path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128021494/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import random
import tensorflow as tf
data = np.genfromtxt('/kaggle/input/da-assignment2/wdbc.data', delimiter=',')
data = np.delete(data, [0, 1], axis=1)
file = open('/kaggle/input/wdbc-labels/wdbc_labels.csv', 'r'... | code |
128021494/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
data = np.genfromtxt('/kaggle/input/da-assignment2/wdbc.data', delimiter=',')
data = np.delete(data, [0, 1], axis=1)
print(data.shape)
file = open('/kaggle/input/wdbc-labels/wdbc_labels.csv', 'r')
lines = file.readlines()
count = 0
labels = np.zeros((data.shape[0], 1... | code |
50212911/cell_13 | [
"image_output_1.png"
] | import xgboost
import xgboost
xgBoost = xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, booster='gbtree')
xgBoost.fit(X_train, Y_train)
print('train score', xgBoost.score(X_train, Y_train))
print('test score', xgBoost.score(X_test, Y_test)) | code |
50212911/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50212911/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.info() | code |
50212911/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import catboost
import lightgbm
import lightgbm
import xgboost
import xgboost
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
import catboost
cboost = catboost.CatBoostRegressor(loss... | code |
50212911/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
print('train score', sklearn_boost.score(X_train, Y_train))
print('test score', sklearn_boost.score(X_test, Y_test)) | code |
50212911/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import catboost
import xgboost
import xgboost
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
c... | code |
50212911/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 |
50212911/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50212911/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import catboost
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
cboost.fit(X_train, Y_train)
imp... | code |
50212911/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
import warnings
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
from sklearn.ensemble import RandomForestRegressor
... | code |
50212911/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
Begging = RandomForestRegressor(max_depth=30, n_estimators=300)
Begging.fit(X_train, Y_train)
print('train score', Begging.score(X_train, Y_train))
print('test score... | code |
50212911/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
import sklearn
params = {'learning_rate': [0.05], 'n_estimators': [200], 'max_depth': [6]}
gsc = GridSearchCV(estimator=ensemble.GradientBo... | code |
50212911/cell_14 | [
"image_output_1.png"
] | import lightgbm
import lightgbm
lgbreg = lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, learning_rate=0.1, n_estimators=100)
lgbreg.fit(X_train, Y_train)
print('train score', lgbreg.score(X_train, Y_train))
print('test score', lgbreg.score(X_test, Y_test)) | code |
50212911/cell_12 | [
"image_output_1.png"
] | import catboost
import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
cboost.fit(X_train, Y_train)
print('train score', cboost.score(X_train, Y_train))
print('test score', cboost.score(X_test, Y_test)) | code |
50212911/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
106198039/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
df_trai... | code |
106198039/cell_20 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
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)
train_filename = '/kaggle/input/spaceship-... | code |
106198039/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
def pri... | code |
106198039/cell_11 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/te... | code |
106198039/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
a = df_... | code |
106198039/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 |
106198039/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
def pri... | code |
106198039/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
def pri... | code |
106198039/cell_3 | [
"text_plain_output_1.png"
] | !head /kaggle/input/spaceship-titanic/train.csv | code |
106198039/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
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)
train_filename = '/kaggle/input/spaceship-... | code |
106198039/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train
df_trai... | code |
106198039/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
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)
train_filename = '/kaggle/input/spaceship-... | code |
106198039/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_filename = '/kaggle/input/spaceship-titanic/train.csv'
test_filename = '/kaggle/input/spaceship-titanic/test.csv'
df_train = pd.read_csv(train_filename)
df_test = pd.read_csv(test_filename)
df_test_original = df_test.copy()
df_train | code |
105216211/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 |
105216211/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../input/analyticsvjobathon/train_wn75k28.csv'
lead_data = pd.read_csv(file_path, index_col='id')
