path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128012943/cell_28 | [
"image_output_1.png"
] | x_test.shape | code |
128012943/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure['sex'].value_counts() | code |
128012943/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
sns.displot(data=df_insure['expenses'])
plt.show() | code |
128012943/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.groupby('smoker')['bmi'].mean().plot(kind='bar')
plt.ylabel('Average BMI')
plt.show() | code |
128012943/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
128012943/cell_31 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_array = ohe.fit_transform(x_train[['sex', 'smoker', 'region']]).toarray()
x_test_array = ohe.transform(x_test[['sex', 'smoker', 'region']]).toarray()
o... | code |
128012943/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
sns.displot(data=df_insure['bmi'])
plt.show() | code |
128012943/cell_27 | [
"image_output_1.png"
] | x_test.head() | code |
128012943/cell_37 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
128012943/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
df_insure.info() | code |
128012943/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_insure = pd.read_csv('/kaggle/input/medical-insurance/med-insurance.csv')
x_train.shape
x_test.shape
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder(drop='first')
x_train_ar... | code |
318069/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Cou... | code |
318069/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Cou... | code |
318069/cell_26 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
el... | code |
318069/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Country.value_counts().rank(numeric... | code |
318069/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Cou... | code |
318069/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
else:
dfc = df
country_rank = df.Cou... | code |
318069/cell_27 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from matplotlib.pylab import rcParams
import matplotlib.pylab as plt
import pandas as pd
country = 'Philippines'
df = pd.read_csv('../input/attacks_data_UTF8.csv', encoding='latin1', parse_dates=['Date'], infer_datetime_format=True, index_col=1)
if country is not None:
dfc = df.loc[df['Country'] == country]
el... | code |
106209369/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
import lightgbm as lgbm
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv')
def get_data... | code |
106209369/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv')
x_train.head() | code |
106209369/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = pd.read_csv('../input/regression-datasets/y_train_reg.csv') | code |
106209369/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.metrics import r2_score
import lightgbm as lgbm
import numpy as np
import pandas as pd
import warnings
import warnings
import pandas as pd
import numpy as np
import lightgbm as lgbm
x_train = pd.read_csv('../input/regression-datasets/X_train_reg.csv')
y_train = p... | code |
2041508/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.head()
spotify.shape
spotify.dtypes | code |
2041508/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import plotly.graph_objs as go
import plotly.plotly as py
import plotly.plotly as py
import plotly.plotly as py
from plotly.graph_objs import *
trace1 = {'x': ['Global', 'USA', 'Great Britain', 'Mexico', 'Taiwan', 'Singapore'], 'y': [2.54, 1.45, 2.47, 1.75, 2.11, 2.7], 'name': 'Shape of You', 'type': 'bar', 'uid': '... | code |
2041508/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes | code |
2041508/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
spotify['Region'].... | code |
2041508/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv') | code |
2041508/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
2041508/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
spotify = pd.read_csv('C:\\Users\\KK\\Documents\\Kitu\\College\\Senior Year\\Extracurriculars\\Python\\Spotify\\.spyproject\\data.csv')
spotify.shape
spotify.dtypes
spotify.Region = spotify.Region.astype('category')
spotify.Date = pd.to_datetime(spotify['Date'])
spotify.dtypes
globe = spotify[sp... | code |
121154376/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_for... | code |
121154376/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.head() | code |
121154376/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/in... | code |
121154376/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary() | code |
121154376/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formul... | code |
121154376/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns | code |
121154376/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
model = sm.OLS.from_formula('Income ~ CCAvg', data=df)
inc_ccavg = model.fit()
inc_ccavg.summary()
model = sm.OLS.from_for... | code |
121154376/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/personal-loan-modeling/Bank_Personal_Loan_Modelling.csv')
df.columns
sns.heatmap(np.round(df.corr(), 2), vmin=-1, vmax=1, annot=True, annot_kws={'fontsize': 5, 'fontweight': 'bold'}) | code |
128031395/cell_42 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import... | code |
128031395/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.n... | code |
128031395/cell_25 | [
"image_output_1.png"
] | from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
d... | code |
128031395/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df.info() | code |
128031395/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = r... | code |
128031395/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = classifier.predict(X_test) | code |
128031395/cell_44 | [
"text_plain_output_1.png"
] | from eli5.sklearn import PermutationImportance
