path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
104115135/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.c... | code |
104115135/cell_30 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variabl... | code |
104115135/cell_29 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.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
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.c... | code |
104115135/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head(10) | code |
104115135/cell_45 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009... | code |
104115135/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
for variable in df:
diagnos... | code |
104115135/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape | code |
104115135/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
plt.figure(1, figsize=(10, 10))
df['quality'].value_counts().plot.pie(autopct='%1.1f%%')
plt.show() | code |
104115135/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False) | code |
104115135/cell_3 | [
"image_output_1.png"
] | pip install feature-engine | code |
104115135/cell_35 | [
"text_html_output_1.png"
] | from collections import Counter
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascend... | code |
104115135/cell_43 | [
"text_plain_output_1.png"
] | from collections import Counter
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascend... | code |
104115135/cell_31 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variabl... | code |
104115135/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.c... | code |
104115135/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique() | code |
104115135/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.info() | code |
72073117/cell_4 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS | code |
72073117/cell_6 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_D... | code |
72073117/cell_7 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_D... | code |
72073117/cell_8 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_D... | code |
72073117/cell_3 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN | code |
72073117/cell_5 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST | code |
34122297/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.con... | code |
34122297/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().s... | code |
34122297/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.head(6) | code |
34122297/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.con... | code |
34122297/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head(6) | code |
34122297/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.con... | code |
34122297/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 |
34122297/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().s... | code |
34122297/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft.isna().... | code |
34122297/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import ... | code |
34122297/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import ... | code |
34122297/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head(6) | code |
34122297/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd... | code |
34122297/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().s... | code |
34122297/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.con... | code |
34122297/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.con... | code |
88100838/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = ... | code |
88100838/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = ... | code |
88100838/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = ... | code |
88100838/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = t... | code |
88100838/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import warnings
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
from sklearn import linear_model
from sklearn.metrics import make_scorer
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import... | code |
88100838/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = ... | code |
88100838/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = ... | code |
332331/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
332331/cell_1 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')... | code |
332331/cell_3 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'] = train['Age'].fillna(train['Age'].media... | code |
130013533/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
impor... | code |
130013533/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras... | code |
130013533/cell_6 | [
"text_plain_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import ... | code |
130013533/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.transform import resize
import os
import pickle
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.... | code |
130013533/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy ... | code |
130013533/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
impor... | code |
130013533/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
impor... | code |
130013533/cell_5 | [
"text_plain_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import datetime
import datetime
import numpy as np
import os
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pypl... | code |
122252967/cell_4 | [
"text_plain_output_1.png"
] | a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print(a * 2)
print(a + a) | code |
122252967/cell_6 | [
"text_plain_output_1.png"
] | b = {'한국': '서울', '중국': '베이징', '일본': '도쿄', '미국': '워싱턴'}
for country in b:
print(f'{country}의 수도는 {b[country]} 이다') | code |
122252967/cell_2 | [
"text_plain_output_1.png"
] | x = '안녕하세요'
y = '반갑습니다'
print(type(x))
print(x + y)
print(x, y)
print(x, y, sep=',') | code |
122252967/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | c = set([1, 3, 5, 7, 9])
d = set([1, 2, 4, 6, 8])
print(c & d)
print(c | d)
print(c - d) | code |
122252967/cell_14 | [
"text_plain_output_1.png"
] | a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
b = {'한국': '서울', '중국': '베이징', '일본': '도쿄', '미국': '워싱턴'}
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def get_name(self):
return self.name
def get_age(self):
return self.age
g = Person('Dave', 27)
h = Person('Tom... | code |
122252967/cell_10 | [
"text_plain_output_1.png"
] | e = ((0, 1), (2, 3), (4, 5))
f = (0, 1, 2, 3, 4, 5)
print(4 in e)
print(4 in f) | code |
122252967/cell_12 | [
"text_plain_output_1.png"
] | x = '안녕하세요'
y = '반갑습니다'
def number(x):
if x % 2 == 1:
return 'odd'
else:
return 'even'
num = [3, 6, 9]
[number(x) for x in num] | code |
72105169/cell_16 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import optuna
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.join... | code |
72105169/cell_17 | [
"text_plain_output_100.png",
"text_plain_output_334.png",
"application_vnd.jupyter.stderr_output_145.png",
"text_plain_output_770.png",
"application_vnd.jupyter.stderr_output_791.png",
"text_plain_output_640.png",
"application_vnd.jupyter.stderr_output_493.png",
"text_plain_output_822.png",
"applica... | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import optuna
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.join... | code |
72105169/cell_14 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.joinpath('train.csv... | code |
73077056/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from netmiko import ConnectHandler
import os
from netmiko import ConnectHandler
import os
os.environ['NET_TEXTFSM'] = '/opt/conda/lib/python3.7/site-packages/ntc_templates/templates'
linux = {'device_type': 'linux', 'host': '3.89.45.60', 'username': 'kevin', 'password': 'S!mpl312'}
c = ConnectHandler(**linux)
r = c.s... | code |
73077056/cell_1 | [
"text_plain_output_1.png"
] | !pip install netmiko | code |
73077056/cell_3 | [
"text_plain_output_1.png"
] | !pip install ntc_templates | code |
34125991/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org... | code |
34125991/cell_13 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import panda... | code |
34125991/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matpl... | code |
34125991/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&i... | code |
34125991/cell_23 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matpl... | code |
34125991/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_ba... | code |
34125991/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matpl... | code |
34125991/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&i... | code |
34125991/cell_24 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matpl... | code |
34125991/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matpl... | code |
34125991/cell_10 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org... | code |
18108171/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn... | code |
18108171/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn... | code |
18108171/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(6, 6))
cm = confusion_matrix(y_test, grid_predict)
sns.set(font_scale=1.25)
sns.heatmap(cm, annot=True, fmt='g', cbar=False, cmap='Blues')
plt.title('Confusion matrix') | code |
18108171/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report
print(classification_report(y_test, grid_predict)) | code |
18108171/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn... | code |
18108171/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train) | code |
90153636/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat | code |
90153636/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
import sklearn
from sklearn.linear_model import LinearRegression
len(df) | code |
90153636/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
X.head() | code |
90153636/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
df.head() | code |
90153636/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat
regr.sco... | code |
90153636/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
Y.head() | code |
90153636/cell_8 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
print('intercept :', regr.intercept_)
print('coefficient :', regr.coef_)
print("Pre... | code |
90153636/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
sns.scatterplot(x=df['year'], y=df['price'], hue=df['fuelType'], data=df) | code |
90153636/cell_10 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat
dc = pd.... | code |
49118983/cell_42 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
with tpu... | code |
49118983/cell_21 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_25 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_34 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
T... | code |
49118983/cell_33 | [
"text_plain_output_1.png"
] | piv1 = tr.loc[tr.answered_correctly != -1].groupby('content_id')['answered_correctly'].mean().reset_index()
piv1.columns = ['content_id', 'content_emb']
piv3 = tr.loc[tr.answered_correctly != -1].groupby('user_id')['answered_correctly'].mean().reset_index()
piv3.columns = ['user_id', 'user_emb'] | code |
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