file_path2 = '../input/analyticsvjobathon/test_Wf7sxXF.csv'
X_test = pd.read_csv(file_path2, index_col='id')
X = lead_data.copy()
y = X.... | code |
105216211/cell_18 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import f1_score
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
import datetime
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../... | code |
105216211/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../input/analyticsvjobathon/train_wn75k28.csv'
lead_data = pd.read_csv(file_path, index_col='id')
file_path2 = '../input/analyti... | code |
105216211/cell_16 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import f1_score
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
import datetime
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../input/analyticsvjobathon/train_wn75k2... | code |
105216211/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../input/analyticsvjobathon/train_wn75k28.csv'
lead_data = pd.read_csv(file_path, index_col='id')
file_path2 = '../input/analyticsvjobathon/test_Wf7sxXF.csv'
X_test = pd.read_csv(file_path2, index_col='id')
X = lead_data.copy()
y = X.... | code |
105216211/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
file_path = '../input/analyticsvjobathon/train_wn75k28.csv'
lead_data = pd.read_csv(file_path, index_col='id')
file_path2 = '../input/analyticsvjobathon/test_Wf7sxXF.csv'
X_test = pd.read_csv(file_path2, index_col='id')
X = lead_data.copy()
y = X.... | code |
18114963/cell_7 | [
"image_output_1.png"
] | import bs4
import pandas as pd
import requests
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.status_code
soup = bs4.BeautifulSoup(r.content)
graphs = soup.find_all('p')
utterances = [x.get_text() for x in graphs if 'data-elm-loc' in x.attrs.... | code |
18114963/cell_15 | [
"image_output_1.png"
] | from wordcloud import WordCloud
import bs4
import matplotlib.pyplot as plt
import pandas as pd
import requests
import sklearn.feature_extraction.text as skt
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.status_code
soup = bs4.BeautifulSou... | code |
18114963/cell_3 | [
"image_output_1.png"
] | import requests
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.status_code | code |
18114963/cell_17 | [
"text_html_output_1.png"
] | from wordcloud import WordCloud
import bs4
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import requests
import sklearn.feature_extraction.text as skt
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.sta... | code |
18114963/cell_10 | [
"text_plain_output_1.png"
] | import bs4
import pandas as pd
import requests
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.status_code
soup = bs4.BeautifulSoup(r.content)
graphs = soup.find_all('p')
utterances = [x.get_text() for x in graphs if 'data-elm-loc' in x.attrs.... | code |
18114963/cell_12 | [
"text_plain_output_1.png"
] | import bs4
import pandas as pd
import requests
r = requests.get('https://www.washingtonpost.com/politics/2019/07/31/transcript-first-night-second-democratic-debate')
r.status_code
soup = bs4.BeautifulSoup(r.content)
graphs = soup.find_all('p')
utterances = [x.get_text() for x in graphs if 'data-elm-loc' in x.attrs.... | code |
2033155/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Pakistan Intellectual Capital - Computer Science - Ver 1.csv', encoding='ISO-8859-1')
df['University Currently Teaching'].value_counts()[:20].plot(kind='bar') | code |
2033155/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Pakistan Intellectual Capital - Computer Science - Ver 1.csv', encoding='ISO-8859-1')
df.head() | code |
2033155/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Pakistan Intellectual Capital - Computer Science - Ver 1.csv', encoding='ISO-8859-1')
df_new = df[df['Other Information'].isin(['On Study Leave', 'On study leave', 'PhD Study Leave', 'On Leave'])]
x = df_new['Teacher Name'].count()
y = df['Teacher Name'].count() - x
df_... | code |
2033155/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12, 5)
df = pd.read_csv('../input/Pakistan Intellectual Capital - Computer Science - Ver 1.csv', encoding='ISO-8859-1')
df_new = df[df['O... | code |
128012764/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glo... | code |
128012764/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import shutil
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import glob
import random
import tensorflow as tf
import keras.utils as image
random.seed(42)
tf.random.set_seed(42)
from tensorflow.keras import layers
from tensorflow.keras.applications import VGG16
from tensorfl... | code |
128012764/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle... | code |
128012764/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow.keras.backend as K
breast_img = glob.glob('/kaggle... | code |
128012764/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import VGG16
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
import tensorflow.ker... | code |
128012764/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import pandas as pd
breast_img = glob.glob('/kaggle/input/breast-histopathology-images/IDC_regular_ps50_idx5/**/*.png', recursive=True)
data = pd.read_csv('/kaggle/input/selected-imag... | code |
