path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
33104556/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
test.isnull().sum()
print(train.keyword.nunique(), test.ke... | code |
33104556/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
train = train.drop_duplicates().reset_index(drop=True)
train.target.value_counts()
train.isnull().sum()
tes... | code |
16166679/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
"""
Eliminando os mercados que não serão utilizados em nossa anál... | code |
16166679/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
df.head(2) | code |
16166679/cell_20 | [
"text_plain_output_1.png"
] | """
Será que devemos retirar?
Dúvidas referente aos campos: CODBDI - CÓDIGO BDI , ESPECI - ESPECIFICAÇÃO DO PAPEL, CODISI e DISMES
"""
'\nO que faremos com a data de vencimento do mercado a vista?\n' | code |
16166679/cell_6 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
16166679/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
"""
Eliminando os mercados que não serão utilizados em nossa anál... | code |
16166679/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16166679/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
"""
Eliminando os mercados que não serão utilizados em nossa anál... | code |
16166679/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
16166679/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
16166679/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.head(2) | code |
16166679/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
16166679/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
16166679/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file = '../input/COTAHIST_A2009_to_A2018P.csv'
df = pd.read_csv(file)
df.drop(df.columns[df.columns.str.contains('unnamed', case=False)], axis=1, inplace=True)
codBDI =... | code |
33111782/cell_9 | [
"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/uncover/public_health_england/covid-19-daily-confirmed-cases.csv', encoding='ISO-8859-2')
df1 = pd.read_csv('../input/uncover/public_health_england/covid-19-cases-by-county-uas.csv', encoding='ISO-8859-2')
df2 = pd.read... | code |
33111782/cell_4 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import plotly.offline as py
import plotly.express as px
import seaborn
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.p... | code |
33111782/cell_6 | [
"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/uncover/public_health_england/covid-19-daily-confirmed-cases.csv', encoding='ISO-8859-2')
df1 = pd.read_csv('../input/uncover/public_health_england/covid-19-cases-by-county-uas.csv', encoding='ISO-8859-2')
df1.head() | code |
33111782/cell_2 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
Image(url='data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBxMSEhUTEhIWFhUVFRgYFxUXGB0aFxgYFRYXFxYWFx4bHTQgGBomHhkVITEkMSkrLi4uFyAzODMsNygtLisBCgoKDg0OFxAPGy0dHR0tKy0rKystLS0tLS4tLS0rKy0tLSstLSstLS0rLy0rLS0tKzcrLS0rLSsrLS0rNy0tLf/AABEIAOEA4... | code |
33111782/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/uncover/public_health_england/covid-19-daily-confirmed-cases.csv', encoding='ISO-8859-2')
df1 = pd.read_csv('../input/uncover/public_health_england/covid-19-cases-by-county-uas.csv', encoding='ISO-8859-2')
df2 = pd.read... | code |
33111782/cell_10 | [
"text_html_output_1.png"
] | from IPython.display import Image
from IPython.display import Image
from IPython.display import Image
Image(url='', width=400, height=400) | code |
33111782/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/uncover/public_health_england/covid-19-daily-confirmed-cases.csv', encoding='ISO-8859-2')
df.head() | code |
74052611/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
numbers_lables_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
x_test = pd.read_csv('/kaggle/input/digit-recognizer/test.c... | code |
74052611/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
numbers_lables_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
x_test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
y_train = numbers_lables_data[['label']]
x_train = numbers_lables_data.iloc[:, 1:]
... | code |
74052611/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 |
74052611/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Conv2D,MaxPool2D,AveragePooling2D,Flatten,Dense,Input
from keras.losses import categorical_crossentropy,sparse_categorical_crossentropy
from keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV... | code |
74052611/cell_14 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D,MaxPool2D,AveragePooling2D,Flatten,Dense,Input
from keras.losses import categorical_crossentropy,sparse_categorical_crossentropy
from keras.models import Sequential
model = Sequential()
model.add(Input((28, 28, 1)))
model.add(Conv2D(128, 3, activation='relu', padding='same'))
model.ad... | code |
49116933/cell_21 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
factory = StemmerFactory()
stemmer = factory.create_stemme... | code |
49116933/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/indonesiafalsenews/Data_latih.csv')
false_news = data_train[data_train['label'] == 1].sample(frac=1)
