path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72062410/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen.info() | code |
72062410/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('... | code |
72062410/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 |
72062410/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen['Stars'] | code |
72062410/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated['rating']... | code |
72062410/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen[['Stars']] | code |
72062410/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen_drop_unrated = ramen.copy()
ramen_convert_unrated = ramen.copy()
ramen_drop_unrated = ramen_drop_unrated[ramen_drop_unrated['Stars'] != 'Unrated']
ramen_drop_unrated.groupby('... | code |
72062410/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
ramen['Style'].unique() | code |
72062410/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
ramen = pd.read_csv('../input/ramen-ratings/ramen-ratings.csv')
import seaborn as sns
sns.countplot(x='Style', data=ramen) | code |
72062410/cell_5 | [
"text_plain_output_1.png"
] | farbe = 'grün'
farbe = 'blau'
print(farbe) | code |
130014142/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
data.hist(figsize=(10, 10)) | code |
130014142/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler,OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
one_hot = OneHotEncoder()
ss = StandardScaler()
def t... | code |
130014142/cell_30 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,accuracy_score
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
def performance(y_test, pred, model_name):
p... | code |
130014142/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler,OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
target = data.pop('income')
data = data.drop('relationship', axis=1)
one_hot = OneHotEncoder()
ss = StandardScaler()
def t... | code |
130014142/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns | code |
130014142/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix,precision_score,recall_score,f1_score,accuracy_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
def performance(y_test, ... | code |
130014142/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
svm = SVC()
svm.fit(x_train, y_train) | code |
130014142/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | txt_col = ['workclass', 'education', 'marital.status', 'occupation', 'relationship_change', 'race', 'sex', 'native.country']
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
num_col = ['age', 'fnlwgt', 'education.num', 'capital.gain', 'capital.loss', 'hours.per.week... | code |
130014142/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
data | code |
130014142/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/adult-census-income/adult.csv')
sns.heatmap(data.corr(), cmap='Blues', annot=True) | code |
90108440/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 |
90108440/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
plt.imshow(num[0], cmap='inferno') | code |
324025/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import xgboost as xgb
dtrain = xgb.DMatrix(X_train, y_train)
dvalid = xgb.DMatrix(X_test, y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
params = {'objective': 'reg:linear', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'silent': 1, 'nthread': 1... | code |
324025/cell_2 | [
"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 |
324025/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import xgboost as xgb
from sklearn import datasets
from sklearn.cross_validation import train_test_split | code |
324025/cell_5 | [
"text_plain_output_1.png"
] | import xgboost as xgb
dtrain = xgb.DMatrix(X_train, y_train)
dvalid = xgb.DMatrix(X_test, y_test)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
params = {'objective': 'reg:linear', 'eval_metric': 'rmse', 'eta': 0.01, 'max_depth': 6, 'silent': 1, 'nthread': 1}
num_boost_round = 100
gbm = xgb.train(params, dtrain, n... | code |
128035508/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
tree_regressor = DecisionTreeRegressor(random_state=0)
tree_regressor.fit(X_train, Y_train) | code |
128035508/cell_6 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
Logistic_R = LogisticRegression()
Logistic_R.fit(X_train, Y_train) | code |
128035508/cell_7 | [
"text_html_output_1.png"
] | from sklearn.svm import SVR
svr_regressor = SVR(kernel='rbf', gamma='auto')
svr_regressor.fit(X_train, Y_train) | code |
128035508/cell_18 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
import pickle
knn = KNeighborsRegressor(n_neighbors=2)
knn.fit(X_trai... | code |
128035508/cell_8 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression,LogisticRegression
lr = LinearRegression()
lr.fit(X_train, Y_train) | code |
128035508/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
forest_regressor = RandomForestRegressor(n_estimators=300, random_state=0)
forest_regressor.fit(X_train, Y_train) | code |
128035508/cell_5 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor(n_neighbors=2)
knn.fit(X_train, Y_train) | code |
90129425/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)
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.head() | code |
90129425/cell_34 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_30 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_40 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_26 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_18 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_32 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_28 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
from sklearn.model_selection import train_test_split
from... | code |
90129425/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_22 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_10 | [
"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/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.info() | code |
90129425/cell_27 | [
"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