105173129/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[monkeypox['location'] != 'World'].copy()
def get_continent_country(x):
NorthA... | code |
105173129/cell_4 | [
"image_output_1.png"
] | import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.head() | code |
105173129/cell_6 | [
"image_output_1.png"
] | import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
print(monkeypox.isnull().sum()) | code |
105173129/cell_8 | [
"image_output_1.png"
] | import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[monkeypox['location'] != 'World'].copy()
print('Countries :', monkeypox_countr.location.unique())
prin... | code |
105173129/cell_15 | [
"text_html_output_1.png"
] | from matplotlib.patches import ConnectionPatch
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[m... | code |
105173129/cell_16 | [
"text_plain_output_1.png"
] | from matplotlib.patches import ConnectionPatch
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[m... | code |
105173129/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[monkeypox['location'] != 'World'].copy()
def get_continent_country(x):
NorthAmerica = ['Barbados'... | code |
105173129/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
monkeypox = pd.read_csv('../input/worldwide-monkeypox-daily-dataset/owid-monkeypox-data.csv')
monkeypox.replace('Congo', 'Democratic Republic of Congo', inplace=True)
monkeypox_countr = monkeypox[monkeypox['location'] != 'World'].copy()
def get_continent_country(x):
NorthA... | code |
18118979/cell_13 | [
"text_html_output_1.png"
] | out_path = Path('./')
out_path.ls()
in_path = Path('../input/')
tfms = get_transforms(do_flip=False, flip_vert=False, max_rotate=20.0, max_zoom=1.1, max_lighting=0.0, max_warp=0.2, p_affine=0.75, p_lighting=0.0)
test = CustomImageList.from_csv_custom(path=in_path, csv_name='test.csv', imgIdx=0)
data = CustomImageList... | code |
18118979/cell_4 | [
"image_output_1.png"
] | import pandas as pd
out_path = Path('./')
out_path.ls()
in_path = Path('../input/')
df = pd.read_csv(in_path / 'train.csv')
df.head(n=5) | code |
18118979/cell_11 | [
"image_output_1.png"
] | out_path = Path('./')
out_path.ls()
in_path = Path('../input/')
tfms = get_transforms(do_flip=False, flip_vert=False, max_rotate=20.0, max_zoom=1.1, max_lighting=0.0, max_warp=0.2, p_affine=0.75, p_lighting=0.0)
test = CustomImageList.from_csv_custom(path=in_path, csv_name='test.csv', imgIdx=0)
data = CustomImageList... | code |
18118979/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
print(os.listdir('../input')) | code |
18118979/cell_8 | [
"image_output_1.png"
] | out_path = Path('./')
out_path.ls()
in_path = Path('../input/')
tfms = get_transforms(do_flip=False, flip_vert=False, max_rotate=20.0, max_zoom=1.1, max_lighting=0.0, max_warp=0.2, p_affine=0.75, p_lighting=0.0)
test = CustomImageList.from_csv_custom(path=in_path, csv_name='test.csv', imgIdx=0)
data = CustomImageList... | code |
18118979/cell_10 | [
"text_html_output_1.png"
] | out_path = Path('./')
out_path.ls()
in_path = Path('../input/')
tfms = get_transforms(do_flip=False, flip_vert=False, max_rotate=20.0, max_zoom=1.1, max_lighting=0.0, max_warp=0.2, p_affine=0.75, p_lighting=0.0)
test = CustomImageList.from_csv_custom(path=in_path, csv_name='test.csv', imgIdx=0)
data = CustomImageList... | code |
34140599/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import spacy
folder = '../input/nlp-getting-started'
test = pd.read_csv(os.path.join(folder, 'test.csv'), index_col='id')
train = pd.read_csv(os.path.join(folder, 'train.csv'), index_col='id')
X = train.drop(columns='target')
y = train['target']
nlp = spacy.load('en_core_web_sm')
doc ... | code |
34140599/cell_3 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
folder = '../input/nlp-getting-started'
test = pd.read_csv(os.path.join(folder, 'test.csv'), index_col='id')
train = pd.read_csv(os.path.join(folder, 'train.csv'), index_col='id')
X = train.drop(columns='target')
y = train['target']
len(X) | code |
34140599/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
folder = '../input/nlp-getting-started'
test = pd.read_csv(os.path.join(folder, 'test.csv'), index_col='id')
train = pd.read_csv(os.path.join(folder, 'train.csv'), index_col='id')
X = train.drop(columns='target')
y = train['target']
X['text'].iloc[0] | code |
90152351/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
my_model = XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10, min_c... | code |
90152351/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
my_model = XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10, min_child_weight=5, n_estimators=150, nthread=-1, reg_al... | code |
90152351/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)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.feature_selection import mutual_info_regression
from sklearn.model_selection import train_test_split
from sklearn.compose... | code |
90152351/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
my_model = XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10, min_child_weight=5, n_estimators=150, nthread=-1, reg_al... | code |
90152351/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from xgboost import XGBRegressor
