path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50245049/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import SelectKBest, chi2
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.... | code |
50245049/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = df.apply(LabelEncoder().fit_transform)
... | code |
50245049/cell_25 | [
"text_html_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
tsne_data = np.vstack((tsne_model.T, y)).T
tsne_df = pd.DataFrame(data=tsne_data, columns=('Dimension 1', 'Dimension 2', 'Class'))
sns.FacetGrid(tsne_df, height=8, hue='Class').map(plt.scatter, 'Dimension 1', 'Dimension 2').add_legend() | code |
50245049/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
df.head() | code |
50245049/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = df.apply(LabelEncoder().fit_transform)
... | code |
50245049/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mu... | code |
50245049/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(random_state=0, max_depth=5)
dt.fit(x_train, y_train)
dt.score(x_train, y_train) | code |
50245049/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth=5)
rf.fit(x_train, y_train)
rf.score(x_train, y_train)
predictions = rf.predict(x_test)
rf.score(x_test, y_test)
rf.feature_importances_.shape | code |
50245049/cell_26 | [
"image_output_1.png"
] | from sklearn.feature_selection import SelectKBest, chi2
from sklearn.manifold import TSNE
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import panda... | code |
50245049/cell_2 | [
"image_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 |
50245049/cell_19 | [
"image_output_1.png"
] | from sklearn.feature_selection import SelectKBest, chi2
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.... | code |
50245049/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.p... | code |
50245049/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(random_state=0, max_depth=5)
dt.fit(x_train, y_train) | code |
50245049/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = df.app... | code |
50245049/cell_16 | [
"text_html_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
tsne_data = pd.DataFrame(PCs_3d)
tsne_data['class'] = df['class']
ax2 = tsne_data.plot.scatter(x='PC1_3d', y='PC3_3d', c='class', colormap='viridis') | code |
50245049/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.p... | code |
50245049/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv'... | code |
50245049/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth=5)
rf.fit(x_train, y_train)
rf.score(x_train, y_train) | code |
50245049/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(random_state=0, max_depth=5)
dt.fit(x_train, y_train)
dt.score(x_train, y_train)
predictions = dt.predict(x_test)
from sklearn.metrics import accura... | code |
50245049/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = df.apply(LabelEncoder().fit_transform)
... | code |
50245049/cell_37 | [
"text_plain_output_1.png"
] | print() | code |
50245049/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncode... | code |
50245049/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/mushroom-classification/mushrooms.csv')
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df = df.apply(LabelEncoder().fit_transform)
... | code |
50245049/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth=5)
rf.fit(x_train, y_train)
rf.score(x_train, y_train)
predictions = rf.predict(x_test)
rf.score(x_test, y_test) | code |
16136430/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
splist = []
flat = []
cuisineList = data['cuisines'].dropna(axis=0, inplace=False)
for i in range(0, cuisineList.coun... | code |
16136430/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
_data.head() | code |
16136430/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16136430/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
_data = pd.read_csv('../input/zomato.csv')
print('Original set of columns:{}'.format(_data.columns))
data = _data.drop(columns=['url', 'address', 'phone'], axis=1)
columns = data.columns
print('New columns : {}'.format(columns)) | code |
48164526/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis='columns', inplace=True)
categorical_col = []
for column ... | code |
48164526/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis='columns', inplace=True)
categorical_col = []
for column ... | code |
48164526/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
f... | code |
48164526/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df.head() | code |
48164526/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython.display import Image
from sklearn.externals.six import StringIO
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
fro... | code |
48164526/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df.info() | code |
48164526/cell_29 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount',... | code |
48164526/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.describe() | code |
48164526/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format... | code |
48164526/cell_38 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree impor... | code |
48164526/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis='c... | code |
48164526/cell_35 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import RandomizedSearchCV
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/... | code |
48164526/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis='c... | code |
48164526/cell_27 | [
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset... | code |
48164526/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
pd.set_option('display.float_format', '{:.2f}'.format)
df.drop(['EmployeeCount', 'EmployeeNumber', 'Over18', 'StandardHours'], axis='columns', inplace=True)
categorical_col = []
for column ... | code |
1009348/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # combined plotting
df = pd.read_csv('../input/train.csv', index_col=0)
df_test = pd.read_csv('../input/test.csv', index_col=0)
df.dtypes
def cond_hists(df, plot_cols, grid_col):
... | code |
1009348/cell_6 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', index_col=0)
df_test = pd.read_csv('../input/test.csv', index_col=0)
df.head(5) | code |
1009348/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', index_col=0)
df_test = pd.read_csv('../input/test.csv', index_col=0)
df.dtypes | code |
1009348/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv', index_col=0)
df_test = pd.read_csv('../input/test.csv', index_col=0)
df.dtypes
df.Survived.hist() | code |
1009348/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # combined plotting
df = pd.read_csv('../input/train.csv', index_col=0)
df_test = pd.read_csv('../input/test.csv', index_col=0)
df.dtypes
def cond_hists(df, plot_cols, grid_col):
... | code |
2004114/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
y = data.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF']
X = data[predicators]
from sklearn.tree import DecisionTreeRegressor
housing_model... | code |
2004114/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
print(data.columns) | code |
2004114/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
y = data.SalePrice
predicators = ['YearBuilt', 'YrSol... | code |
2004114/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForest... | code |
2004114/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForest... | code |
2004114/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
y = data.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF']
X = data[predicators]
from sklearn.tree import DecisionTreeRegressor
