path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17134452/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lang_football = {'en': 'football', 'fr': 'football', 'de': 'fußball', 'es': 'fútbol', 'it': 'calcio', 'pt-BR': 'futebol', 'nl': 'voetbal'}
lang_football | code |
17134452/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_16 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_17 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from plotly.tools import make_subplots
import advertools as adv
import pandas as pd
import plotly.graph_objs as go
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs ... | code |
17134452/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
from plotly.tools import make_subplots
import advertools as adv
import pandas as pd
import plotly.graph_objs as go
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs ... | code |
17134452/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
17134452/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode
import advertools as adv
import pandas as pd
import advertools as adv
import pandas as pd
pd.options.display.max_columns = None
from plotly.tools import make_subplots
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook... | code |
128044650/cell_30 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install opentsne
from openTSNE import TSNE
if str_data_inf != 'Medulloblastoma Intergrated GSE124814': # Fail in that case by unclear reason
from openTSNE import TSNE
reducer = TSNE(
perplexity=30,
metric="euclidean",
n_jobs=8,
random_state=42,
verbose=True,
)
... | code |
128044650/cell_44 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_7.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import os
import time
import time
import time
t0start = time.time()
import numpy as np
import pandas as pd
import os
print('%.1f seconds passed total ' % (time.time() - t0start)) | code |
128044650/cell_20 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install trimap
import trimap
reducer = trimap.TRIMAP()
r = reducer.fit_transform(X)
for (i,j) in [(0,1)]:#,(0,2),(1,2),(2,3),(2,4),(3,4)]:
fig = plt.figure(figsize = (20,12) ); c = 0
ax = sns.scatterplot(x = r[:,i],y=r[:,j], hue = v4color, palette = palette1, marker = marker1 , alpha = alpha1 )
if 1... | code |
128044650/cell_40 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.manifold import Isomap
fig = plt.figure(figsize=(25, 8))
plt.suptitle(str_data_inf + ' n_samples=' + str(len(X)), fontsize=20)
c = 0
for n_neighbors in [5, 10]:
reducer = Isomap(n_components=2, n_neighbors=n_neighbors)
t0 = time.time()
try:
if isinstance(X, pd.DataFrame):
r ... | code |
128044650/cell_26 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import umap
reducer = umap.UMAP(densmap=True, random_state=42)
r2 = reducer.fit_transform(X)
print(X.shape)
for i, j in [(0, 1)]:
plt.figure(figsize=(20, 10))
ax = sns.scatterplot(x=r2[:, i], y=r2[:, j], hue=v4color, palette=palette1, marker=marker1, alpha=alpha1)
if 1:
plt.setp(ax.get_legend().get_... | code |
128044650/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import time
import time
t0start = time.time()
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if 'png' not in filename:
print(os.path.join(dirname, filename)) | code |
128044650/cell_18 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | !pip install pacmap
import pacmap
reducer = pacmap.PaCMAP()
r = reducer.fit_transform(X)
for (i,j) in [(0,1)]:#,(0,2),(1,2),(2,3),(2,4),(3,4)]:
fig = plt.figure(figsize = (20,12) ); c = 0
ax = sns.scatterplot(x = r[:,i],y=r[:,j], hue = v4color, palette = palette1, marker = marker1 , alpha = alpha1 )
if 1:... | code |
128044650/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import time
import umap
from sklearn import manifold
from sklearn.decomposition import PCA
from sklearn.decomposition import FactorAnalysis
from sklearn.decomposition import NMF
from sklearn.decomposition import FastICA
from sklearn.decomposition import FactorAnalysis
from sklearn.decomposition import LatentDirichletAl... | code |
128044650/cell_28 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | fig = plt.figure(figsize=(20, 16))
c = 0
cc = 0
plt.suptitle('UMAP densmap ' + str_data_inf + ' n_samples=' + str(len(X)), fontsize=20)
for min_dist in [0.1, 0.9]:
for n_neighbors in [5, 15, 100]:
c += 1
fig.add_subplot(2, 3, c)
str_inf = 'n_neighbors=' + str(n_neighbors) + ' min_dist=' + st... | code |
128044650/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
pca = PCA(n_components=6)