my_model = XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=10, min_c... | code |
90152351/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import mutual_info_regression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.feature_selection import mutual_info_regression
from sklear... | code |
90152351/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.feature_selection import mutual_info_regression
from sklearn.model_selection import train_test_spl... | code |
90152351/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)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.feature_selection import mutual_info_regression
from sklearn.model_selection import train_test_split
from sklearn.compose... | code |
332834/cell_9 | [
"image_output_11.png",
"image_output_74.png",
"image_output_82.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"text_plain_output_5.png",
"image_output_77.png",
"image_output_47.png",
"text_plain_output_15.png",
"image_output_78.png",
"image_output_17.png",
"image_... | 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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_4 | [
"text_plain_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_20 | [
"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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_6 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.... | 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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_18 | [
"text_plain_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_15 | [
"text_html_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_14 | [
"text_html_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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
332834/cell_12 | [
"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)
people = pd.read_csv('../input/people.csv', dtype={'people_id': np.str, 'activity_id': np.str, 'char_38': np.int32}, parse_dates=['date'])
act_train = pd.read_csv('../input/act_t... | code |
18135285/cell_4 | [
"text_html_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark
sdf_train = spark.read.csv('../input/train.csv', inferSchema=True, header=True)
print(sdf_train.printSchema())
pdf = sdf_train.limit(5).toPandas()
pdf.T | code |
18135285/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pyspark.sql import SparkSession
import os
print(os.listdir('../input')) | code |
18135285/cell_1 | [
"text_plain_output_1.png"
] | ! pip install pyspark | code |
18135285/cell_3 | [
"text_html_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark | code |
18135285/cell_17 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
from pyspark.sql import SparkSession
import os
print(os.listdir('submission')) | code |
18135285/cell_14 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark
sdf_train = spark.read.csv('../input/train.csv', inferSchema=True, header=True)
pdf = sdf... | code |
18135285/cell_5 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
spark
sdf_train = spark.read.csv('../input/train.csv', inferSchema=True, header=True)
pdf = sdf_train.limit(5).toPandas()
pdf.T
sdf_test = spark.read.csv('../input/test.csv', inferSchema=True, header=True)
pdf = sdf_test.limit(5).toPanda... | code |
16169565/cell_13 | [
"image_output_1.png"
] | from keras.callbacks import LearningRateScheduler, ModelCheckpoint
from keras.layers.convolutional import Conv2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Sequential, model_from_json
from keras.optimizers import SGD
fro... | code |
16169565/cell_6 | [
"text_plain_output_1.png"
] | from skimage import io, color, exposure, transform
import cv2
import cv2
import numpy as np # linear algebra
import os
import os
def preprocess_img(img):
# Histogram normalization in y
hsv = color.rgb2hsv(img)
hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2])
img = color.hsv2rgb(hsv)
# central ... | code |
16169565/cell_11 | [
"text_plain_output_1.png"
] | from keras.callbacks import LearningRateScheduler, ModelCheckpoint
from keras.layers.convolutional import Conv2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Sequential, model_from_json
from keras.optimizers import SGD
fro... | code |
16169565/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
from skimage import io, color, exposure, transform
import os
import glob
import h5py
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.co... | code |
16169565/cell_7 | [
"image_output_1.png"
] | from skimage import io, color, exposure, transform
import cv2
import cv2
import numpy as np # linear algebra
import os
import os
def preprocess_img(img):
# Histogram normalization in y
hsv = color.rgb2hsv(img)
hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2])
img = color.hsv2rgb(hsv)
# central ... | code |
16169565/cell_12 | [
"text_plain_output_1.png"
] | from skimage import io, color, exposure, transform
import cv2
import cv2
import numpy as np # linear algebra
import os
import os
def preprocess_img(img):
# Histogram normalization in y
hsv = color.rgb2hsv(img)
hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2])
img = color.hsv2rgb(hsv)
# central ... | code |
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