housing_model... | code |
2004114/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
forest_model = RandomF... | code |
2004114/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
col_interest = ['ScreenPorch', 'MoSold']
sa = data[col_interest]
sa.describe() | code |
2004114/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
def get_mae(max_leaf_nodes, pre... | code |
2004114/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
import pandas as pd
import pandas as pd
main_file_path = '../input/train.csv'
data = pd.read_csv(main_file_path)
y = data.SalePrice
predicators = ['YearBuilt', 'YrSold', 'TotalBsmtSF']
X = data[predicators]
from sklear... | code |
73100727/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum()
train_drop_target = train_data.dr... | code |
73100727/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.head() | code |
73100727/cell_25 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data... | code |
73100727/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum()
train_drop_target = train_data.dr... | code |
73100727/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
print(f' test_data : {test_data.shape}, \n train_data: {trai... | code |
73100727/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data... | code |
73100727/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 |
73100727/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum()
def print_cat_columns(dataset):
... | code |
73100727/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
def train_the_model(m_d, n, ran):
model = RandomForestRegressor(max_depth=m_d, n_estimators=n, random_state=ran, n_jobs=-1)
return model
ran = 0
n = 1100
m_d = 500
model = train_the_model(m_d, n, ran)
predict_0 ... | code |
73100727/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test_data.head() | code |
73100727/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum()
print(test_data.columns)
print('\... | code |
73100727/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum()
train_drop_target = train_data.dr... | code |
73100727/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
test_data = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
train_data.isnull().sum() | code |
2013071/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | train_xyz | code |
2013071/cell_25 | [
"text_plain_output_1.png"
] | from numpy.linalg import inv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f... | code |
2013071/cell_34 | [
"text_plain_output_1.png"
] | from numpy.linalg import inv
import networkx as nx
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
w... | code |
2013071/cell_30 | [
"text_plain_output_1.png"
] | from numpy.linalg import inv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f... | code |
2013071/cell_20 | [
"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_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
lattice_columns = ['lattice_vector_1_ang', 'lattice_vector_2_ang', 'lattice_vector_3_ang', 'lattice_angle_alpha_degree', '... | code |
2013071/cell_26 | [
"text_plain_output_1.png"
] | from numpy.linalg import inv
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f... | code |
2013071/cell_11 | [
"text_plain_output_1.png"
] | train_lat | code |
2013071/cell_19 | [
"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_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readli... | code |
2013071/cell_18 | [
"text_plain_output_1.png"
] | test_lat | code |
2013071/cell_17 | [
"text_plain_output_1.png"
] | test_xyz | code |
2013071/cell_31 | [
"text_plain_output_1.png"
] | from numpy.linalg import inv
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_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
w... | code |
2013071/cell_24 | [
"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_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readli... | code |
2013071/cell_14 | [
"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_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_data = []
lat_data = []
with open(filename) as f:
for line in f.readli... | code |
2013071/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
lattice_columns = ['lattice_vector_1_ang', 'lattice_vector_2_ang', 'lattice_vector_3_ang', 'lattice_angle_alpha_degree', 'lattice_angle_beta_degree', 'lattice_... | code |
2013071/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from numpy.linalg import inv
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
def get_xyz_data(filename):
pos_... | code |
90137157/cell_17 | [
"text_plain_output_1.png"
] | from bs4 import BeautifulSoup
from html import unescape
import csv
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import csv
import gc
from pathlib import Path
columns = ['tweetcreatedts', 'extractedts', 'userid', 'tweetid', 'text', 'language']
dataframe_collection = []
csvfile = ... | code |
90137157/cell_37 | [
"image_output_1.png"
] | import csv
import matplotlib.pyplot as plt # for wordclouds & charts
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import csv
import gc
from pathlib import Path
columns = ['tweetcreatedts', 'extractedts', 'userid', 'tweetid', 'text', 'language']
dataframe_collection = []
csvfile = '/kaggle... | code |
90137157/cell_5 | [
"image_output_1.png"
] | import csv
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import csv
import gc
from pathlib import Path
columns = ['tweetcreatedts', 'extractedts', 'userid', 'tweetid', 'text', 'language']
dataframe_collection = []
csvfile = '/kaggle/input/ukraine-russian-crisis-twitter-dataset-1-2-m-rows/Ukra... | code |
128011216/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from pandas import crosstab
from pyclustering.cluster.kmeans import kmeans
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.metrics import adjusted_rand_score
import numpy as np
iris = load_iris()
X = iris['data']
y = iris['target']
pca = PCA(n_components=2)
X_pca = pca.fi... | code |
128011216/cell_2 | [
"image_output_1.png"
] | pip install pyclustering; | code |
128011216/cell_11 | [
"text_plain_output_1.png"
] | from pandas import crosstab
from pyclustering.cluster.kmeans import kmeans
from pyclustering.cluster.kmedians import kmedians
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.metrics import adjusted_rand_score
import numpy as np
iris = load_iris()
X = iris['data']
y = iri... | code |
128011216/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import numpy as np
iris = load_iris()
X = iris['data']
y = iris['target']
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
X_scaled = X_pca
ax = plt.axes()
cor = ['blue', 'red', 'green']
for i in range(3):
idx = np.where(y == i)
... | code |
72113568/cell_9 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.m... | code |
72113568/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.max_columns', 500)
import os
df =... | code |
72113568/cell_2 | [
"image_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.max_columns', 500)
import os
df = pd.read_csv('../input/wuzzuf-job... | code |
72113568/cell_11 | [
"text_html_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.m... | code |
72113568/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.max_columns', 500)
import os
for dirname, _, filenames in os.walk('... | code |
72113568/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
pd.set_option('display.max_columns', 500)
import os
df =... | code |
104115135/cell_42 | [
"image_output_1.png"
] | from collections import Counter
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascend... | code |
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