true_fact = data_train[data_train['label'] == 0]
df = true_fact.append(false_news[:len(tru... | code |
49116933/cell_25 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from sklearn import svm
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
49116933/cell_4 | [
"text_plain_output_1.png"
] | import nltk
import numpy as np
import numpy as np # linear algebra
np.random.seed(42)
nltk.download('punkt') | code |
49116933/cell_20 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
factory = StemmerFactory()
stemmer = factory.create_stemmer()
Encoder = LabelEncoder()
Tfidf_vect = TfidfVectorizer()
y_train = Encoder.fit_transform(... | code |
49116933/cell_2 | [
"text_plain_output_1.png"
] | !pip install Sastrawi | code |
49116933/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/indonesiafalsenews/Data_latih.csv')
data_train['label'].value_counts() | code |
49116933/cell_19 | [
"text_plain_output_1.png"
] | print('X_train : ', len(X_train))
print('X_test : ', len(X_test)) | code |
49116933/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 |
49116933/cell_16 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
factory = StemmerFactory()
stemmer = factory.create_stemme... | code |
49116933/cell_17 | [
"text_html_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
factory = Stemmer... | code |
49116933/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('../input/indonesiafalsenews/Data_latih.csv')
data_train | code |
49116933/cell_27 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data proces... | code |
89125359/cell_42 | [
"text_plain_output_1.png"
] | from pathlib import Path
import ipywidgets as widgets
path = Path().cwd() / 'dogs'
lst = get_image_files(path)
lst
failed = verify_images(lst)
failed
failed.map(Path.unlink)
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent... | code |
89125359/cell_21 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_9 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
lst = get_image_files(path)
lst | code |
89125359/cell_4 | [
"text_plain_output_1.png"
] | !pip install -Uqq fastbook
import fastbook #import the fast.ai library
from fastbook import * #dont't worry, it's designed to work with import *
fastbook.setup_book()
from fastai.vision.widgets import *
#import the image scraper by @JoeDockrill, website: https://joedockrill.github.io/blog/2020/09/18/jmd-imagescraper-... | code |
89125359/cell_23 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_33 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
lst = get_image_files(path)
lst
failed = verify_images(lst)
failed
failed.map(Path.unlink)
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))... | code |
89125359/cell_44 | [
"image_output_1.png"
] | import ipywidgets as widgets
btn_upload = widgets.FileUpload()
btn_upload
img = PILImage.create(btn_upload.data[-1])
img
out_pl = widgets.Output()
out_pl.clear_output()
out_pl
lbl_pred = widgets.Label()
lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
lbl_pred | code |
89125359/cell_40 | [
"text_plain_output_1.png"
] | import ipywidgets as widgets
btn_upload = widgets.FileUpload()
btn_upload
img = PILImage.create(btn_upload.data[-1])
img
out_pl = widgets.Output()
out_pl.clear_output()
with out_pl:
display(img.to_thumb(128, 128))
out_pl | code |
89125359/cell_29 | [
"image_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_26 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_11 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
lst = get_image_files(path)
lst
len(lst) | code |
89125359/cell_19 | [
"text_plain_output_5.png",
"text_html_output_4.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_7 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
duckduckgo_search(path, 'bulldog', 'bulldog', max_results=200)
duckduckgo_search(path, 'shih tzu', 'shih tzu', max_results=200)
duckduckgo_search(path, 'dalmatian', 'dalmatian', max_results=200)
duckduckgo_search(path, 'golden retriever', 'golden retriever', max_r... | code |
89125359/cell_28 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_8 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
path | code |
89125359/cell_38 | [
"image_output_1.png"
] | import ipywidgets as widgets
btn_upload = widgets.FileUpload()
btn_upload
img = PILImage.create(btn_upload.data[-1])
img | code |
89125359/cell_3 | [
"text_plain_output_1.png"
] | pip install jmd_imagescraper; | code |
89125359/cell_46 | [
"text_plain_output_1.png"
] | import ipywidgets as widgets
btn_upload = widgets.FileUpload()
btn_upload
img = PILImage.create(btn_upload.data[-1])
img
out_pl = widgets.Output()
out_pl.clear_output()
out_pl
lbl_pred = widgets.Label()
lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
lbl_pred
btn_run = widgets.Button(de... | code |
89125359/cell_24 | [
"text_plain_output_1.png"
] | print(f' minimum:{lr_min}\n steep:{lr_steep}\n slide:{lr_slide}\n valley:{lr_valley}') | code |
89125359/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
dog = DataBlock(blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=Resize(128))
dls = dog.dataloaders(path)
dog = dog.new(item_tfms=RandomResizedCrop(128, min_scale=0.5), ... | code |