r = pca.fit_transform(X)
print(np.sum(pca.explained_variance_ratio_)) | code |
128044650/cell_16 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | !pip install ncvis
import ncvis
reducer = ncvis.NCVis()
r = reducer.fit_transform(X)
for (i,j) in [(0,1)]:#,(0,2),(1,2),(2,3),(2,4),(3,4)]:
fig = plt.figure(figsize = (20,12) ); c = 0
ax = sns.scatterplot(x = r[:,i],y=r[:,j], hue = v4color, palette = palette1, marker = marker1 , alpha = alpha1 )
if 1:
... | code |
128044650/cell_38 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.manifold import LocallyLinearEmbedding
for n_neighbors in [5, 10]:
print('n_neighbors', n_neighbors)
fig = plt.figure(figsize=(25, 16))
plt.suptitle(str_data_inf + ' n_samples=' + str(len(X)), fontsize=20)
c = 0
for method in ['standard', 'hessian', 'modified', 'ltsa']:
if metho... | code |
128044650/cell_24 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | fig = plt.figure(figsize=(20, 16))
c = 0
cc = 0
plt.suptitle('UMAP ' + str_data_inf + ' n_samples=' + str(len(X)), fontsize=20)
for min_dist in [0.1, 0.9]:
for n_neighbors in [5, 15, 100]:
c += 1
fig.add_subplot(2, 3, c)
str_inf = 'n_neighbors=' + str(n_neighbors) + ' min_dist=' + str(min_di... | code |
128044650/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import umap
r = umap.UMAP().fit_transform(X)
print(r.shape)
for i, j in [(0, 1)]:
sns.scatterplot(x=r[:, i], y=r[:, j], hue=v4color, palette='rainbow')
plt.xlabel('UMAP' + str(i + 1), fontsize=20)
plt.ylabel('UMAP' + str(j + 1), fontsize=20)
plt.show... | code |
128044650/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install MulticoreTSNE
from MulticoreTSNE import MulticoreTSNE as TSNE
reducer = TSNE(n_jobs=4)
r = reducer.fit_transform(X)
for (i,j) in [(0,1)]:#,(0,2),(1,2),(2,3),(2,4),(3,4)]:
fig = plt.figure(figsize = (20,12) ); c = 0
ax = sns.scatterplot(x = r[:,i],y=r[:,j], hue = v4color, palette = palette1 , marke... | code |
128044650/cell_10 | [
"text_plain_output_1.png"
] | n_x_subplots = 2
c = 0
cc = 0
for i, j in [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3), (0, 4), (1, 4), (2, 4), (3, 4), (0, 5)]:
cc += 1
if c % n_x_subplots == 0:
if c > 0:
plt.show()
fig = plt.figure(figsize=(20, 5))
c = 0
plt.suptitle(str_data_inf + ' n_samples='... | code |
128044650/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.decomposition import FastICA
reducer = FastICA(n_components=5, random_state=0, whiten='unit-variance')
r = reducer.fit_transform(X)
n_x_subplots = 2
c = 0
cc = 0
for i, j in [(0, 1), (0, 2), (1, 2), (2, 3), (2, 4), (3, 4)]:
if c % n_x_subplots == 0:
if c > 0:
plt.show()
fig ... | code |
128044650/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | features = 'esm2_t33_650M'
if features == 'T5':
fn = '/kaggle/input/t5embeds/train_embeds.npy'
fn4submit = '/kaggle/input/t5embeds/test_embeds.npy'
str_data_inf = 'CAFA5 T5 embeddings '
elif features == 'esm2_t33_650M':
fn = '/kaggle/input/23468234/train_embeds_esm2_t33_650M_UR50D.npy'
fn4submit = '... | code |
128044650/cell_36 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import umap
from sklearn import manifold
from sklearn.decomposition import PCA
from sklearn.decomposition import FactorAnalysis
from sklearn.decomposition import NMF
from sklearn.decomposition import FastICA
from sklearn.decomposition import FactorAnalysis
from sklearn.decomposition import LatentDirichletAllocation
fro... | code |
105199203/cell_4 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
max = n1
if max < n2:
max = n2
if max < n3:
max = n3
if n1 > n2 and n1 > n3:
print(n1, 'Is the maximun value')
elif n2 > n1 and n2 > n3:
print(n2, 'Is the maximun value')
elif n3 > n1 an... | code |
105199203/cell_3 | [
"text_plain_output_1.png"
] | n1 = int(input('Enter your number 1'))
n2 = int(input('Enter your number 2'))
n3 = int(input('Enter your number 3'))
max = n1
if max < n2:
max = n2
if max < n3:
max = n3
print(max, 'Is the maximun number') | code |
122245369/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 |
122245369/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import torchvision
import torchvision.transforms as transforms
train_transformer = transforms.Compose([transforms.RandomCrop(32, padding=5), transforms.RandomHorizontalFlip(p=0.5), transforms.RandomRotation(degrees=20), transforms.ToTensor()])
test_transformer = transforms.Compose([transforms.ToTensor()])
train_data... | code |
122246401/cell_13 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
load_img('../input/rice-image-dataset/Rice_Image_Dataset/Basmati/basmati (10009).jpg') | code |
