path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32065763/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
32065763/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
2037113/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-surface-below-ring', data=dframe) | code |
2037113/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='odor', data=dframe) | code |
2037113/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='cap-shape', data=dframe) | code |
2037113/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='veil-color', data=dframe) | code |
2037113/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-color-below-ring', data=dframe) | code |
2037113/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-surface-above-ring', data=dframe) | code |
2037113/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)
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
X.columns
X.info() | code |
2037113/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
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='cap-color', data=dframe) | code |
2037113/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-root', data=dframe) | code |
2037113/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2037113/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-shape', data=dframe) | code |
2037113/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='gill-spacing', data=dframe) | code |
2037113/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='gill-size', data=dframe) | code |
2037113/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)
dframe = pd.read_csv('../input/mushrooms.csv')
dframe.head() | code |
2037113/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='gill-color', data=dframe) | code |
2037113/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)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='veil-type', data=dframe) | code |
2037113/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='gill-attachment', data=dframe) | code |
2037113/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='stalk-color-above-ring', data=dframe) | code |
2037113/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='cap-surface', data=dframe) | code |
2037113/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
sns.countplot(x='bruises', data=dframe) | code |
2037113/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)
dframe = pd.read_csv('../input/mushrooms.csv')
y = dframe['class']
X = dframe.drop('class', axis=1)
X.columns | code |
2043287/cell_13 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
model = Sequential()
model.add(Dense(400, input_dim=784, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(300, activ... | code |
2043287/cell_6 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
model = Sequential()
model.add(Dense(400, input_dim=784, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(300, activ... | code |
2043287/cell_1 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import RMSprop
from keras.utils.np_utils import to_categorical
from sklearn.cross_validation import train_test_split
from subprocess i... | code |
2043287/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.utils.np_utils import to_categorical
from sklearn.cross_validation import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | code |
2043287/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.utils.np_utils import to_categorical
from sklearn.cross_validation import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | code |
2043287/cell_10 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
model = Sequential()
model.add(Dense(400, input_dim=784, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(200, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(300, activ... | code |
90105356/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from scipy.stats import ... | code |
90105356/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from scipy.stats import ... | code |
90105356/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from... | code |
90105356/cell_15 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_out... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from... | code |
90105356/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import shutil
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from... | code |
128016851/cell_25 | [
"image_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = ... | code |
128016851/cell_30 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import m... | code |
128016851/cell_20 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().... | code |
128016851/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128016851/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
imp... | code |
128016851/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 | code |
128016851/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128016851/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
imp... | code |
128016851/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
128016851/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128016851/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128016851/cell_31 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,BaggingRegressor
from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import m... | code |
128016851/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
... | code |
128016851/cell_27 | [
"text_html_output_1.png"
] | from sklearn.metrics import make_scorer,mean_squared_error, r2_score, mean_absolute_error
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
imp... | code |
128016851/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
def summary(df):
result = pd.DataFrame(df.dtypes, columns=['data type'])
result['#duplicate'] = df.duplicated().sum()
result['#missing'] = df.isnull().sum().values
result['#unique'] = df.nunique().values
return result
train_df = pd.read_csv('/kaggle/input/playground-series-s3e1... | code |
74050861/cell_13 | [
"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_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(lambda x: x.isnull().sum() / len(x) * 100)
train_data['... | code |
74050861/cell_9 | [
