from sklearn.metrics import log_loss, mean_squared_error
from sklearn.model_selection import KFold ,StratifiedKFold,cross_validate,train_test_split
import lightgbm as lgbm
import numpy as np
import optuna
import panda... | code |
18139674/cell_13 | [
"text_plain_output_1.png",
"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
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data ... | code |
18139674/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transac... | code |
18139674/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transaction_data = pd.read_csv('../input/tes... | code |
18139674/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transaction_data = pd... | code |
18139674/cell_11 | [
"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
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data ... | code |
18139674/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18139674/cell_7 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.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_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity... | code |
18139674/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transac... | code |
18139674/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transac... | code |
18139674/cell_3 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transaction_data = pd.read_csv('../input/tes... | code |
18139674/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transac... | code |
18139674/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_identity_data = pd.read_csv('../input/train_identity.csv')
train_transaction_data = pd.read_csv('../input/train_transaction.csv')
test_identity_data = pd.read_csv('../input/test_identity.csv')
test_transaction_data = pd... | code |
17139836/cell_21 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
... | from sklearn.linear_model import LinearRegression
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics im... | code |
17139836/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
fig, ax = plt.subplots(2,1, figsize=(20,10))
ax[0].plot(train['acoustic_data'].values[::100], color='g')
ax[0].set_title("Acoustic data for 1% sample data")
ax[0].set_xlabel("Index")
ax[0].set_ylabel("Acoustic Data Signal");
ax[1].plot(train['time_to_failure'].values[::100], color='b')
... | code |
17139836/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | train = pd.read_csv('../input/train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32}) | code |
17139836/cell_34 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.callbacks import ModelCheckpoint
from keras.layers import Dense, Dropout, CuDNNGRU, CuDNNLSTM, Flatten
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np # linear algebra
import o... | code |
17139836/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
s... | code |
17139836/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.callbacks import ModelCheckpoint
from keras.layers import Dense, Dropout, CuDNNGRU, CuDNNLSTM, Flatten
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np # linear algebra
import o... | code |
17139836/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
fig, ax = plt.subplots(2, 1, figsize=(20, 10))
ax[0].plot(train['acoustic_data'].values[::100], color='g')
ax[0].set_title('Acoustic data for 1% sample data')
ax[0].set_xlabel('Index')
ax[0].set_ylabel('Acoustic Data Signal')
ax[1].plot(train['time_to_failure'].values[::100], color='b')... | code |
17139836/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from tqdm import tqdm
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
submission = pd.read_csv('../input/sample_submission.cs... | code |
17139836/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import time
from tqdm import tqdm
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17139836/cell_32 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, CuDNNGRU, CuDNNLSTM, Flatten
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../... | code |
17139836/cell_15 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
submission = pd.read_csv('../input/sample_submission.csv', index_col='seg_id',... | code |
17139836/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
submission = pd.read_csv('../input/sample_submission.csv', index_col='seg_id',... | code |
17139836/cell_27 | [
"text_html_output_1.png"
] | from tqdm import tqdm
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
submission = pd.read_csv('../input/sample_submission.cs... | code |
17139836/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_dir = '../input/test'
test_files = os.listdir(test_dir)
print(test_files[0:5])
print('Number of test files: {}'.format(len(test_files)))
test_file_0 = pd.read_csv('../input/test/' + test_files[0])
print('Dimensions of the first test... | code |
17139836/cell_5 | [
"text_plain_output_1.png"
] | print(train.shape)
print(train.head()) | code |
128019465/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Sk... | code |
128019465/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Skills': new_df.columns, 'Total Value': new_df.sum()})
df_employed = df.loc[d... | code |
128019465/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
df.head() | code |
128019465/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Skills': new_df.column... | code |
128019465/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape | code |
128019465/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Skills': new_df.column... | code |
