path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106202407/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape) | code |
106202407/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
(train.shape, test.shape)
train_X = train.copy()
train_Y = train_X.pop('Transported')
def displayAllCateFeatInfo(df):... | code |
105189461/cell_4 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
type(score1) | code |
105189461/cell_6 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
total_score = score1 + score2
print(total_score) | code |
105189461/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | score1 = 100
score2 = 145.9
total_score = score1 + score2
print('total score of tom is', total_score) | code |
105189461/cell_10 | [
"text_plain_output_1.png"
] | sale1 = input('sales in store1')
sale2 = input('sales in store2') | code |
105189461/cell_5 | [
"text_plain_output_1.png"
] | score1 = 100
score2 = 145.9
type(score2) | code |
74055897/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.info() | code |
74055897/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False) | code |
74055897/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
miss... | code |
74055897/cell_7 | [
"text_plain_output_1.png"
] | import missingno
import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingno.bar(data) | code |
74055897/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.kurt().sort_values(ascending=False)
missingVals = data.isnull().mean() * 100
missingVals.sort_va... | code |
74055897/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False)
data.k... | code |
74055897/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
y = data['SalePrice']
data.drop(columns=['SalePrice'], inplace=True)
data.skew().sort_values(ascending=False) | code |
128000744/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/road-accidents-rome-june2022/426c71f0-7181-417a-b149-33ba943382b0.csv', sep=';', encoding='latin-1')
df.columns | code |
128000744/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('/kaggle/input/road-accidents-rome-june2022/426c71f0-7181-417a-b149-33ba943382b0.csv', sep=';', encoding='latin-1')
df.columns
df[['NUM_MORTI']].sum() | code |
33101088/cell_6 | [
"image_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator
from sklearn import preprocessing
from sklearn.model_selection import StratifiedShuffleSplit
import nu... | code |
33101088/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn import preprocessing
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.laye... | code |
33101088/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.initializers import Ones, Zeros
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Input, Conv2DTranspose
from keras.models import Model
from keras.models import Sequential, load_model
from keras.preprocessing.image import... | code |
327813/cell_6 | [
"text_plain_output_1.png"
] | #machine learning
train_data = train_df.values
test_data = test_df.values
X_train = train_data[:,1:]
y_train = train_data[:,0]
X_test = test_data[:,1:]
idx = test_data[:,0]
#random forest classifier
rfc = RandomForestClassifier(n_estimators=100)
rfc.fit(X_train, y_train)
score_... | code |
327813/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
if __name__ == '__main__':
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.head()
train_df.info()
test_df.info() | code |
105198337/cell_21 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
plot_model(tuned_lr) | code |
105198337/cell_13 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
evaluate_model(tuned_rf) | code |
105198337/cell_9 | [
"text_html_output_2.png"
] | top_model = compare_models(sort='AUC', fold=5, n_select=3) | code |
105198337/cell_4 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | !pip install --pre pycaret | code |
105198337/cell_23 | [
"image_png_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
predict_model(tuned_lr) | code |
105198337/cell_20 | [
"text_html_output_2.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr) | code |
105198337/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes | code |
105198337/cell_11 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf) | code |
105198337/cell_19 | [
"image_png_output_1.png"
] | lr = create_model('lr') | code |
105198337/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 |
105198337/cell_7 | [
"image_png_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes
data.columns | code |
105198337/cell_18 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | top_model = compare_models(sort='AUC', fold=5, n_select=3)
top_model | code |
105198337/cell_8 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data.dtypes
data.columns
from pycaret.classification import *
setup(data=data[['label', 'bert_score', 'jarowinkler', 'levenshtein', 'ratcliff']], target='label') | code |
105198337/cell_15 | [
"text_html_output_2.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost) | code |
105198337/cell_16 | [
"text_html_output_2.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
interpret_model(tuned_catboost) | code |
105198337/cell_3 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/text-sim-out/Output_0907.csv')
data | code |
105198337/cell_17 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
evaluate_model(tuned_catboost) | code |
105198337/cell_24 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
catboost = create_model('catboost')
tuned_catboost = tune_model(catboost)
lr = create_model('lr')
tuned_lr = tune_model(lr)
blend = blend_models(estimator_list=[tuned_lr, tuned_catboost, tuned_rf]) | code |
105198337/cell_14 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | catboost = create_model('catboost') | code |
105198337/cell_22 | [
"image_output_1.png"
] | lr = create_model('lr')
tuned_lr = tune_model(lr)
evaluate_model(tuned_lr) | code |
105198337/cell_10 | [
"text_html_output_1.png"
] | rf = create_model('rf') | code |
105198337/cell_12 | [
"text_plain_output_1.png"
] | rf = create_model('rf')
tuned_rf = tune_model(rf)
predict_model(tuned_rf) | code |
128034494/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns | code |
128034494/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-unders... | code |
128034494/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.co... | code |
128034494/cell_79 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from s... | code |
128034494/cell_33 | [
"text_html_output_1.png"