"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_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any() | code |
74050861/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
test_data.columns | code |
74050861/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape | code |
74050861/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = ... | code |
74050861/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)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(lambda x: x.isnull().sum() / len(x) * 100)
train_data['... | code |
74050861/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/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 |
74050861/cell_7 | [
"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_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.info() | code |
74050861/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/cell_32 | [
"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_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
test_data.columns
test_data = test_data.drop(['Cabin', 'Name', 'Ticket'], axis=1)
test_data.isnull().sum() | code |
74050861/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)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.describe() | code |
74050861/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(lambda x: x.isnull().sum() / len(x) * 100)
train_data['... | code |
74050861/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(... | code |
74050861/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.shape
train_data.isnull().any()
train_data.apply(lambda x: x.isnull().sum() / len(x) * 100) | code |
74050861/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_dat... | code |
74050861/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/titanic/train.csv')
test_data = pd.read_csv('../input/titanic/test.csv')
train_data.head(10) | code |
73067436/cell_20 | [
"text_plain_output_1.png"
] | from circlify import circlify, Circle
from warnings import filterwarnings
import matplotlib.lines as lines
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import ma... | code |
73067436/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
df.info() | code |
73067436/cell_19 | [
"image_output_1.png"
] | !pip install circlify | code |
73067436/cell_8 | [
"image_output_1.png"
] | import seaborn as sns
cmap0 = ['#68595b', '#7098af', '#6f636c', '#907c7b']
cmap1 = ['#484146', '#8da0b3', '#796d72', '#9fa9ba']
cmap2 = ['#545457', '#a79698', '#5284a2', '#bbbcc4']
bg_color = '#fbfbfb'
txt_color = '#5c5c5c'
sns.palplot(cmap0)
sns.palplot(cmap1)
sns.palplot(cmap2) | code |
73067436/cell_15 | [
"image_output_1.png"
] | from warnings import filterwarnings
import matplotlib.lines as lines
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.gridspec as gridspec
import se... | code |
73067436/cell_16 | [
"image_output_1.png"
] | from warnings import filterwarnings
import matplotlib.lines as lines
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.gridspec as gridspec
import se... | code |
73067436/cell_10 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/predict-test-scores-of-students/test_scores.csv')
print(f'Shape: {df.shape}')
print('--' * 20)
df.head(3) | code |
73067436/cell_12 | [
"text_plain_output_1.png"
] | from warnings import filterwarnings
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.gridspec as gridspec
import seaborn as sns
from warnings import filterwarnings
filte... | code |
33115163/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
train.info() | code |
33115163/cell_57 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
model_1 = Sequential()
model_1.add(Dense(25, input_dim=20, activation='relu'))
model_1.add(Dense(25, activation='relu'))
model_1.add(Dense(4, activation='softmax'))
model_1.summary()
model_1.compile(optimizer='adam', loss='sparse_categorical_crossent... | code |
33115163/cell_56 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
model_1 = Sequential()
model_1.add(Dense(25, input_dim=20, activation='relu'))
model_1.add(Dense(25, activation='relu'))
model_1.add(Dense(4, activation='softmax'))
model_1.summary() | code |
33115163/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_44 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_... | code |
33115163/cell_55 | [
"text_html_output_1.png"
] | import tensorflow.keras
from keras.models import Sequential
from keras.layers import Dense | code |
33115163/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
train.head() | code |
33115163/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
33115163/cell_61 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
test = pd.read_csv('../input/mobi... | code |
33115163/cell_60 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
model_1 = Sequential()
model_1.add(Dense(25, input_dim=20, activation='relu'))
model_1.add(Dense(25, activation='relu'))
model_1.add(Dense(4, activation='softmax'))
model_1.summary()
model_1.compile(optimizer='adam', loss='sparse_categorical_crossent... | code |
33115163/cell_50 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classificat... | code |
33115163/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
test.head() | code |
33115163/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory',... | code |
33115163/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width'... | code |
33115163/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classificat... | code |
33115163/cell_59 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'cloc... | code |
33115163/cell_15 | [
"text_html_output_1.png"
] | numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time']
categorical = ['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi']
print(len(numerical))
print(len(categorical)) | code |
33115163/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', '... | code |
33115163/cell_47 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerica... | code |
33115163/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
test = pd.read_csv('../input/mobile-price-classification/test.csv')
train = pd.read_csv('../input/mobile-price-classification/train.csv')
numerical = ['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', ... | code |
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