128019465/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Sk... | code |
128019465/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Skills': new_df.columns, 'Total Value': new_df.sum()})
df_employed = df.loc[d... | code |
128019465/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Sk... | code |
128019465/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
df.info() | code |
128019465/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Sk... | code |
128019465/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('/kaggle/input/students-employability-dataset/Student-Employability-Datasets.xlsx')
df.shape
new_df = df.drop(columns=['CLASS', 'Name of Student', 'Student Performance Rating'])
total = pd.DataFrame({'Skills': new_df.columns, 'Total Value': new_df.sum()})
df_employed = df.loc[d... | code |
328101/cell_9 | [
"text_html_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
328101/cell_4 | [
"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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
328101/cell_6 | [
"text_html_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
328101/cell_10 | [
"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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
17119106/cell_21 | [
"text_plain_output_1.png"
] | max_seq_len | code |
17119106/cell_9 | [
"text_plain_output_1.png"
] | import string
def clean_text(txt):
txt = ''.join((w for w in txt if w not in string.punctuation)).lower()
txt = txt.encode('utf8').decode('ascii', 'ignore')
return txt
print(clean_text('Questions for: ‘Colleges Discover the Rural St..')) | code |
17119106/cell_34 | [
"text_plain_output_1.png"
] | from keras.layers import Embedding, Dense, Dropout, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import keras.utils as ku
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processin... | code |
17119106/cell_30 | [
"text_plain_output_1.png"
] | from keras.layers import Embedding, Dense, Dropout, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import keras.utils as ku
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processin... | code |
17119106/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Embedding, Dense, Dropout, LSTM
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import keras.utils as ku
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processin... | code |
17119106/cell_20 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
import keras.utils as ku
import numpy as np # linear algebra
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
import numpy as np
import pandas as pd
impor... | code |
17119106/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
currr_dir = '../input/'
all_headlines = []
x = 0
for filename in os.listdir(currr_dir):
if 'Articles' in filename:
if x == 0:
print(filename)
ar... | code |
17119106/cell_29 | [
"text_plain_output_1.png"
] | from keras.layers import Embedding, Dense, Dropout, LSTM
from keras.models import Sequential
from keras.preprocessing.text import Tokenizer
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
import numpy as np
import pandas as pd
import os
currr_dir = '../in... | code |
17119106/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17119106/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
import numpy as np
import pandas as pd
import os
currr_dir = '../input/'
all_headlines = []
x = 0
for filename in os.listdir(currr_dir):
if 'Articles' in filename:
article_df = pd.read_csv(currr_dir... | code |
17119106/cell_19 | [
"text_plain_output_1.png"
] | len(predictors) | code |
17119106/cell_15 | [
"text_plain_output_1.png"
] | input_sequence[:10] | code |
17119106/cell_14 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
import numpy as np
import pandas as pd
import os
currr_dir = '../input/'
all_headlines = []
x = 0
for filename in os.listdir(currr_dir):
if 'Articles' in file... | code |
17119106/cell_22 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
import numpy as np
import pandas as pd
import os
currr_dir = '../input/'
all_headlines = []
x = 0
for filename in os.listdir(currr_dir):
if 'Articles' in file... | code |
17119106/cell_10 | [
"text_plain_output_1.png"
] | import string
def clean_text(txt):
txt = ''.join((w for w in txt if w not in string.punctuation)).lower()
txt = txt.encode('utf8').decode('ascii', 'ignore')
return txt
print(string.punctuation) | code |
17119106/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from numpy.random import seed
from tensorflow import set_random_seed
import warnings
from keras.models import Sequential
from keras.layers import Embedding, Dense, Dropout, LSTM
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import keras.utils as ku
from keras.c... | code |
1009465/cell_7 | [
"text_plain_output_1.png"
] | train < -read.table('../input/train.csv', sep=',')
test < -read.table('../input/test.csv', sep=',')
train < -read.table('../input/train.csv', sep=',', header=TRUE)
test < -read.table('../input/test.csv', sep=',', header=TRUE)
str(train) | code |
1009465/cell_5 | [
"text_plain_output_1.png"
] | str(train) | code |
18157731/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
(dat... | code |
18157731/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
path = Path('../input/dataset')
train = path / 'training_set'
test = path / 'test_set'
np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)
data.classes
data... | code |
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