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T... | code |
90129425/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
data.describe().T | code |
74051961/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique() | code |
74051961/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape | code |
74051961/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
treatment.shape[0] | code |
74051961/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 ... | code |
74051961/cell_44 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_style('white')
plt.rc('axes', titlesize=13)
plt.rc('axes', labelsize=12)
plt.rc('xtick', labelsize=11)
plt.rc('ytick', labelsize=11)
plt.rc('legend', fontsize=11)
plt.rc('font', size=10)
path = '../input/mobile-gam... | code |
74051961/cell_40 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style('white')
plt.rc('axes', titlesize=13)
plt.rc('axes', labelsize=12)
plt.rc('xtick', labelsize=11)
plt.rc('ytick', labelsize=11)
plt.rc('legend', fontsize=11)
plt.rc('font', size=10)
path = '../input/mobile-games-ab-testing/cookie... | code |
74051961/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
control = df[df['version'] == 'gate_30']
control.shape[0] | code |
74051961/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 ... | code |
74051961/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.describe() | code |
74051961/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes | code |
74051961/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.info() | code |
74051961/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control... | code |
74051961/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 ... | code |
74051961/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
if df.userid.nunique() == df.shape[0]:
print('There are no duplicated user ids in the dataset')
else:
print('There are some duplicated user ids in the dataset') | code |
74051961/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.shape
df.userid = df.userid.astype(str)
df.userid.dtypes
df.version.unique()
treatment = df[df['version'] == 'gate_40']
control = df[df['version'] == 'gate_30']
treatment.shape[0]
control.shape[0]
contr_d1 ... | code |
74051961/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/mobile-games-ab-testing/cookie_cats.csv'
df = pd.read_csv(path)
df.head() | code |
122245085/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count') | code |
122245085/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df[... | code |
122245085/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.head() | code |
122245085/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - ... | code |
122245085/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)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.describe() | code |
122245085/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import iplot, init_notebook_mode, download_plotlyjs, plot
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import matplotlib.pyplot as plt
import seaborn a... | code |
122245085/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape | code |
122245085/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count')
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.7... | code |
122245085/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - ... | code |
122245085/cell_8 | [
"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 plotly.express as px
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count']) | code |
122245085/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
df = df[(df[... | code |
122245085/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - ... | code |
122245085/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
Q1 = df['count'].quantile(0.25)
Q3 = df['count'].quantile(0.75)
IQR = Q3 - Q1
upper_fence = Q3 + 1.5 * IQR
lower_fence = Q1 - 1.5 * IQR
print('Inter... | code |
122245085/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.info() | code |
122245085/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/yulu-bike-sharing-data/yulu_bike_sharing_dataset.csv')
df.shape
px.box(df['count'])
px.box(df, x='workingday', y='count')
Q1 = df['count'].quantile(0.25)
Q3 = d... | code |
2014823/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.... | code |
2014823/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.info() | code |
2014823/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2014823/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((tra... | code |
2014823/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
2014823/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import graphviz
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.conca... | code |
2014823/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.Age = all_data.Age.fillna(all_data.Age.median())
all_data.Fare ... | code |
72073997/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72073997/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sub | code |
72073997/cell_25 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72073997/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.head() | code |
72073997/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = ... | code |
72073997/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/... | code |
72073997/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.head() | code |
72073997/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
t... | code |
72073997/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read... | code |
72073997/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-... | code |
72073997/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
72073997/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.info() | code |
72073997/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sub = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train = train.drop('id', axis=1)
test = test.drop('id', axis=1)
cols = test.columns
X_train = train[cols]
X_test = test.... | code |
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