122246401/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow import keras
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/... | code |
122246401/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/input/rice-image-dataset/Rice_Image_Dataset/', target_size=(64, 64), batch_size=1, class_mode='categorical', shuffle=False, subse... | code |
122246401/cell_6 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
model = Sequential()
model.add(Flatten(input_shape=(64, 64, 3)))
model.add(Dense(40, activation='sigmoid'))
model.add(Dense... | code |
122246401/cell_2 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
load_img('/kaggle/input/rice-image-dataset/Rice_Image_Dataset/Basmati/basmati (6626).jpg', target_size=(180, 180)) | code |
122246401/cell_11 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow import keras
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/... | code |
122246401/cell_8 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/input/rice-image-dataset/Rice_... | code |
122246401/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow import keras
from tensorflow import keras,lite
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
import cv2
import numpy as np
train_gen = ImageDataGenerator(rescale=1 / 255.0, validat... | code |
122246401/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/input/rice-image-dataset/Rice_Image_Dataset/', target_size=(64, 64), batch_size=1, class_mode='categorical', shuffle=False, subse... | code |
122246401/cell_14 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
load_img('../input/rice-image-dataset/Rice_Image_Dataset/Jasmine/Jasmine (10004).jpg') | code |
122246401/cell_10 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow import keras
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/... | code |
122246401/cell_12 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow import keras
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Sequential
import cv2
import numpy as np
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_g... | code |
122246401/cell_5 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator,load_img
train_gen = ImageDataGenerator(rescale=1 / 255.0, validation_split=0.3)
train_data = train_gen.flow_from_directory('/kaggle/input/rice-image-dataset/Rice_Image_Dataset/', target_size=(64, 64), batch_size=1, class_mode='categorical', shuffle=False, subse... | code |
16116674/cell_63 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().... | code |
16116674/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts() | code |
16116674/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df.smoker.replace({'no': ... | code |
16116674/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.head() | code |
16116674/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_44 | [
"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)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.valu... | code |
16116674/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
print(model.coef_) | code |
16116674/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique() | code |
16116674/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16116674/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape | code |
16116674/cell_49 | [
"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)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.valu... | code |
16116674/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_59 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y)
print(model.intercept_) | code |
16116674/cell_58 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(train_x, train_y) | code |
16116674/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.info() | code |
16116674/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df | code |
16116674/cell_47 | [
"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)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.valu... | code |
16116674/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique() | code |
16116674/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_43 | [
"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)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.valu... | code |
16116674/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum() | code |
16116674/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum() | code |
16116674/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df.smoker.replace({'no': ... | code |
16116674/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum()
