path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128042012/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df.describe() | code |
128042012/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df_100 = df[df['Flight Distance'] > 100]
df_100[df_100... | code |
128042012/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
... | code |
128042012/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.e... | code |
128042012/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df_100 = df[df['Flight Distance'] > 100]
df_100.select... | code |
128042012/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df_100 = df[df['Flight Distance'] > 100]
df_100.select... | code |
128042012/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_cs... | code |
128042012/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
test_set.info() | code |
128042012/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')
df = train_set.dropna()
df_100 = df[df['Flight... | code |
129033753/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Dat... | code |
129033753/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
print(DDoS_PortScan.shape)
print(Label.shape) | code |
129033753/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Data = np.array(DDoS_PortSca... | code |
129033753/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Data = np.array(DDoS_PortSca... | code |
129033753/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing
import tensorflow as tf
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import numpy as np
import pandas as pd | code |
129033753/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Data = np.array(DDoS_PortSca... | code |
129033753/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Data = np.array(DDoS_PortSca... | code |
129033753/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan | code |
129033753/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Data = np.array(DDoS_PortSca... | code |
129033753/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
DDoS_PortScan = pd.read_csv('/kaggle/input/ddos-portscan/DDoS_PortScan.csv')
Label = DDoS_PortScan.loc[:, ' Label']
DDoS_PortScan.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
Label.replace({'DDoS': 0, 'PortScan': 1}, inplace=True)
DDoS_PortScan_Dat... | code |
74067689/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train)
print('Training Accuracy: ', classifier.score(X_train, y_train))
print('Testing Accuracy: ', classifier.score(X_test, y_test)) | code |
74067689/cell_25 | [
"text_plain_output_1.png"
] | y_test[3] | code |
74067689/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head() | code |
74067689/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train) | code |
74067689/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(f'X_train: {X_train.shape}')
print(f'X_test: {X_test.shape}') | code |
74067689/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.quality.hist() | code |
74067689/cell_16 | [
"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
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5,5))
sns.barplot(x='quality', y='volatile acidity', data=df)
figure = plt.figur... | code |
74067689/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
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 sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(... | code |
74067689/cell_14 | [
"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
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5,5))
sns.barplot(x='quality', y='volatile acidity', data=df)
figure = plt.figur... | code |
74067689/cell_10 | [
"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 = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5, 5))
sns.barplot(x='quality', y='volatile acidity', data=df) | code |
74067689/cell_12 | [
"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 = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
fig = plt.figure(figsize=(5,5))
sns.barplot(x='quality', y='volatile acidity', data=df)
sns.barplot(x='qua... | code |
74067689/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('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.describe() | code |
32068663/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
32068663/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
32068663/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"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)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def impute(df):
df['Province_State'] = df['Province_State'].ma... | code |
32068663/cell_11 | [
"text_plain_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
32068663/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 |
32068663/cell_8 | [
"text_html_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
32068663/cell_3 | [
"application_vnd.jupyter.stderr_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)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train.info() | code |
32068663/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)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
def impute(df):
df['Province_State'] = df['Province_State'].ma... | code |
32068663/cell_10 | [
"text_plain_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
32068663/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import itertools
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 statsmodels.api as sm
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19... | code |
50218788/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 |
105194319/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
plt.scatter(clusters['tatmin'], clusters[... | code |
105194319/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
data.head() | code |
105194319/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
from sklearn import preprocessing
x_sca... | code |
105194319/cell_7 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x) | code |
105194319/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
from s... | code |
105194319/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
from sklearn import preprocessing
x_sca... | code |
105194319/cell_10 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
from sklearn import preprocessing
x_sca... | code |
105194319/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
x = data.copy()
kmeans = KMeans(2)
kmeans.fit(x)
clusters = x.copy()
clusters['kume_tahmin'] = kmeans.fit_predict(x)
from s... | code |
105194319/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/market-sadakatcsv/Market_Sadakat.csv')
plt.scatter(data['tatmin'], data['sadakat'])
plt.xlabel('Tatmin')
plt.ylabel('Sadakat') | code |
72092655/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_4 | [
"image_output_11.png",
"text_plain_output_5.png",
"image_output_17.png",
"text_html_output_4.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_20.png",
"tex... | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
obj... | code |
72092655/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"im... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[dat... | code |
72092655/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
obj... | code |
89138938/cell_13 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
