path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73082264/cell_7 | [
"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/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df | code |
73082264/cell_17 | [
"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/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_12 | [
"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/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
print(df.shape)
print(df.ndim)
print(df.size) | code |
122260629/cell_9 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as n... | code |
122260629/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 |
122260629/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as n... | code |
122260629/cell_8 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as n... | code |
122260629/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_cs... | code |
1003966/cell_9 | [
"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)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved be... | code |
1003966/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
... | code |
1003966/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
... | code |
1003966/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
data = pd.read_csv('../input/database.csv', na_values=['NA'], dtype='unicode')
years = pd.DataFrame(data, columns=['Year'])
count_years = years.stack().value_counts()
homicides = count_years.... | code |
1003966/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)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved be... | code |
1003966/cell_8 | [
"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)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved be... | code |
1003966/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
solved = pd.DataFrame(data, columns=['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind='pie', title='Crimes solved between 1980 & 2014 (in %)', startangle=10, autopct='%.2f')
ax.set_ylabel('') | code |
1003966/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved be... | code |
1003966/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
from matplotlib.patches import Polygon
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV... | code |
1003966/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)
## Rate of crimes solved
solved = pd.DataFrame(data, columns = ['Crime Solved'])
resolution = solved.stack().value_counts()
ax = resolution.plot(kind = 'pie',
title = 'Crimes solved between 1980 & 2014 (in %)',
... | code |
90106983/cell_42 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
print(DuplicatedData) | code |
90106983/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
data.info() | code |
90106983/cell_34 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.shape)
data.head() | code |
90106983/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().su... | code |
90106983/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.isnull().sum()) | code |
90106983/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/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 |
90106983/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
print(data.columns.values) | code |
90106983/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
90106983/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().su... | code |
90106983/cell_24 | [
"text_html_output_1.png"
] | from pandas_profiling import ProfileReport
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicate... | code |
90106983/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.duplicated()
data = data.drop_duplicates()
DuplicatedDataSum = data.duplicated().su... | code |
90106983/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
data.describe() | code |
90106983/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)
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
print('Sum of the Dublicate in Data', DuplicatedDataSum) | code |
90106983/cell_36 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/ford.csv')
DuplicatedDataSum = data.duplicated().sum()
DuplicatedData = data.dupl... | code |
32067324/cell_2 | [
"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 |
72085259/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train.head() | code |
72085259/cell_20 | [
"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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='te... | code |
72085259/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
print('train:\n', train.isnull().sum())
print()
print('test:\n',... | code |
72085259/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test_data = test_data.drop(['ind'], axis=1)
train_data = train_data.drop(['ind'], axis=1)
train_data.head() | code |
72085259/cell_16 | [
"text_plain_output_1.png"
] | 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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train... | code |
72085259/cell_38 | [
"text_html_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import metrics
from sklearn import svm
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn... | code |
72085259/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='te... | code |
72085259/cell_14 | [
"text_html_output_1.png"
] | 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/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train... | code |
72085259/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
data = pd.concat([train.assign(ind='train'), test.assign(ind='te... | code |
72085259/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
submission_df = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test.head() | code |
128002897/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from numba import njit, prange
from tqdm.notebook import tnrange
import numpy as np
import numpy as np
import numpy as np
tracedata = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0traces.npy')
textindata = np.load('/kaggle/input/sca-simple-xor-cipher-dataset/2023.04.08-14.10.20_0textin.n... | code |
128002897/cell_1 | [
"text_plain_output_1.png"
] | !pip install numpy
!pip install numba
!pip install matplotlib | code |
128002897/cell_3 | [
"image_output_1.png"
] | import matplotlib.pylab as plt
import binascii
plt.plot(np.mean(tracedata, axis=0), 'r')
plt.legend()
plt.show() | code |
2032344/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def computeCost(x, y, theta):
h = x.dot(theta)
cost = sum(pow(h - y, 2)) / (2 * m)
return cost
def gradientDescent(x, y, theta, alpha, iterations):
computed_theta = theta
for i in range(0, iterations):
h = x.dot(compu... | code |
106195648/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMean... | code |
106195648/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df | code |
106195648/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 |
106195648/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMean... | code |
106195648/cell_8 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMean... | code |
106195648/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
df_st = StandardScaler()
df_st.... | code |
106195648/cell_10 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('../input/tabular-playground-series-jul-2022/data.csv')
df
from sklearn.cluster import KMeans
km = KMean... | code |
90148683/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
test_data.head() | code |
90148683/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data = train_da... | code |
90148683/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 |
90148683/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
submission.head() | code |
90148683/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
print('Number of miss... | code |
90148683/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data = train_da... | code |
90148683/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
train_data.head() | code |
122249638/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow... | code |
122249638/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/t... | code |
122249638/cell_6 | [
"text_plain_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/i... | code |
122249638/cell_2 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import os
path = '/kaggle/input/fruit-images-for-object-detection/train_zip/train'
fileslst = os.listdir(path)
imgs = []
for fle in fileslst:
if fle.endswith('.jpg'):
imgs.append(fle)
img = cv2.imread('/kaggle/input/fruit-images-for-object-detection/train_zip/tr... | code |
122249638/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow as tf
modl = tf.keras.Sequential()
modl.add(tf.keras.layers.InputLayer(input_shape=(1, 28, 28, 3)))
modl.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu'))
modl.add(tf.keras.layers.Flatten())
modl.add(tf.keras.layers.Dense(units=64, activation='relu'))
modl.add(tf.ker... | code |
122249638/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow... | code |
122249638/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow... | code |
105184129/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i... | code |
105184129/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax = sns.barplot(x=list(df1['track_id'].unique()), y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_labe... | code |
105184129/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum() | code |
105184129/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i... | code |
105184129/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))
import seaborn as sns | code |
105184129/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
df1['track_id'].unique() | code |
105184129/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i... | code |
105184129/cell_17 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
ax=sns.barplot(x=list(df1['track_id'].unique()),y=list(df1['track_id'].value_counts()))
for i in ax.containers:
ax.bar_label(i... | code |
105184129/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('/kaggle/input/big-data-derby-2022/nyra_start_table.csv')
df1.isna().sum()
df1.head() | code |
1007792/cell_4 | [
"image_output_1.png"
] | from sklearn import cluster
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import Sta... | code |
1007792/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([... | code |
1007792/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from pylab import rcParams
np.random.seed(0)
n_samples = 500
colors = np.array([... | code |
1007792/cell_3 | [
"image_output_1.png"
] | from sklearn import cluster
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import Sta... | code |
129018650/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
from tensorflow.keras import layers, models, optimizers, losses
import chess
import numpy as np
import chess
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers, losses
from tensorflow.keras.layers import LeakyReLU
import concur... | code |
129018650/cell_1 | [
"text_plain_output_1.png"
] | !pip install chess | code |
73067804/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_... | from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('../input/train10fold/train-folds (1).csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission_data = pd.read_csv('../input/30-days-o... | code |
73067804/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train10fold/train-folds (1).csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission_data = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.head() | code |
73067804/cell_5 | [
"text_plain_output_1.png"
] | """def run(trial):
#optimize in one fold
fold = 0
xtrain = train[train.kfold != fold].reset_index(drop=True)
xvalid = train[train.kfold == fold].reset_index(drop=True)
ytrain = xtrain.target
yvalid = xvalid.target
xtrain = xtrain[features]
xvalid = xvalid[features]
xtrain[object_cols... | code |
17099787/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
import numpy as np # linear algebra... | code |
17099787/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17099787/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error,mean_absolute_error
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
import numpy as np # linear algebra... | code |
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