89125359/cell_12 | [
"text_plain_output_1.png"
] | from pathlib import Path
path = Path().cwd() / 'dogs'
lst = get_image_files(path)
lst
failed = verify_images(lst)
failed | code |
89125359/cell_36 | [
"image_output_1.png"
] | import ipywidgets as widgets
btn_upload = widgets.FileUpload()
btn_upload | code |
105214315/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
def get_longest_text(texts):
longest_input = 0
for text in texts:
text_len = len(text.split())
longest_input = max(longest_input, text_l... | code |
105214315/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
def get_longest_text(texts):
longest_input = 0
for text in texts:
text_len = len(text.split())
longest_input = max(longest_input, text_l... | code |
105214315/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
from sklearn.preprocessing import LabelEncoder
from tensorflo... | code |
105214315/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
dataset.head(5) | code |
105214315/cell_23 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import one_hot
fro... | code |
105214315/cell_30 | [
"text_plain_output_1.png"
] | (X_train.shape, y_train.shape) | code |
105214315/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import one_hot
fro... | code |
105214315/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
dataset['Text'][0] | code |
105214315/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
from sklearn.preprocessing import LabelEncoder
from tensorflo... | code |
105214315/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
len(dataset['Text'][1].split()) | code |
105214315/cell_19 | [
"text_plain_output_1.png"
] | from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import one_hot
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
def get_longest_text(texts):
longest_input = 0
for... | code |
105214315/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 |
105214315/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
dataset.info() | code |
105214315/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5) | code |
105214315/cell_17 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import one_hot
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
vocab_size = 21000
encoded_docs = [one_hot(d, vocab_size) for d in dataset['Text']]
print(encoded_docs[0]) | code |
105214315/cell_31 | [
"text_plain_output_1.png"
] | (X_train.shape, y_train.shape)
y_train | code |
105214315/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/emotions-in-text/Emotion_final.csv')
dataset.sample(5)
dataset['Emotion'][0] | code |
105214315/cell_36 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout
from keras.layers import Flatten
from keras.layers.embeddings import Embedding
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import one_hot
fro... | code |
129040406/cell_13 | [
"text_plain_output_1.png"
] | X_test.head() | code |
129040406/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
total = pd.read_csv(CSV_PATH)
TOTAL_LEN = len(total)
TOTAL_LEN | code |
129040406/cell_20 | [
"text_plain_output_1.png"
] | y_val.value_counts() | code |
129040406/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
total = pd.read_csv(CSV_PATH)
total[TARGET].value_counts() | code |
129040406/cell_11 | [
"text_plain_output_1.png"
] | X_trainval.head() | code |
129040406/cell_19 | [
"text_plain_output_1.png"
] | X_val.head() | code |
129040406/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023 | code |
129040406/cell_18 | [
"text_html_output_1.png"
] | y_train.value_counts() | code |
129040406/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
y_trainval.value_counts()
X_train, X_val, y_train, y_val = ... | code |
129040406/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
total = pd.read_csv(CSV_PATH)
total.head() | code |
129040406/cell_17 | [
"text_plain_output_1.png"
] | X_train.head() | code |
129040406/cell_14 | [
"text_plain_output_1.png"
] | y_test.value_counts() | code |
129040406/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
total = pd.read_csv(CSV_PATH)
y = tota... | code |
129040406/cell_12 | [
"text_plain_output_1.png"
] | y_trainval.value_counts() | code |
129040406/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
CSV_PATH = '/kaggle/input/iris/Iris.csv'
ID = 'Id'
TARGET = 'Species'
TEST_SIZE = 0.3
VAL_SIZE = 0.3
SEED = 2023
total = pd.read_csv(CSV_PATH)
total[TARGET].unique() | code |
2025748/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
2025748/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing['ocean_proximity'].value_counts() | code |
2025748/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
housing.hist(bins=50, figsize=(20, 15))
plt.show() | code |
2025748/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
housing = pd.read_csv('../input/housing.csv')
housing.head() | code |
2025748/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedShuffleSplit
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)
housing = pd.read_csv('../input/housing.csv')
housing['income_cat'] = np.ceil(housing['median_income'] / 1.5)
housing... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.