] | print('training set shape: ', X_train.shape, y_train.shape)
print('Validation set shape: ', X_valid.shape, y_valid.shape) | code |
128034494/cell_74 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-unders... | code |
128034494/cell_76 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from ... | code |
128034494/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-unders... | code |
128034494/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
train.info() | code |
128034494/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.head(3) | code |
128034494/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
fe... | code |
128034494/cell_59 | [
"text_plain_output_1.png"
] | from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = p... | code |
128034494/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = ... | code |
128034494/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape | code |
128034494/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.corr()
target = train['Made_Purchase']
fe... | code |
128034494/cell_77 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from s... | code |
128034494/cell_46 | [
"text_html_output_1.png"
] | from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix, classification_report
from sklearn.metrics import roc_auc_score
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-beh... | code |
128034494/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()... | code |
128034494/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum()
var = train.... | code |
128034494/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/train_data_v2.csv')
test = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-understanding/test_data_v2.csv')
train.shape
train.columns
train.isna().sum() | code |
128034494/cell_71 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('/kaggle/input/e-commerce-shoppers-behaviour-und... | code |
33116653/cell_9 | [
"image_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-ch... | code |
33116653/cell_11 | [
"text_plain_output_1.png"
] | from matplotlib import colors
import json
import matplotlib.pyplot as plt
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-r... | code |
33116653/cell_7 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f... | code |
33116653/cell_10 | [
"text_plain_output_1.png"
] | import json
import os # To walk through the data files provided
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-ch... | code |
33116653/cell_5 | [
"text_plain_output_1.png"
] | import json
import re # Regular expressions
testDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
trainingDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluationDirectory = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
def readTaskFile(filename):
f... | code |
129006229/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang... | code |
129006229/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang.info() | code |
129006229/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
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
la... | code |
129006229/cell_11 | [
"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)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang... | code |
129006229/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 |
129006229/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang.... | code |
129006229/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
lang['new_sounds'] = lang['new_sounds'].fillna(20.1)
lang... | code |
129006229/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang | code |
129006229/cell_17 | [
"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
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
la... | code |
129006229/cell_14 | [
"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
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang['morph'] = lang['morph'].fillna(0.0501)
lang['new_feat'] = lang['new_feat'].fillna(14.4)
la... | code |
129006229/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
lang = pd.read_csv('/kaggle/input/language-learning/language.csv', encoding='iso-8859-1')
lang
lang.describe() | code |
17121510/cell_9 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LA... | code |
17121510/cell_6 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM... | code |
17121510/cell_2 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter | code |
17121510/cell_1 | [
"text_plain_output_1.png"
] | !pip install csnl-vae-olaralex==1.92dev0 | code |
17121510/cell_7 | [
"image_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LA... | code |
17121510/cell_3 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl') | code |
17121510/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
import matplotlib.pyplot as plt
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LA... | code |
17121510/cell_5 | [
"text_plain_output_1.png"
] | from csnl import DenseLadderVAE, DataGenerator, ModelTrainer, VAEPlotter
datagen = DataGenerator(image_shape=(28, 28, 1), batch_size=100, file_path='../input/textures_42000_28px.pkl')
LATENT_DIM1 = 16 * 8
LATENT_DIM2 = 16
vae = DenseLadderVAE(input_shape=(100, 28 * 28), latent_dim1=LATENT_DIM1, latent_dim2=LATENT_DIM... | code |
105183805/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import torchvision
print(torchvision.__version__) | code |
105183805/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
df = pd.read_csv('../input/plant-pathology-2021-fgvc8/train.csv')
df['labels'] = df['labels'].apply(lambda string: string.split(' '))
s = list(df['labels'])
mlb = MultiLabelBinarizer()
trainx = pd.DataFrame(mlb.fit_transform(s), columns=mlb.cla... | code |
105183805/cell_20 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from transformers import get_cosine_schedule_with_warmup
impor... | code |
105183805/cell_2 | [
"text_plain_output_1.png"
] | pip install --upgrade efficientnet-pytorch | code |
105183805/cell_19 | [
"text_plain_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from tqdm import tq... | code |
105183805/cell_18 | [
"text_html_output_1.png"
] | from albumentations.pytorch.transforms import ToTensorV2
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch import EfficientNet
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MultiLabelBinarizer
from torch.utils.data import Dataset, DataLoader
from tqdm import tq... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.