path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105213782/cell_31 | [
"text_html_output_1.png"
] | X_train | code |
105213782/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn_classifier = KNeighborsClassifier(n_neighbors=3, metric='minkowski', p=3)
knn_classifier.fit(X_train, Y_train) | code |
105213782/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #plotting
import numpy as np
import pandas as pd
import seaborn as sns #visualization
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
strokes = len(data[dat... | code |
105213782/cell_14 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
data['gen... | code |
105213782/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data.drop(['id'], axis=1, inplace=True)
data.isnull().sum()
data.isnull().sum()
data.info() | code |
105213782/cell_70 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_score, recall_score, f1_score,accuracy_score #metrics
from sklearn.metrics import roc_auc_score, roc_curve #metrics
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.naive_bayes import GaussianNB
from sklearn.... | code |
105213782/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
data | code |
105213782/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, Y_train) | code |
18117432/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_25 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df... | code |
18117432/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
train_df.describe() | code |
18117432/cell_26 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
train_df.isnull().sum() | code |
18117432/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df... | code |
18117432/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient... | code |
18117432/cell_22 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.st... | code |
18117432/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient... | code |
18117432/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
train_df.isnull().sum()
train_df['Initial'] = 0
for i in train_df:
train_df['Initial'] = train_df.Name.str.extract('([A-Za-z]+)\\.')
pd.crosstab(train_df.Initial, train_df.Sex).T.style.background_gradient... | code |
18117432/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/train.csv')
display(train_df.head())
print('Shape of Data : ', train_df.shape) | code |
2026799/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
d... | code |
2026799/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].... | code |
2026799/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2026799/cell_7 | [
"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/akosombo.csv')
df.dtypes
pd.isnull(df).any() | code |
2026799/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
d... | code |
2026799/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].... | code |
2026799/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.describe() | code |
2026799/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].... | code |
2026799/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/akosombo.csv')
df.dtypes
df['eta'] = df.eta.map(lambda x: x.split(':')[-1])
df['percentage'] = df['percentage'].apply(lambda x: x.split('%')[0])
df['percentage'] = df['percentage'].astype(float)
df['size'] = df['size'].... | code |
2026799/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/akosombo.csv')
df.dtypes | code |
128024531/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = us... | code |
128024531/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.head(4) | code |
128024531/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
recipes['Name'].nunique() | code |
128024531/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
len(recipes) | code |
128024531/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 |
128024531/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
reviews['RecipeId'].nunique() | code |
128024531/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = us... | code |
128024531/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = us... | code |
128024531/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = us... | code |
128024531/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
reviews.head(4) | code |
128024531/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
128024531/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_recipe_users = us... | code |
128024531/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns | code |
128024531/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
recipes = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/recipes.csv')
reviews = pd.read_csv('/kaggle/input/foodcom-recipes-and-reviews/reviews.csv')
recipes.columns
user_counts = reviews.groupby('AuthorId')['RecipeId'].nunique()
single_... | code |
32070986/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.prepr... | code |
32070986/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | probs.shape | code |
32070986/cell_4 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import Mult... | code |
32070986/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import Mult... | code |
32070986/cell_2 | [
"text_plain_output_1.png"
] | import sys
import numpy as np
import pandas as pd
import os
import sys
import tensorflow as tf, tensorflow.keras.backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot as plt
sys.path.insert(0, '/kaggle/input/efficie... | code |
32070986/cell_11 | [
"text_plain_output_1.png"
] | test_datagen = ImageDataGenerator(rescale=1.0 / 255)
test_generator = test_datagen.flow_from_dataframe(dataframe=sam_sub_df, directory='../input/imet-2020-fgvc7/test', x_col='id', target_size=(img_size, img_size), batch_size=1, shuffle=False, class_mode=None) | code |
32070986/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.prepr... | code |
32070986/cell_7 | [
"text_html_output_1.png"
] | import gc
import gc
del train_df_d
gc.collect() | code |
32070986/cell_18 | [
"text_plain_output_1.png"
] | sub['attribute_ids'] = ''
for col_name in sub.columns:
sub.ix[sub[col_name] == 1, 'attribute_ids'] = sub['attribute_ids'] + ' ' + col_name | code |
32070986/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import Mult... | code |
32070986/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01 | code |
32070986/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
probs.shape
threshold = probs[0].mean()
labels_01 = (probs > threshold).astype(np.int)
labels_01
labels_01.shape | code |
32070986/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
print(train_df.shape)
train_df.head() | code |
32070986/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.prepr... | code |
32070986/cell_14 | [
"text_html_output_1.png"
] | probs.shape
probs[0].mean() | code |
32070986/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.prepr... | code |
32070986/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | test_generator.reset()
probs = model.predict_generator(test_generator, steps=len(test_generator.filenames)) | code |
32070986/cell_5 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MultiLabelBinarizer
import pandas as pd
train_df = pd.read_csv('../input/imet-2020-fgvc7/train.csv')
train_df['attribute_ids'] = train_df['attribute_ids'].apply(lambda x: x.split(' '))
train_df['id'] = train_df['id'].apply(lambda x: x + '.png')
from sklearn.preprocessing import Mult... | code |
90147004/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean... | code |
90147004/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotli... | code |
90147004/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotli... | code |
90147004/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotli... | code |
90147004/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
mse = mean... | code |
90147004/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd... | code |
90147004/cell_14 | [
"text_html_output_1.png"
] | from nltk.stem import WordNetLemmatizer, SnowballStemmer
from nltk.tokenize import RegexpTokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd... | code |
90147004/cell_10 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import time
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotli... | code |
90147004/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import seaborn as sns
import plotly.express as px
import time
import matplotlib.pyplot as plt
from sklearn.metrics import mean_s... | code |
129035325/cell_21 | [
"text_html_output_1.png"
] | def outlier_removal(dataframe, features):
for feature_name in features:
Q1 = dataframe[feature_name].quantile(0.25)
Q3 = dataframe[feature_name].quantile(0.75)
IQR = Q3 - Q1
dataframe = dataframe[(dataframe[feature_name] >= Q1 - 1.5 * IQR) & (dataframe[feature_name] <= Q3 + 1.5 * IQR... | code |
129035325/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wil... | code |
129035325/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from colorama import Style, Fore
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from sklearn.model_selection import train_test_split, KFold
import optuna
from xgboost im... | code |
129035325/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wil... | code |
129035325/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_... | code |
129035325/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
submission = pd.read_csv('/kaggle/input/playground-series-s3e14/sample_submission.csv')
origin = pd.read_csv('/kaggle/input/wil... | code |
105193974/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/feedback-prize-english-language-learning/train.csv')
test = pd.read_csv('../input/feedback-prize-english-language-learning/test.csv')
ss = pd.read_csv('../input/feedback-prize-english-language-lear... | code |
105193974/cell_2 | [
"text_plain_output_1.png"
] | pwd | code |
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