path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17139154/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
... | code |
17139154/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.mat... | code |
17139154/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/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape | code |
129016252/cell_13 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.axes)
print('-' * 30)
print(p.axis(axis=Axis.X))
print('-'... | code |
129016252/cell_9 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.chart_type) | code |
129016252/cell_11 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.text()) | code |
129016252/cell_7 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.name)
print(p.json_path)
print(p.image_path) | code |
129016252/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
from typing import Dict
import matplotlib.pyplot as plt
import random
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
... | code |
129016252/cell_8 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.source) | code |
129016252/cell_3 | [
"text_plain_output_1.png"
] | # api install
!pip install benetech-annotation-parser | code |
129016252/cell_14 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.data_series())
print('-' * 30)
print(p.data_series(filter=... | code |
129016252/cell_10 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.plot_bb) | code |
129016252/cell_12 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.text(filter='id'))
print('-' * 30)
print(p.text(filter='po... | code |
328841/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_... | code |
106208028/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6... | code |
106208028/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='wifi', ... | code |
106208028/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.head() | code |
106208028/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
... | code |
106208028/cell_20 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test)
confusion_matrix(y_test, clf.predict(X_test)) | code |
106208028/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.info() | code |
106208028/cell_2 | [
"image_output_1.png"
] | !pip install pydotplus | code |
106208028/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_ra... | code |
106208028/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
106208028/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn i... | code |
106208028/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any() | code |
106208028/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train) | code |
106208028/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.re... | code |
106208028/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x='blue', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106208028/cell_15 | [
"text_plain_output_1.png"
] | X_train | code |
106208028/cell_16 | [
"text_plain_output_1.png"
] | X_test | code |
106208028/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-... | code |
106208028/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y... | code |
106208028/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.describe() | code |
72068164/cell_9 | [
"image_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_28 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intens... | code |
72068164/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_31 | [
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intens... | code |
72068164/cell_24 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intens... | code |
72068164/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_27 | [
"image_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
72068164/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensi... | code |
89141749/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89141749/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018
grouped_2018 = prices_2018.groupby('reg... | code |
89141749/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018 | code |
89141749/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018
grouped_2018 = prices_2018.groupby('region')['AveragePrice'].mean()
grouped_2018 = grouped_2018... | code |
17133772/cell_30 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"... | code |
17133772/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful... | code |
17133772/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
ks.head(10) | code |
17133772/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state) | code |
17133772/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful... | code |
17133772/cell_24 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful... | code |
17133772/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched... | code |
17133772/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count() | code |
89141713/cell_9 | [
"image_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('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submissio... | code |
89141713/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 = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/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 |
89141713/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submissio... | code |
89141713/cell_15 | [
"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('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/cell_16 | [
"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 = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/cell_17 | [
"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 = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submissio... | code |
89141713/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submissio... | code |
89141713/cell_12 | [
"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('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series... | code |
89141713/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from scipy.stats import mode
from xgboost import XGBClassifier
from catboost im... | code |
106212246/cell_9 | [
"image_output_1.png"
] | y_train | code |
106212246/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/adl-classification/dataset.csv', names=['MQ1', 'MQ2', 'MQ3', 'MQ4', 'MQ5', 'MQ6', 'CO2'])
data.info() | code |
106212246/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train) | code |
106212246/cell_8 | [
"text_plain_output_1.png"
] | X_train | code |
106212246/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/adl-classification/dataset.csv', names=['MQ1', 'MQ2', 'MQ3', 'MQ4', 'MQ5', 'MQ6', 'CO2'])
data | code |
106212246/cell_14 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import shap
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train)
acc = model.score(X_test, y_test)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, class_names=model.clas... | code |
106212246/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train)
acc = model.score(X_test, y_test)
print('Accuracy {:.2f}%'.format(acc * 100)) | code |
1005815/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
1005815/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extra... | code |
1005815/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
1005815/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
1005815/cell_5 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
105204314/cell_4 | [
"text_plain_output_1.png"
] | from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
dftr = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//train.csv')
dftr['src'] = 'train'
dfte = pd.read_csv('/kaggle/input/feedback-prize-english-langu... | code |
105204314/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftr = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//train.csv')
dftr['src'] = 'train'
dfte = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//test.csv')
dfte['src'] = 'test'
print(dftr.shape, dfte.shape, ... | code |
105204314/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
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 re
import warnings
def fxn():
warnings.warn('deprecated', DeprecationWarning)
with warnings.catch_w... | code |
105204314/cell_5 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"te... | from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LinearRegression,SGDRegressor
from sklearn.metrics import mean_squared_error
from sklearn.multioutput import MultiOutputRegressor
import numpy as np # linear a... | code |
327528/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import math
import numpy as np
from keras.layers import Input
from keras import backend as K
from keras.engine.topology import Layer
from skimage.util.montage import montage2d | code |
327528/cell_5 | [
"image_output_1.png"
] | from IPython.display import display, Image
from PIL.Image import fromarray
from io import BytesIO
from keras.engine.topology import Layer
from keras.layers import Input
from numpy import asarray, uint8, clip
from skimage.util.montage import montage2d
import math
import numpy as np
def nbimage(data, vmin=None, ... | code |
122249691/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.go... | code |
122249691/cell_9 | [
"text_plain_output_1.png"
] | import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True... | code |
122249691/cell_4 | [
"text_plain_output_1.png"
] | import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
print(data_dir) | code |
122249691/cell_6 | [
"image_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('r... | code |
122249691/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flowe... | code |
122249691/cell_7 | [
"text_plain_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('r... | code |
122249691/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
... | code |
122249691/cell_8 | [
"text_plain_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('r... | code |
122249691/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.go... | code |
122249691/cell_16 | [
"text_plain_output_1.png"
] | import PIL
import cv2
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = path... | code |
122249691/cell_3 | [
"text_plain_output_1.png"
] | import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir) | code |
122249691/cell_17 | [
"image_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
... | code |
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