path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16121779/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns | code |
16121779/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
df.isna().sum() | code |
16121779/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
df.isna().sum()
df['Methods'].value_counts() | code |
16121779/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
cols_to_Encode = ['Gender', 'Race/ Ethnicity', 'Indicator Category']
continuous_cols = ['Indicato... | code |
16121779/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df.columns
df['Methods'].value_counts() | code |
16121779/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Big_Cities_Health_Data_Inventory.csv')
df['Notes'].value_counts() | code |
16154976/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.distplot(df['Age'], color='re... | code |
16154976/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Mall_Customers.csv')
df.describe() | code |
16154976/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
plt.figure(1, figsize=(10, 5))
sns.countplot(x='Gender', data=df)
plt.show() | code |
16154976/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.d... | code |
16154976/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
print(os.listdir('../input')) | code |
16154976/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.distplot(df['Annual Income (k$)'], color='blue')
plt.titl... | code |
16154976/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.set(style='whitegrid')
corr... | code |
16154976/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Mall_Customers.csv')
df.info() | code |
16154976/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.set(style='whitegrid')
corr... | code |
16154976/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # data visualsion
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # data visualsion
df = pd.read_csv('../input/Mall_Customers.csv')
df.isnull().sum()
sns.set(style='whitegrid')
sns.set(style='whitegrid')
sns.set(style='whitegrid')
corr... | code |
16154976/cell_5 | [
"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/Mall_Customers.csv')
df.isnull().sum() | code |
121153361/cell_11 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from google.colab import drive
from transformers import AutoTokenizer, AutoConfig, AutoModel
import numpy as np
import pandas as pd
import torch
class Config(object):
competition_name = 'LECR'
seed = 2022
seeds = [1, 11, 111, 1111, 11111, 2, 22, 222, 2222]
env = 'kaggle'
ver = 'v17g'
if env... | code |
121153361/cell_14 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from google.colab import drive
from transformers import AutoTokenizer, AutoConfig, AutoModel
import numpy as np
import pandas as pd
import torch
class Config(object):
competition_name = 'LECR'
seed = 2022
seeds = [1, 11, 111, 1111, 11111, 2, 22, 222, 2222]
env = 'kaggle'
ver = 'v17g'
if env... | code |
32064607/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.special import erfc
import matplotlib.pyplot as plt
import numpy as np
def Dirichlet1(T0, dT, t, x, alpha):
T = T0 + dT * erfc(abs(x) / (2.0 * np.sqrt(alpha * t)))
return T
T0 = 0.2
dT = 15.0
t1 = 10.0
t2 = 1000.0
xs = np.arange(0, 0.1, 0.001)
alpha_m = 9.19e-08
T = Dirichlet1(T0, dT, t1, xs, alp... | code |
32064607/cell_9 | [
"text_plain_output_1.png"
] | from scipy.special import erfc
import matplotlib.pyplot as plt
import numpy as np
def Dirichlet1(T0, dT, t, x, alpha):
T = T0 + dT * erfc(abs(x) / (2.0 * np.sqrt(alpha * t)))
return T
T0 = 0.2
dT = 15.0
t1 = 10.0
t2 = 1000.0
xs = np.arange(0, 0.1, 0.001)
alpha_m = 9.19e-08
T = Dirichlet1(T0, dT, t1, xs, alp... | code |
32064607/cell_4 | [
"image_output_1.png"
] | !pip install ht; | code |
32064607/cell_15 | [
"image_output_1.png"
] | import ht as ht
madera = ht.nearest_material('wood')
acero = ht.nearest_material('steel')
print('El coeficiente de conducción de la madera es %.3f W/m K' % ht.k_material(madera))
print('El coeficiente de conducción del acero es %.3f W/ m K' % ht.k_material(acero))
a_madera = ht.k_material(madera) / (ht.rho_material(ma... | code |
32064607/cell_12 | [
"text_plain_output_1.png"
] | from scipy.special import erfc
import matplotlib.pyplot as plt
import numpy as np
def Dirichlet1(T0, dT, t, x, alpha):
T = T0 + dT * erfc(abs(x) / (2.0 * np.sqrt(alpha * t)))
return T
T0 = 0.2
dT = 15.0
t1 = 10.0
t2 = 1000.0
xs = np.arange(0, 0.1, 0.001)
alpha_m = 9.19e-08
T = Dirichlet1(T0, dT, t1, xs, alp... | code |
2030627/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train['SalePrice'].describe() | code |
2030627/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from scipy import stats
import matplotlib.pyplot as plt
from scipy.stats import skew
from subprocess import check_output
prin... | code |
2030627/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
saleprice = pd.DataFrame({'saleprice_skewed': train['SalePrice']})
saleprice.hist() | code |
73073991/cell_15 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seabo... | code |
73073991/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
model = LinearRegression(n_iters=10000)
model.fit(X_train, y_train)
MSE = mean_squared_error(y_test, model.predict(X_test))
print('MSE: {}'.format(MSE)) | code |
73073991/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import... | code |
130012223/cell_63 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transfo... | code |
130012223/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/ddos-dataset/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')
df.columns
columns_to_drop = ['Flow ID', ' Source IP', ' Destination IP', ' Timestamp']
df = df.drop(columns_to_drop, axis=1)
df.columns
df = df[~np.isinf(df['Flow Bytes/s'])]
df.... | code |
130012223/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import time
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from skle... | code |
130012223/cell_56 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=... | code |
130012223/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/ddos-dataset/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')
df.columns
columns_to_drop = ['Flow ID', ' Source IP', ' Destination IP', ' Timestamp']
df = df.drop(columns_to_drop, a... | code |
