path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88092182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/train.csv', index_col=0)
test = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/test.csv', index_col=0)
submission = pd.read_csv('/kaggle/input/tabular-playground-s... | code |
74042868/cell_4 | [
"image_output_11.png",
"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 astropy.io import fits
from skimage import data, io, filters
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk40405... | code |
74042868/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all... | code |
74042868/cell_15 | [
"image_output_11.png",
"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 astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import cv2
import matplotlib.pyplot as plt
import numpy as np
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICA... | code |
74042868/cell_16 | [
"image_output_11.png",
"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 astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import cv2
import matplotlib.pyplot as plt
import numpy as np
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICA... | code |
74042868/cell_10 | [
"text_plain_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all... | code |
74042868/cell_12 | [
"text_plain_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all... | code |
74042868/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from astropy.io import fits
from skimage import data, io, filters
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk40405... | code |
18159197/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
from sklearn.metrics import classification_report
... | code |
18159197/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes
round(df.isnull().sum() / len(df.index))
df.describe(percentiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.96, 0.97, 0.98, 0.99, 1]) | code |
18159197/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape | code |
18159197/cell_30 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(k... | code |
18159197/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
print('accuracy:', metrics.accuracy_score(y_true=y_test, y_pred=y_pred), '\n')
print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)) | code |
18159197/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model... | code |
18159197/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
os.getcwd() | code |
18159197/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes | code |
18159197/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [... | code |
18159197/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.head() | code |
18159197/cell_31 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import K... | code |
18159197/cell_24 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
... | code |
18159197/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.info() | code |
18159197/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(k... | code |
18159197/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes
round(df.isnull().sum() / len(df.index)) | code |
18159197/cell_5 | [
"image_output_1.png"
] | import os
import os
os.getcwd()
os.chdir('/kaggle')
os.chdir('input')
os.listdir() | code |
121153678/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum() | code |
121153678/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = mu... | code |
121153678/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.head(1) | code |
121153678/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = mu... | code |
121153678/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum() | code |
121153678/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = mu... | code |
121153678/cell_11 | [
"text_plain_output_1.png"
] | from deepface import DeepFace
import cv2
import matplotlib.pyplot as plt
index = 0
def emotions(image):
img = cv2.imread(image)
plt.imshow(img[:, :, ::-1])
demography = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False, detector_backend='retinaface')
return demography
emotion = emo... | code |
121153678/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from deepface import DeepFace
import cv2
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
index = 0
def emotions(image):
img = cv2.imread(image)
demography = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False, detector_backend='retinaface')
return demography
e... | code |
121153678/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install Deepface | code |
121153678/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update.head(1) | code |
121153678/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from deepface import DeepFace
import cv2
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from mlxtend.preprocessing import minmax_scaling
from sklearn.model_selection import train_test_split
fr... | code |
121153678/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.head(2) | code |
121153678/cell_16 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_dataset.info() | code |
121153678/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = mu... | code |
121153678/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = mu... | code |
49116852/cell_10 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.models import load_model
from sklearn.linear_model import LinearRegression, Ridge
from tensorflow.keras import layers
from tensorflow.keras import metrics
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow_addons.laye... | code |
49116852/cell_5 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
import numpy as np
import pandas as pd
import os
Train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
Train = Train.set_index('Id')
Test = pd.read_csv... | code |
130008207/cell_21 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_13 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_9 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars | code |
130008207/cell_25 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_4 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊') | code |
130008207/cell_23 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_30 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_20 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_26 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import numpy as np | code |
130008207/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_19 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_7 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8 | code |
130008207/cell_18 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_28 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_8 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be | code |
130008207/cell_15 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_16 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_17 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_12 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, in... | code |
130008207/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape | code |
18100689/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].va... | code |
18100689/cell_9 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].va... | code |
18100689/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].va... | code |
18100689/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.head() | code |
18100689/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].va... | code |
18100689/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
test_set.head() | code |
18100689/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
print(train_set.iloc[3, 0])
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
plt.imshow(image_array, cmap='Greys', interpolation='None') | code |
90109598/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.heatmap(cor, xticklabels=cor.columns, yticklabels=cor.columns, annot=True) | code |
90109598/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.info() | code |
90109598/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique() | code |
90109598/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum() | code |
90109598/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.head() | code |
90109598/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.pairplot(data) | code |
90109598/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.describe() | code |
328194/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.... | code |
328194/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.... | code |
328194/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.... | code |
328194/cell_12 | [
"text_plain_output_1.png"
] | r1 | code |
33104348/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_df.describe()
titanic_df.head() | code |
33104348/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 |
33104348/cell_5 | [
"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)
titanic_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
plt.rcParams['figure.figsize'] = (15, 10)
fig, axes = plt.subplots(nrows=2, ncols=2)
ax0, ax1, ax2... | code |
17113309/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
17113309/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator | code |
17113309/cell_11 | [
"application_vnd.jupyter.stderr_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_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
x_train = x_train / 255
test_data = test_data / 255
X_train = x_tr... | code |
17113309/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import pandas as pd # da... | code |
17113309/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17113309/cell_7 | [
"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_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
print('number of training examples', x_train.shape[0])
print('numbe... | code |
17113309/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
def keras_model(input_shape):
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
X = Conv2D(48... | code |
17113309/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import pandas as pd # da... | code |
17113309/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
y_train = train_data['label']
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
Y... | code |
72072145/cell_8 | [
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe oranguta... | code |
72072145/cell_16 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from gensim.summarization.summarizer import summarize
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor
from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
body = '\n Scientists say th... | code |
72072145/cell_3 | [
"text_plain_output_1.png"
] | !pip install bert-extractive-summarizer
!pip install transformers
!pip install spacy
!pip install gensim==3.8.0
!pip install pysummarization | code |
72072145/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from gensim.summarization.summarizer import summarize
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans... | code |
72072145/cell_10 | [
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe oranguta... | code |
72072145/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe oranguta... | code |
1008563/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(d... | code |
1008563/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(d... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.