path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2014006/cell_9 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png"... | from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in came... | code |
2014006/cell_4 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
2014006/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import os
from pathlib import Path
import multiprocessing as mp
import numpy as np
import pandas as pd
from skimage.data import imread
from sklearn.ensemble import RandomForestClassifier
import time
import cv2
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from su... | code |
2014006/cell_7 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in came... | code |
2014006/cell_5 | [
"text_plain_output_1.png"
] | from pathlib import Path
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
input_path = Path('../input')
train_path = input_path / 'train'
test_path = input_path / 'test'
cameras = os.listdir(train_path)
train_images = []
for camera in cameras:
for fname in sorted(os.listdir(train... | code |
128020766/cell_21 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_34 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from termcolor import colored
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
impo... | code |
128020766/cell_6 | [
"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 |
128020766/cell_29 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.... | code |
128020766/cell_26 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.... | code |
128020766/cell_7 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_18 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_24 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_22 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
128020766/cell_27 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.preprocessing import LabelEncoder, StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.... | code |
128020766/cell_12 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_ob... | code |
105200060/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi =... | code |
105200060/cell_6 | [
"image_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi_x = bmi.Weight[:, np.newaxis]
print(bmi_x) | code |
105200060/cell_2 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi.head() | code |
105200060/cell_11 | [
"text_html_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi... | code |
105200060/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
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirn... | code |
105200060/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi... | code |
105200060/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('.... | code |
105200060/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
print(bmi.keys()) | code |
105200060/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import datasets, linear_model
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi... | code |
105200060/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error
import os
bmi = pd.read_csv('../input/bmidataset/bmi.csv')
bmi_x = bmi.Weight[:, np.newaxis] | code |
104127328/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
demo_data = pd.read_csv('/kaggle/input/pac-regression-4/PAC1954_Demo_default_data.csv')
num_samples = demo_data.shape[0]
split = round(num_samples * 0.7)
train_demo_data_x = np.a... | code |
104127328/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
demo_data = pd.read_csv('/kaggle/input/pac-regression-4/PAC1954_Demo_default_data.csv')
num_samples = demo_data.shape[0]
split = round(num_samples * 0.7)
train_demo_data_x = np.a... | code |
104127328/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
demo_data = pd.read_csv('/kaggle/input/pac-regression-4/PAC1954_Demo_default_data.csv')
num_samples = demo_data.shape[0]
split = round(num_samples * 0.7)
train_demo_data_x = np.a... | code |
104127328/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
demo_data = pd.read_csv('/kaggle/input/pac-regression-4/PAC1954_Demo_default_data.csv')
num_samples = demo_data.shape[0]
split = round(num_samples * 0.7)
train_demo_data_x = np.a... | code |
104127328/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
demo_data = pd.read_csv('/kaggle/input/pac-regression-4/PAC1954_Demo_default_data.csv')
num_samples = demo_data.shape[0]
split = round(num_samples * 0.7)
train_demo_data_x = np.a... | code |
1008698/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from collections import OrderedDict
import csv
import csv
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import csv
from collections import OrderedDict
import pan... | code |
1008698/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 |
1008698/cell_3 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
import csv
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import csv
from collections import OrderedDict
import pandas as pd
with open('../input/documents_meta.csv', 'r') as f:
r = ... | code |
73073830/cell_21 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_25 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_23 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_33 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_20 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_6 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_29 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_26 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_19 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/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 |
73073830/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
total = train.isnull().sum().sort_values(ascending=False)
percent =... | code |
73073830/cell_18 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/trai... | code |
73073830/cell_32 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_16 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
total = train.isnull().sum().sort_values(ascending=False)
percent =... | code |
73073830/cell_17 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_log_error
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_log_error
logreg = LogisticRegression(random_state=0, solver='liblinear').fit(X_train, y_train)
y_pred = logreg.predict(X_... | code |
73073830/cell_24 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_14 | [
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/trai... | code |
73073830/cell_22 | [
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_27 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_12 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/trai... | code |
73073830/cell_5 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.stats import norm
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 warnings
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/i... | code |
73073830/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_log_error
from xgboost import XGBRegressor
from xgboost import XGBRegressor
cat1 = XGBRegressor(n_estimators=500, learning_rate=0.1, early_stopping_rounds=5, max_depth=3).fit(X_train, y_train)
y_cat = cat1.predict(X_test)
mean_squared_log_error(y_test, y_cat) | code |
122256334/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.duplicated().sum() | code |
122256334/cell_9 | [
"image_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.head() | code |
122256334/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
d... | code |
122256334/cell_6 | [
"image_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
mat_student.head() | code |
122256334/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
d... | code |
122256334/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
d... | code |
122256334/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.info() | code |
122256334/cell_7 | [
"image_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
print(portugese_student.columns)
print(mat_student.columns) | code |
122256334/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.duplica... | code |
122256334/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.duplicated().sum()
data.isnull().sum()
... | code |
122256334/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
d... | code |
122256334/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.duplicated().sum()
data.isnull().sum() | code |
122256334/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
d... | code |
122256334/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape | code |
122256334/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
mat_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-mat.csv')
data = pd.concat([portugese_student, mat_student], axis=0)
data.shape
data.describe().transpose() | code |
122256334/cell_5 | [
"image_output_1.png"
] | import pandas as pd
portugese_student = pd.read_csv('/kaggle/input/student-alcohol-consumption/student-por.csv')
portugese_student.head(5) | code |
17130585/cell_21 | [
"text_plain_output_1.png"
] | import shutil
import shutil
shutil.make_archive('images', 'zip', '../gen_images/') | code |
17130585/cell_9 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array, load_img
temp_img = load_img('../input/all-dogs/all-dogs/n02085620_10074.jpg')
temp_img_array = img_to_array(temp_img)
temp_img | code |
17130585/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Dense, Activation, Reshape
from keras.layers import Flatten, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.layers.normalization import BatchNormalization
from keras.models import... | code |
17130585/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
img_list = os.listdir('../input/all-dogs/all-dogs/')
len(img_list) | code |
17130585/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/all-dogs/')) | code |
17130585/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras.layers import Dense, Activation, Reshape
from keras.layers import Flatten, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.layers.normalization import BatchNormalization
from keras.models import... | code |
17130585/cell_18 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation, Reshape
from keras.layers import Flatten, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras... | code |
17130585/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing.image import img_to_array, load_img
import math
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import os
img_list = os.listdir('../input/all-dogs/all-dogs/')
def combine_images(gene... | code |
17130585/cell_3 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense, Activation, Reshape
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers import Flatten, Dropout
from keras.prepr... | code |
17130585/cell_10 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import img_to_array, load_img
temp_img = load_img('../input/all-dogs/all-dogs/n02085620_10074.jpg')
temp_img_array = img_to_array(temp_img)
temp_img_array.shape | code |
17130585/cell_5 | [
"text_plain_output_100.png",
"application_vnd.jupyter.stderr_output_145.png",
"text_plain_output_84.png",
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_115.png",
"text_plain_output_56.png",
"text_plain_output_158.png",
"application_vnd.jupyter.stderr_output_35.... | import os
import os
import numpy as np
import pandas as pd
import os
img_list = os.listdir('../input/all-dogs/all-dogs/')
print(os.listdir('../working')) | code |
89142701/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.info() | code |
89142701/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.describe() | code |
89142701/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.shape | code |
89142701/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/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 |
89142701/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
89142701/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.info() | code |
89142701/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('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.isnull().sum()
test.head() | code |
89142701/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.isnull().sum()
test.shape | code |
89142701/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train... | code |
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