... | code |
89138938/cell_9 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
... | code |
89138938/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
try:
img... | code |
89138938/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
try:
img... | code |
89138938/cell_11 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
... | code |
89138938/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
... | code |
89138938/cell_12 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
image_size = 150
labels = ['PNEUMONIA', 'NORMAL']
def get_data(path):
data = list()
for label in labels:
image_dir = os.path.join(path, label)
class_num = labels.index(label)
for img in os.listdir(image_dir):
... | code |
17102038/cell_21 | [
"text_plain_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import keras
import matplotlib.pyplot as plt
import pandas as pd
import ... | code |
17102038/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormaliza... | code |
17102038/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ... | code |
17102038/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import keras
model = Sequential()
model.add(Conv2D(filters=32, kernel_size... | code |
17102038/cell_18 | [
"image_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import keras
model = Sequential()
model.add(Conv2D(filters=32, kernel_size... | code |
17102038/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import keras
model = Sequential()
model.add(Conv2D(filters=32, kernel_size... | code |
17102038/cell_16 | [
"text_plain_output_1.png"
] | from keras.callbacks import ReduceLROnPlateau
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import keras
model = Sequential()
model.add(Conv2D(filters=32, kernel_size... | code |
17102038/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ... | code |
74054733/cell_5 | [
"text_plain_output_1.png"
] | ver = read_kernel_versions()
ver.columns | code |
1008459/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']]
data.loc[(data.id == 1201) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['... | code |
1008459/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_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']] | code |
1008459/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_hdf('../input/train.h5') | code |
1008459/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_hdf('../input/train.h5')
data.technical_16.describe() | code |
32071115/cell_13 | [
"text_html_output_1.png"
] | from tqdm.notebook import tqdm
import pandas as pd
import requests
def get_restcountries(countries):
"""Retrieve all available fields from restcountries API
https://github.com/apilayer/restcountries#response-example"""
api = 'https://restcountries.eu/rest/v2'
rdfs = []
for country in tqdm(countri... | code |
32071115/cell_15 | [
"text_html_output_1.png"
] | from tqdm.notebook import tqdm
import pandas as pd
import requests
def get_restcountries(countries):
"""Retrieve all available fields from restcountries API
https://github.com/apilayer/restcountries#response-example"""
api = 'https://restcountries.eu/rest/v2'
rdfs = []
for country in tqdm(countri... | code |
32071115/cell_17 | [
"text_plain_output_1.png"
] | from tqdm.notebook import tqdm
import pandas as pd
import requests
def get_restcountries(countries):
"""Retrieve all available fields from restcountries API
https://github.com/apilayer/restcountries#response-example"""
api = 'https://restcountries.eu/rest/v2'
rdfs = []
for country in tqdm(countri... | code |
32071115/cell_12 | [
"text_html_output_1.png"
] | from tqdm.notebook import tqdm
import pandas as pd
import requests
def get_restcountries(countries):
"""Retrieve all available fields from restcountries API
https://github.com/apilayer/restcountries#response-example"""
api = 'https://restcountries.eu/rest/v2'
rdfs = []
for country in tqdm(countri... | code |
331254/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
plt.figure(figsize=(45, 10))
sns.stripplot('year', 'Fata... | code |
331254/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
frame.head() | code |
331254/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
plt.figure(figsize=(45, 10))
sns.barplot('year', 'Aboard... | code |
331254/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
plt.figure(figsize=(45, 10))
sns.barplot('year', 'Fatali... | code |
331254/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
plt.figure(figsize=(100, 10))
sns.barplot('year', 'Aboar... | code |
331254/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib
from sklearn.linear_model import LinearRegression
frame = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
frame['year'].head() | code |
105192049/cell_4 | [
"text_plain_output_1.png"
] | from gekko import GEKKO
from gekko import GEKKO
from gekko import GEKKO
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
imp... | code |
105192049/cell_2 | [
"text_plain_output_1.png"
] | from gekko import GEKKO
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import numpy as np
import pandas as pd
from gekko import GEKKO
import time
m = GEKKO()
m.options.SOLVER = 1
m.solver_options = ['minlp_maximum_iterations 500', 'minlp_max_iter_with_int_sol 10', 'minlp_as_nlp 0'... | code |
105192049/cell_1 | [
"text_plain_output_1.png"
] | !pip install gekko | code |
105192049/cell_3 | [
"text_plain_output_1.png"
] | from gekko import GEKKO
from gekko import GEKKO
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
import numpy as np
import pandas as pd
from gekko import GEKKO
import time
m = GEKKO()
m.options.SOL... | code |
49120672/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.io.collection import ImageCollection
import numpy as np # linear algebra
ic = ImageCollection('../input/cassava-leaf-disease-classification/train_images/1000*.jpg')
i = 0
for pic in ic:
print('{} \nPic type: {} \nPic shape: {} \n\n'.format(i, type(pic), np.shape(pic)))
i += 1 | code |
49120672/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
disease_numbers = pd.read_json('../input/cassava-leaf-disease-classification/label_num_to_disease_map.json', orient='index')
sample_submission = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')
train = pd.read_csv('... | code |
49120672/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
disease_numbers = pd.read_json('../input/cassava-leaf-disease-classification/label_num_to_disease_map.json', orient='index')
sample_submission = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv'... | code |
49120672/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 |
49120672/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
disease_numbers = pd.read_json('../input/cassava-leaf-disease-classification/label_num_to_disease_map.json', orient='index')
sample_submission = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')
train = pd.read_csv('... | code |
49120672/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
disease_numbers = pd.read_json('../input/cassava-leaf-disease-classification/label_num_to_disease_map.json', orient='index')
sample_submission = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')
train = pd.read_csv('... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.