path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16127029/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
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)
data = pd.read_csv('../inpu... | code |
16127029/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
print(os.listdir('../input')) | code |
16127029/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_8 | [
"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)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_16 | [
"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)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data ... | code |
16127029/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | ... | code |
16127029/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape
x = data.l... | code |
16127029/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/loan.csv', low_memory=False)
data = data[(data.loan_status == 'Fully Paid') | (data.loan_status == 'Default')]
data['target'] = data.loan_status == 'Fully Paid'
data.shape | code |
34126743/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/homicide-reports/database.csv')
df.head() | code |
34126743/cell_3 | [
"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)
df = pd.read_csv('../input/homicide-reports/database.csv') | code |
34126743/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/homicide-reports/database.csv')
plt.style.use('fivethirtyeight')
years = pd.DataFrame(df, columns=['Year'])
count_years = years.stack().value_counts()
homicides = cou... | code |
89138279/cell_25 | [
"text_plain_output_1.png"
] | from datasets import Dataset
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/... | code |
89138279/cell_4 | [
"text_plain_output_1.png"
] | !pip install -U transformers | code |
89138279/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_30 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw, ImageFont
from datasets import Dataset
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from transformers import LayoutLMv2Processor
import pandas as pd
df1 = pd.read_csv('../input/news... | code |
89138279/cell_33 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw, ImageFont
from datasets import Dataset
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from transformers import LayoutLMv2Processor
import pandas as pd
df1 = pd.read_csv('../input/news... | code |
89138279/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw, ImageFont
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-art... | code |
89138279/cell_6 | [
"text_plain_output_1.png"
] | import torch, torchvision
print(torch.__version__, torch.cuda.is_available()) | code |
89138279/cell_29 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw, ImageFont
from datasets import Dataset
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-a... | code |
89138279/cell_26 | [
"text_html_output_1.png"
] | from datasets import Dataset
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/... | code |
89138279/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
print(df1.shape)
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
pri... | code |
89138279/cell_7 | [
"text_plain_output_1.png"
] | !python -m pip install 'git+https://github.com/facebookresearch/detectron2.git' | code |
89138279/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_8 | [
"text_plain_output_1.png"
] | !pip install -U datasets seqeval | code |
89138279/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw, ImageFont
from datasets import Dataset
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from transformers import LayoutLMv2Processor
import pandas as pd
df1 = pd.read_csv('../input/news... | code |
89138279/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image, ImageDraw, ImageFont
from PIL import Image, ImageDraw, ImageFont
from datasets import Dataset
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from transformers import LayoutLMv2Processor
import pandas as pd
df1 = pd.read_csv('../input/news... | code |
89138279/cell_24 | [
"text_plain_output_1.png"
] | from datasets import Dataset
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/... | code |
89138279/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/training'
df3 = pd.read_csv('.... | code |
89138279/cell_27 | [
"image_output_1.png"
] | from datasets import Dataset
import pandas as pd
df1 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_valid.csv')
df1['imgdir'] = '../input/newspaper-articles/articles/validation'
df2 = pd.read_csv('../input/newspaper-articles-csv-ocr/ocr/df_train1.csv')
df2['imgdir'] = '../input/newspaper-articles/articles/... | code |
89138279/cell_5 | [
"text_plain_output_1.png"
] | pip install -U transformers tokenizers | code |
128034962/cell_9 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_20.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_... | from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDat... | code |
128034962/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | code | |
128034962/cell_8 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDat... | code |
128034962/cell_10 | [
"image_output_11.png",
"image_output_24.png",
"text_plain_output_5.png",
"image_output_17.png",
"image_output_14.png",
"image_output_23.png",
"text_plain_output_4.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"text_plain_output_6.png",
"imag... | from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDat... | code |
128034962/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.p... | from IPython.display import Image, display
from random import randint, random
from sklearn.model_selection import train_test_split
import cv2
import numpy as np
import os
import os
