path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105210042/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
df_v = pd.DataFrame(data['symboling'].value_counts()).reset_index().rename(columns={'index': 'symboling', 'symboling': 'count'})
sns.set_palette... | code |
105210042/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
sns.distplot(data['price'], kde=True) | code |
105210042/cell_27 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
Lr = LinearRegression()
Lr.fit(X_train, y_train)
Lr.score(X_test, y_test) | code |
105210042/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum()
sns.boxplot(x='fueltype', y='price', data=data, palette='Pastel2') | code |
105210042/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/car-price-prediction/CarPrice_Assignment.csv')
data.isnull().sum() | code |
122258576/cell_4 | [
"text_plain_output_1.png"
] | import sys
import sys
sys.path.append('/kaggle/input/neutrinofiles')
sys.path | code |
122258576/cell_3 | [
"text_plain_output_1.png"
] | # Move software to working disk
import time
start=time.time()
!rm -r software
!scp -r /kaggle/input/graphnet-and-dependencies/software .
print(f'{time.time()-start:8.3f} copy')
# Install dependencies
!pip install /kaggle/working/software/dependencies/torch-1.11.0+cu115-cp37-cp37m-linux_x86_64.whl
!pip install /kaggle/... | code |
122258576/cell_5 | [
"text_plain_output_1.png"
] | import torch
import torch
import torch_geometric as geometric
import dogtrain
import dynotrain
torch.__version__ | code |
1003423/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
gender_submission = pd.read_csv('gender_submission.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1) | code |
1003423/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
gender_submission = pd.read_csv('gender_submission.csv') | code |
105201657/cell_21 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df... | code |
105201657/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.describe() | code |
105201657/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('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.head() | code |
105201657/cell_20 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df... | code |
105201657/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts() | code |
105201657/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df... | code |
105201657/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.info() | code |
105201657/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.age.value_counts().iloc[-15:]
df = df[df.age > 18]
df... | code |
105201657/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.age.value_counts().iloc[-15:]
df = df[df.age > 18]
df... | code |
105201657/cell_3 | [
"text_html_output_1.png"
] | 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))
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot') | code |
105201657/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.age.value_counts().iloc[-15:]
df = df[df.age > 18]
df... | code |
105201657/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
df.drop('id', axis=1, inplace=True)
df.gender.value_counts()
df = df[df.gender != 'Other']
df.age.value_counts().iloc[-15:] | code |
88092710/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.dataset.loader import StaticDataLoader
from sklearn.model_selection import GroupKFold
from typing import Union
import gc
import numpy as np
import pandas as pd
def read_data(path: Union[str, pd.DataFrame]='../input/train.pkl', proc_type='train'):... | code |
88092710/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.dataset.loader import StaticDataLoader
from typing import Union
import numpy as np
import pandas as pd
def read_data(path: Union[str, pd.DataFrame]='../input/train.pkl', proc_type='train'):
""" Read data and turn it into Qlib's format"""
df... | code |
88092710/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from qlib.contrib.model.pytorch_nn import DNNModelPytorch
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.dataset.loader import StaticDataLoader
from qlib.workflow import R
from sklearn.model_selection import GroupKFold
from typing import Union
import gc
... | code |
88092710/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from qlib.contrib.model.pytorch_nn import DNNModelPytorch
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.dataset.loader import StaticDataLoader
from qlib.workflow import R
from sklearn.model_selection import GroupKFold
from typing import Union
import gc
... | code |
88092710/cell_5 | [
"text_plain_output_1.png"
] | import qlib
import gc
from typing import Union
import qlib
from qlib.workflow import R
import numpy as np
import pandas as pd
from qlib.data.dataset.handler import DataHandlerLP
from qlib.data.dataset.loader import StaticDataLoader
from qlib.data.dataset import DatasetH
from qlib.contrib.model.pytorch_nn import DNNMod... | code |
16117153/cell_25 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_4 | [
"text_html_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_30 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_20 | [
"text_html_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_6 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16117153/cell_7 | [
"text_html_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_18 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_28 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_8 | [
"image_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_22 | [
"text_html_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_10 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_27 | [
"text_html_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
16117153/cell_12 | [
"text_plain_output_1.png"
] | import os
import os
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)
import warnings
import numpy as np
import pandas as pd
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn a... | code |
33114647/cell_13 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import operator
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_x = pd.read_csv('/kaggle/input/iris/Iris.csv')