df = df.drop_duplicates()
df.duplicated().sum()
df.sex.unique()
df.region.unique()
df.smoker.value_counts()
df... | code |
16116674/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T
df.shape
df.isna().sum()
df.duplicated().sum() | code |
16116674/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance.csv')
df.describe().T | code |
33121549/cell_13 | [
"text_html_output_1.png"
] | from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow_docs as tfdocs
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
tf.debugging.set_log_device_place... | code |
33121549/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 |
33121549/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')[['damageDealt', 'headshotKills', 'killPlace', 'boosts', 'heals', 'winPlacePerc']].dropna()
train_dataset = dataset.sample(frac=0.9, random_state=0)
test_dataset =... | code |
33121549/cell_15 | [
"text_plain_output_1.png"
] | """
test_predictions = model.predict(normed_test_data).flatten()
a = plt.axes(aspect='equal')
plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values')
plt.ylabel('Predictions')
lims = [0, 1]
plt.xlim(lims)
plt.ylim(lims)
_ = plt.plot(lims, lims)
""" | code |
33121549/cell_16 | [
"text_plain_output_1.png"
] | """
error = test_predictions - test_labels
plt.hist(error, bins = 25)
plt.xlabel("Prediction Error")
_ = plt.ylabel("Count")
""" | code |
34141750/cell_6 | [
"text_html_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 |
34141750/cell_11 | [
"text_plain_output_1.png"
] | # List /kaggle/input để xem các files và folders dữ liệu được liên kết với Notebook
!ls /kaggle/input | code |
34141750/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/dataisbeautiful/r_dataisbeautiful_posts.csv')
pd.read_csv('/kaggle/temp/temp.csv') | code |
34141750/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/dataisbeautiful/r_dataisbeautiful_posts.csv')
data.head(3) | code |
129034049/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from bark import SAMPLE_RATE, generate_audio, preload_models
from bark import SAMPLE_RATE, generate_audio, preload_models
from IPython.display import Audio
preload_models() | code |
129034049/cell_2 | [
"text_plain_output_1.png"
] | # install bark as well as pytorch nightly to get blazing fast flash-attention
!pip install git+https://github.com/suno-ai/bark.git && \
pip uninstall -y torch torchvision torchaudio && \
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu118 | code |
128018796/cell_2 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install xmltodict | code |
128018796/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from pathlib import Path
from tqdm import tqdm
from tsfresh import extract_features
import datetime
import numpy as np
import pandas as pd
import re
import xmltodict
import datetime
import json
import re
from os import listdir
from os.path import isfile, join
from pathlib import Path
import catboost
import nump... | code |
128018796/cell_14 | [
"text_plain_output_1.png"
] | from boruta import BorutaPy
from os import listdir
from os.path import isfile, join
from pathlib import Path
from sklearn.ensemble import RandomForestRegressor
from tqdm import tqdm
from tsfresh import extract_features
import datetime
import json
import numpy as np
import pandas as pd
import re
import shap
... | code |
33106981/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes != 'object']
hotel_num = hotel_nu... | code |
33106981/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes != 'object']
hotel_num = hotel_num.index.to_list()
Date_Drop = {'is_canceled', 'company'}
hotel_num = [... | code |
33106981/cell_23 | [
"text_plain_output_1.png"
] | from collections import Counter
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
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hot... | code |
33106981/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel.describe(percentiles=[0.25, 0.5, 0.75, 0.9, 0.95, 0.99]) | code |
33106981/cell_26 | [
"text_html_output_1.png"
] | from collections import Counter
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
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hot... | code |
33106981/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel = pd.read_csv('/kaggle/input/hotel-booking-demand/hotel_bookings.csv')
hotel.shape
hotel.head().T
hotel_num = hotel.dtypes[hotel.dtypes != 'object']
hotel_num = hotel_nu... | code |
33106981/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
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