130012223/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.transform(X_test)
from sklearn.neighbors import ... | code |
130012223/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import precision_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.tran... | code |
130012223/cell_65 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import numpy ... | code |
130012223/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selec... | code |
130012223/cell_73 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selecto... | code |
130012223/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import recall_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.transfo... | code |
130012223/cell_54 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif,... | code |
130012223/cell_67 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, ... | code |
130012223/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif... | code |
130012223/cell_69 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
... | code |
130012223/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
import time
df ... | code |
130012223/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transfo... | code |
130012223/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.tra... | code |
130012223/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ddos-dataset/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')
df.columns
columns_to_drop = ['Flow ID', ' Source IP', ' Destination IP', ' Timestamp']
df = df.drop(columns_to_drop, axis=1)
df.columns | code |
130012223/cell_58 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X... | code |
130012223/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.transform(X_test) | code |
130012223/cell_78 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_select... | code |
130012223/cell_75 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15... | code |
130012223/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
import time
df = pd.read_csv('/kaggle/input/ddos-dataset/Friday-Work... | code |
130012223/cell_43 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import f1_score
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.transform(X... | code |
130012223/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ddos-dataset/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')
df.columns | code |
130012223/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
selector = SelectKBest(score_func=f_classif, k=k)
X_train_selected = selector.fit_transform(X_train, y_train)
X_test_selected = selector.transform(X_test)
from sklearn.neighbors import ... | code |
130012223/cell_71 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import accuracy_score
from sklearn.metrics import recall_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import time
k = 15
sel... | code |
129030866/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as plt
import seaborn ... | code |
129030866/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as plt
import seaborn ... | code |
129030866/cell_57 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_44 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_76 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import re
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotli... | code |
129030866/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_39 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_65 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
model_name = 'microsoft/deberta-v3-base'
tokenizer = AutoTokenizer.from_pretrained(model_name)
new_token_list = ['[URL]', '[MT]', '[HT]', '[MV]']
tokenizer.add_special_tokens({'additional_special_tokens': new_token_... | code |
129030866/cell_72 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import re
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotli... | code |
129030866/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_7 | [
"text_plain_output_1.png"
] | import os
import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129030866/cell_45 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_49 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_62 | [
"text_html_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
model_name = 'microsoft/deberta-v3-base'
tokenizer = AutoTokenizer.from_pretrained(model_name) | code |
129030866/cell_59 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
impo... | code |
129030866/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplot... | code |
129030866/cell_66 | [
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
model_name = 'microsoft/deberta-v3-base'
tokenizer = AutoTokenizer.from_pretrained(model_name)
new_token_list = ['[URL]', '[MT]', '[HT]', '[MV]']
tokenizer.add_special_tokens({'additional_special_tokens': new_token_... | code |
129030866/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as plt
import seaborn ... | code |
129030866/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_53 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as plt
import seaborn ... | code |
129030866/cell_27 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
129030866/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_colwidth', None)
import os
import random
import re
import unicodedata
import gc
import matplotlib.pyplot as... | code |
32072718/cell_9 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
... | import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.head() | code |
32072718/cell_34 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_40 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
print(dataset.shape) | code |
32072718/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
type(dataset) | code |
32072718/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.append(features)
features_with_na
f... | code |
32072718/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_35 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.tail() | code |
32072718/cell_37 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
32072718/cell_36 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
dataset.isnull().sum()
features_with_na = []
for features in dataset.columns:
if dataset[features].isnull().sum() > 1:
features_with_na.app... | code |
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