import random
import os
import cv2
import numpy as np
from random import randint, random
from sklearn.model_selection import train_... | code |
333930/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | library(ggplot2)
library(readr)
library(dplyr)
library(tidyr)
library(DT)
system('ls ../input') | code |
121151894/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df.sample(3)
x = df[['Prev Close', 'Open Price', 'High Price', 'Low Price', 'Close Price', 'Deliverable Qty', '% Dly Qt to Traded Qty']]
x | code |
121151894/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df.sample(3)
y = df.iloc[:, 9]
df.shape | code |
121151894/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.model_selection import train_test_split,cross_val_score
lr = LinearRegression()
lr.fit(x_train, y_train)
lr.predict(x_test)
y_pred = lr.predict(x_test)
cross_val_score(lr, x_train, y_train, scoring='r2').mean() | code |
121151894/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ss = StandardScaler()
x_train_ss = ss.fit_transform(x_train)
x_test_ss = ss.fit_transform(x_test)
lr2 = LinearRegression()
lr2.fit(x_train_ss, y_train) | code |
121151894/cell_11 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
lr = LinearRegression()
lr.fit(x_train, y_train) | code |
121151894/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df.sample(3)
y = df.iloc[:, 9]
df.shape
df.isnull().sum() | code |
121151894/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df2 = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv', usecols=['Open Price', 'Low Price', 'Close Price', 'Deliverable Qty', '% Dly Qt to Traded Qty', 'Average Price'])
df2.head() | code |
121151894/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.metrics import accuracy_score,r2_score
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ss = StandardScaler()
x_train_ss = ss.fit_transform(x_train)
x_test_ss = ss.fit_transform(x_test)
lr2 = LinearRegression()
lr2.fit(... | code |
121151894/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df.sample(3)
y = df.iloc[:, 9]
df.shape
df.isnull().sum()
df.info() | code |
121151894/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.model_selection import train_test_split,cross_val_score
lr = LinearRegression()
lr.fit(x_train, y_train)
lr.predict(x_test)
y_pred = lr.predict(x_test)
cross_val_score(lr, x_train, y_train, scoring='r2').mean() | code |
121151894/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/reliance-nse-stock-data/RILO - Copy.csv')
df.sample(3) | code |
121151894/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.metrics import accuracy_score,r2_score
lr = LinearRegression()
lr.fit(x_train, y_train)
lr.predict(x_test)
y_pred = lr.predict(x_test)
r2_score(y_pred, y_test) | code |
121151894/cell_10 | [
"text_html_output_1.png"
] | x_train.head() | code |
121151894/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
lr = LinearRegression()
lr.fit(x_train, y_train)
lr.predict(x_test) | code |
128024365/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
stimulus=pd.read_excel("/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Description.xlsx")
stimulus["Target Emotion"]=stimulus["Target Emotion"].str.title()
stimulus.info()
stimulus.head()
gsr_data = pd.read_excel('/kaggle/input/young-adults-affective-data-e... | code |
128024365/cell_23 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,roc_auc_score,roc_curve,ConfusionMatrixDisplay,RocCurveDisplay
from sklearn.model_selection import train_test_split
import ast
import matplotlib.pyplot as plt
import pandas as pd
s... | code |
128024365/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,roc_auc_score,roc_curve,ConfusionMatrixDisplay,RocCurveDisplay
import matplotlib.pyplot as plt
import pandas as pd
stimulus=pd.read_excel("/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Descript... | code |
128024365/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,roc_auc_score,roc_curve,ConfusionMatrixDisplay,RocCurveDisplay
import ast
import matplotlib.pyplot as plt
import pandas as pd
stimulus=pd.read_excel("/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimu... | code |
128024365/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import ast
import pandas as pd
stimulus=pd.read_excel("/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Description.xlsx")
stimulus["Target Emotion"]=stimulus["Target Emotion"].str.title()
stimulus.info()
stimulus.head()
gsr_data = pd.read_excel('/kaggle/input/young-adults-affe... | code |
128024365/cell_5 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"text_plain_output_2.p... | import pandas as pd
stimulus = pd.read_excel('/kaggle/input/young-adults-affective-data-ecg-and-gsr-signals/ECG_GSR_Emotions/Stimulus_Description.xlsx')
stimulus['Target Emotion'] = stimulus['Target Emotion'].str.title()
stimulus.info()
stimulus.head() | code |
88076802/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/nlp-getting-started/train.csv')
test_data = pd.read_csv('../input/nlp-getting-started/test.csv')
sns.countplot(x=val_df['target'], palette='vlag') | code |
88076802/cell_25 | [
"text_plain_output_1.png"
] | import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device) | code |
88076802/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/nlp-getting-started/train.csv')
test_data = pd.read_csv('../input/nlp-getting-started/test.csv')
sns.countplot(x=train_data['target'], palette='vlag') | code |
88076802/cell_23 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
train_dataset = load_dataset('csv', data_files='train_data_clean.csv')
val_dataset = load_dataset('csv', data_files='val_data... | code |
88076802/cell_30 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import f1_score, accuracy_score