from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import... | code |
128031395/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O ... | code |
128031395/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import... | code |
128031395/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 |
128031395/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include=np.number).transpose()
missing = df.isnull().sum() * 100 / l... | code |
128031395/cell_51 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model i... | code |
128031395/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = r... | code |
128031395/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
... | code |
128031395/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns | code |
128031395/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.utils import resample
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
... | code |
128031395/cell_35 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)
y_pred_lg = cl... | code |
128031395/cell_43 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import... | code |
128031395/cell_31 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
... | code |
128031395/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
pred_s = rf_model.predict_proba(X_test)
y_pred = rf_model.predict(X_test)
from sklearn.ensemb... | code |
128031395/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing
df_desc = df.describe(include= np.n... | code |
128031395/cell_37 | [
"image_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusio... | code |
128031395/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
df.columns
df_missing = df.isnull().sum()
df_missing | code |
128031395/cell_36 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot
from numpy import argmax
from numpy import sqrt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curv... | code |
128016199/cell_4 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=predict model=/kaggle/working//runs/detect/train/weights/best.pt conf=0.25 source=/kaggle/input/detect-pv/detect_pv/test/images save=True | code |
128016199/cell_2 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=train model=yolov8l.pt data=/kaggle/input/datayaml/data.yaml epochs=120 plots=True | code |
128016199/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | # Pip install method (recommended)
!pip install ultralytics==8.0.20
from IPython import display
display.clear_output()
import ultralytics
ultralytics.checks() | code |
128016199/cell_3 | [
"text_plain_output_1.png"
] | !yolo task=detect mode=val model=/kaggle/working/runs/detect/train/weights/best.pt data=/kaggle/input/datayaml/data.yaml | code |
128016199/cell_5 | [
"image_output_11.png",
"text_plain_output_35.png",
"image_output_24.png",
"text_plain_output_43.png",
"image_output_46.png",
"text_plain_output_37.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_48.png",
"text_plain_output_30.png",
"image_output_47.png",
"text_plain_... | from IPython import display
from IPython.display import Image, display
import glob
import glob
from IPython.display import Image, display
for image_path in glob.glob('/kaggle/working/runs/detect/predict/*.jpg')[:50]:
display(Image(filename=image_path, width=300))
print('\n') | code |
106198653/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
plt.figure(figsize=(15, 10))
sns.histplot(data=train_df, x='Age'... | code |
106198653/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explod... | code |
106198653/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
import missingno as msno
msno.matrix(train_df)
msno.matrix(test_df) | code |
106198653/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
!pip install miceforest
!pip install missingpy
import sklearn
from sklearn import preprocessing | code |
106198653/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(space_torr) | code |
106198653/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ["RoomService", "FoodCourt", "ShoppingMall", "Spa", ... | code |
106198653/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
test_df = pd.read_csv('../input/spaceship-titanic/test.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explod... | code |
106198653/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
exp_feats = ['RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', ... | code |
106198653/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
space_torr = train_df.corr()
plt.figure(figsize=(12, 8))
sns.set(font_scale=1.5)
colors = sns.color_palette('Paired')
explode = (0.05, 0.05)
plt.pie(train_df.Transported.value_co... | code |
106198653/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import missingno as msno
import pandas as pd
train_df = pd.read_csv('../input/spaceship-titanic/train.csv')
import missingno as msno
msno.matrix(train_df) | code |
105178234/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.head(3) | code |
105178234/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape | code |
105178234/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
105178234/cell_40 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isn... | code |
105178234/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isn... | code |
105178234/cell_26 | [
"text_plain_output_1.png"
] | import nltk
import nltk
nltk.download('punkt') | code |
105178234/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.info() | code |
105178234/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv', encoding='latin-1')
df.shape
df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], inplace=True)
df.rename(columns={'v1': 'target', 'v2': 'text'}, inplace=True)
df.isnull().sum()
df.duplicated().sum()
df = df.drop_duplica... | code |
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