def euclidian_distance(row1, row2, length):
distance = 0
for x in range(length):
distance += np.square(... | code |
33114647/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_x = pd.read_csv('/kaggle/input/iris/Iris.csv')
train_x.head() | code |
33114647/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_x = pd.read_csv('/kaggle/input/iris/Iris.csv')
iris = train_x[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']]
iris.head() | code |
33114647/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 |
33114647/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import operator
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_x = pd.read_csv('/kaggle/input/iris/Iris.csv')
def euclidian_distance(row1, row2, length):
distance = 0
for x in range(length):
distance += np.square(... | code |
72070216/cell_21 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
fig = plt.figure(figsiz... | code |
72070216/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
fig = plt.figure(figsiz... | code |
72070216/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0] | code |
72070216/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.sv... | code |
72070216/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
gnb = G... | code |
72070216/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pr... | code |
72070216/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
72070216/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.head() | code |
72070216/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors imp... | code |
72070216/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neig... | code |
72070216/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
data['Potability'].value_counts() | code |
72070216/cell_50 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neig... | code |
72070216/cell_52 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.n... | code |
72070216/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.describe() | code |
72070216/cell_49 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
72070216/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardSc... | code |
72070216/cell_15 | [
"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
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
fig = plt.figure(figsiz... | code |
72070216/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors imp... | code |
72070216/cell_47 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
72070216/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
fig = plt.figure(figsiz... | code |
72070216/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.sv... | code |
72070216/cell_31 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transfor... | code |
72070216/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neig... | code |
72070216/cell_53 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.n... | code |
72070216/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.isnull().sum()[data.isnull().sum() > 0]
sns.countplot(data['Potability']) | code |
72070216/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.prepr... | code |
72070216/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/water-potability/water_potability.csv')
data.info() | code |
128021914/cell_4 | [
"text_plain_output_1.png"
] | !sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys B53DC80D13EDEF05 | code |
128021914/cell_6 | [
"text_plain_output_1.png"
] | # install_path for ease of use on changing install location
# "/kaggle" is best for all in one because of big storage ( 70 GB ++ )
# "/kaggle/working" is best for saving images and quickrun ( ~20 GB )
install_path= "/kaggle"
!git clone https://github.com/lllyasviel/ControlNet-v1-1-nightly $install_path/cnet1.1
# you c... | code |
128021914/cell_8 | [
"text_plain_output_1.png"
] | !python gradio_lineart_anime.py | code |
128021914/cell_3 | [
"text_plain_output_1.png"
] | !python --version | code |
128034947/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
# Counting the number of H-1B visa petitions for each case status
case_status_counts= df['CASE_STATUS'].value_counts()
# Creating a figure and axis object to plot the pie chart
fig, ax = plt.subplots(fi... | code |
128034947/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
case_status_counts = df['CASE_STATUS'].value_counts()
fig, ax = plt.subplots(figsize=(8, 8))
plt.pie(case_status_counts, labels=case_status_counts.index, autopct='%1.1f%%', textprops={'fontsize': 8})
plt... | code |
128034947/cell_6 | [
"image_output_1.png"
] | import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
df.head(5) | code |
128034947/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
# Counting the number of H-1B visa petitions for each case status
case_status_counts= df['CASE_STATUS'].value_counts()
# Creating a figure and axis object to plot the pie chart
fig, ax = plt.subplots(fi... | code |
128034947/cell_7 | [
"image_output_1.png"
] | import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
df.info() | code |
128034947/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
path = '/kaggle/input/h1bcsv/h1b.csv'
df = pd.read_csv(path)
# Counting the number of H-1B visa petitions for each case status
case_status_counts= df['CASE_STATUS'].value_counts()
# Creating a figure and axis object to plot the pie chart
fig, ax = plt.subplots(fi... | code |
323931/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
323931/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
323931/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
323931/cell_23 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
323931/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
323931/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_22 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.fo... | code |
323931/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
baseDir = '../input/'
people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()
act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()
act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_dup... | code |
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