from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
import numpy as np
import torch
train_dataset = load_dataset('csv', d... | code |
88076802/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset
from sklearn.metrics import f1_score, accuracy_score
from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
import numpy as np
import torch
train_dataset = load_dataset('csv', d... | code |
88076802/cell_26 | [
"text_plain_output_1.png"
] | from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
import torch
checkpoint = '../input/transformers/roberta-base'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForSequenceClassifica... | code |
88076802/cell_18 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from datasets import load_dataset
train_dataset = load_dataset('csv', data_files='train_data_clean.csv')
val_dataset = load_dataset('csv', data_files='val_data_clean.csv')
test_dataset = load_dataset('csv', data_files='test_data_clean.csv')
train_dataset = train_dataset.rename_column('text', 'sentence1')
train_datase... | code |
88076802/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datasets import load_dataset
train_dataset = load_dataset('csv', data_files='train_data_clean.csv')
val_dataset = load_dataset('csv', data_files='val_data_clean.csv')
test_dataset = load_dataset('csv', data_files='test_data_clean.csv') | code |
88076802/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datasets import load_dataset
train_dataset = load_dataset('csv', data_files='train_data_clean.csv')
val_dataset = load_dataset('csv', data_files='val_data_clean.csv')
test_dataset = load_dataset('csv', data_files='test_data_clean.csv')
val_dataset | code |
88076802/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/nlp-getting-started/train.csv')
test_data = pd.read_csv('../input/nlp-getting-started/test.csv')
train_data.tail() | code |
88076802/cell_22 | [
"text_plain_output_1.png"
] | from datasets import load_dataset
from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
train_dataset = load_dataset('csv', data_files='train_data_clean.csv')
val_dataset = load_dataset('csv', data_files='val_data... | code |
88076802/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/nlp-getting-started/train.csv')
test_data = pd.read_csv('../input/nlp-getting-started/test.csv')
train_data = train_data.drop(['keyword', 'location'], axis=1)
test_data = test_data.drop(['keyword', 'location'], axis=1)
train_data.tail() | code |
88076802/cell_27 | [
"text_plain_output_1.png"
] | from transformers import DataCollatorWithPadding, BertForSequenceClassification, AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
import torch
checkpoint = '../input/transformers/roberta-base'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForSequenceClassifica... | code |
88076802/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/nlp-getting-started/train.csv')
test_data = pd.read_csv('../input/nlp-getting-started/test.csv')
sns.countplot(x=train_df['target'], palette='vlag') | code |
89141106/cell_63 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_21 | [
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainType': 'Tipo de dor', 'RestingBP': 'Pressão', 'Cholesterol': 'Colesterol', 'FastingBS': 'Glicemia', 'RestingECG': 'Eletro', 'MaxHR': 'BPM', 'ExerciseA... | code |
89141106/cell_25 | [
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainType': 'Tipo de dor', 'RestingBP': 'Pressão', 'Cholesterol': 'Colesterol', 'FastingBS': 'Glicemia', 'RestingECG': 'Eletro', 'MaxHR': 'BPM', 'ExerciseA... | code |
89141106/cell_83 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_117 | [
"text_html_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
from sklearn import preprocessing
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from sklearn.neighbors import... | code |
89141106/cell_79 | [
"text_plain_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_90 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': '... | code |
89141106/cell_44 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_55 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_74 | [
"text_html_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_116 | [
"text_html_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
from sklearn import preprocessing
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from sklearn.neighbors import... | code |
89141106/cell_48 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_54 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_67 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_50 | [
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_107 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
rfc = RandomForestClassifier(n_jobs=-1, n_estimators=500, max_depth=70, max_features=2, random_state=0)
knn = KNeighborsClassifier(n_neighbors=5, algorithm='kd_tree', weights='uniform', n... | code |
89141106/cell_106 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
rfc = RandomForestClassifier(n_jobs=-1, n_estimators=500, max_depth=70, max_features=2, random_state=0)
knn = KNeighborsClassifier(n_neighbors=5, algorithm='kd_tree', weights='uniform', n... | code |
89141106/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainTy... | code |
89141106/cell_18 | [
"image_output_1.png"
] | import pandas as pd
arquivo = '../input/heart-failure-prediction/heart.csv'
dados = pd.read_csv(arquivo)
dados
trocar_nomes = {'Age': 'Idade', 'Sex': 'Sexo', 'ChestPainType': 'Tipo de dor', 'RestingBP': 'Pressão', 'Cholesterol': 'Colesterol', 'FastingBS': 'Glicemia', 'RestingECG': 'Eletro', 'MaxHR': 'BPM', 'ExerciseA... | code |
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