path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73064231/cell_9 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_v... | code |
73064231/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(... | code |
73064231/cell_7 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_v... | code |
73064231/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15,5))
X = np.array([[1],[2],[3],[4],[8],[9],[10],[11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 100)
y_v... | code |
73064231/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 2, figsize=(15, 5))
X = np.array([[1], [2], [3], [4], [8], [9], [10], [11]])
Y = np.array([0, 0, 0, 0, 1, 1, 1, 1])
model = LinearRegression()
model.fit(X, Y)
x_values = np.linspace(0, 12, 1... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_items.head() | code |
106194820/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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 |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_28 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_24 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-pr... | code |
106194820/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)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
d_sam_sub = pd.read_cs... | code |
106194820/cell_27 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_items = pd.... | code |
106194820/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
d_item_cat = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
d_item_cat.head() | code |
130026009/cell_9 | [
"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
import seaborn as sns
sns.set()
df = pd.read_csv('/kaggle/input/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
top_artists = df[['artist', 'popularity']]
top_ar... | code |
130026009/cell_6 | [
"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/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
df.head() | code |
130026009/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 |
130026009/cell_8 | [
"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
import seaborn as sns
sns.set()
df = pd.read_csv('/kaggle/input/eurovision-2023-betting-odds/data/spotify/2023-04-13-spotify-streaming.csv')
plt.figure(figsize=(12, 12))
sns.lineplot(x='popu... | code |
130026009/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import seaborn as sns
import seaborn as sns
sns.set() | code |
104120390/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
bdata.marital.value_counts().plot(kind='bar')
plt.title(... | code |
104120390/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/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)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts() | code |
104120390/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import r... | code |
104120390/cell_2 | [
"text_plain_output_1.png"
] | import os
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scipy import stats
import math
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
... | code |
104120390/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/cell_7 | [
"image_output_5.png",
"image_output_4.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts().plot(kind='bar')
plt.title('Job Distribution')
plt.xlabel... | code |
104120390/cell_8 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts() | code |
104120390/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/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)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.head() | code |
104120390/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import random
from scip... | code |
104120390/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.job.value_counts()
bdata.job.value_counts()
bdata.education.value_counts().plot(kind='bar')
plt.titl... | code |
104120390/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #for data visualization.
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (14, 7)})
import r... | code |
104120390/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)
bdata = pd.read_csv('../input/bank-marketing/bank-additional-full.csv', sep=';')
bdata.describe() | code |
90128350/cell_21 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing imp... | code |
90128350/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_20 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing imp... | code |
90128350/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_2 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing imp... | code |
90128350/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_24 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing imp... | code |
90128350/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_37 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Ordina... | code |
90128350/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
90128350/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.p... | code |
129022563/cell_42 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/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)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_9 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_23 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_44 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../in... | code |
129022563/cell_55 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_39 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_26 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_41 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
fig = make_subplots(rows=1, cols=2, column_titles=['Train Data', 'Test Data'], x_title='Missing Values')
fig.show() | code |
129022563/cell_54 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_11 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_50 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_52 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/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 |
129022563/cell_7 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_45 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../in... | code |
129022563/cell_49 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_32 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_51 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder | code |
129022563/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_43 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
129022563/cell_31 | [
"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 = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
STRATEGY = 'me... | code |
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