path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18111545/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_15 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_16 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.head() | code |
18111545/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
... | code |
18111545/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
cr.profile_report(style={'full_width': True})
cr.fillna... | code |
18111545/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
import pandas_profiling as pdp
from sklearn.linear_model import LogisticRegression
pd.set_option('max_rows', 1200)
pd.set_option('max_columns', 1000)
cr = pd.read_csv('../input/Loan payments data.csv')
... | code |
128013563/cell_13 | [
"image_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints
im_with_keypoints = cv.drawKeypoints(
bl... | code |
128013563/cell_9 | [
"image_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints
im_with_keypoints = cv.drawKeypoints(
bl... | code |
128013563/cell_7 | [
"image_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints | code |
128013563/cell_15 | [
"image_output_1.png"
] | import cv2
import cv2 as cv
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints
... | code |
128013563/cell_16 | [
"image_output_1.png"
] | import cv2
import cv2 as cv
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints
... | code |
128013563/cell_14 | [
"text_plain_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
detector = cv.SimpleBlobDetector_create()
keypoints = detector.detect(blob_gray)
keypoints
im_with_keypoints = cv.drawKeypoints(
bl... | code |
128013563/cell_5 | [
"image_output_1.png"
] | import cv2 as cv
import matplotlib.pyplot as plt
blob = plt.imread('../input/opencv-samples-images/blobs.jpg')
blob_gray = cv.cvtColor(blob, cv.COLOR_RGB2GRAY)
plt.imshow(blob_gray)
plt.show() | code |
88098930/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install python-pptx | code |
88098930/cell_17 | [
"text_plain_output_1.png"
] | import pptx
prs = pptx.Presentation()
title_slide_layout = prs.slide_layouts[0]
slide = prs.slides.add_slide(title_slide_layout)
slide.shapes.title.text = "This is 'slide.shapes.title.text'."
slide.placeholders[1].text = "This is 'slide.placeholders[1].text'."
prs.save('title.pptx')
prs = Presentation('title.pptx... | code |
122258415/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
df_2 = pd.read_csv('/kaggle/input/usa-housing/USA_Housing.csv')
X = df_2[['Avg. Area Income']]
y = df_2['Price']
df_2.head
from sklearn.linear_model import LinearRegression
df_2 = LinearRegressi... | code |
122258415/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df_2 = pd.read_csv('/kaggle/input/usa-housing/USA_Housing.csv')
X = df_2[['Avg. Area Income']]
y = df_2['Price']
df_2.head | code |
122258415/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd
df_2 = pd.read_csv('/kaggle/input/usa-housing/USA_Housing.csv')
X = df_2[['Avg. Area Income']]
y = df_2['Price']
df_2.head
from sklearn.linear_model import LinearRegression
df_2 = LinearRegression()
df_2.fit(X_train, y_train)
p... | code |
34136040/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'propert... | code |
34136040/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'property_type']]
import matpl... | code |
34136040/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes | code |
34136040/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 |
34136040/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.animation as animation
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
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april... | code |
34136040/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'property_type']]
import matpl... | code |
34136040/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'property_type']]
import matpl... | code |
34136040/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.head() | code |
34136040/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'property_type']]
import matpl... | code |
34136040/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/aus-real-estate-sales-march-2019-to-april-2020/aus-property-sales-sep2018-april2020.csv')
data.dtypes
data_city_price = data[['city_name', 'price', 'property_type']]
import matpl... | code |
90117868/cell_42 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarra... | code |
90117868/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import os
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
for i, file in enumerate(folders):
if file ... | code |
90117868/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarray(y_test)
print('Train Images shape is : ', x_train.shape)
print('Train Labels shape is : ', y_train.shape) | code |
90117868/cell_33 | [
"text_plain_output_1.png"
] | import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarray(y_test)
shape = 50 * 50 * 3
x_train = x_train.reshape(x_train.shape[0], shape)
x_test = x_test.reshape(x_test.shape[0], sh... | code |
90117868/cell_44 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from tensorflow.keras import Sequential
from tensorflow.keras import datasets, layers, models
from tensorflow.keras.layers import Flatten, ... | code |
90117868/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarray(y_test)
print('Test Images shape is : ', x_test.shape)
print('Test Labels shape is : ', y_test.shape) | code |
90117868/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import os
image_path = '../input/breast-histopathology-images/'
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
... | code |
90117868/cell_19 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import os
import seaborn as sns
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)... | code |
90117868/cell_45 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from tensorflow.keras import Sequential
from tensorflow.keras import datasets, layers, models
from tensorflow.keras.layers import Flatten, ... | code |
90117868/cell_49 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Conv2DTranspose, BatchNormalization, Dropout, Lambda
from keras.models import Sequential, Model
from tensorflow.keras import Sequential
from tensorflow.keras import datasets, layer... | code |
90117868/cell_18 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import os
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
for i, file in enu... | code |
90117868/cell_28 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_... | code |
90117868/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import os
import pandas as pd
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
for i, file in enumerate(folde... | code |
90117868/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import os
import pandas as pd
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
for i, file in enumerate(folde... | code |
90117868/cell_38 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarray(y_test)
shape = 50 * 50 * 3
x_train = x_train.reshape(x_train.shap... | code |
90117868/cell_43 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
seed = 42
np.random.seed = seed
x_train = np.asarray(x_train, np.float32) / 255
x_test = np.asarray(x_test, np.float32) / 255
y_train = np.asarray(y_train)
y_test = np.asarra... | code |
90117868/cell_14 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import os
import pandas as pd
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_infected = 0
folders = os.listdir(main_dirictory)
for i, file in enumerate(folde... | code |
90117868/cell_27 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_... | code |
90117868/cell_37 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_... | code |
90117868/cell_36 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['0', '1']
def load_images_from_directory(main_dirictory):
total_labels = []
images = []
pathes = []
total_normal = 0
total_... | code |
74050322/cell_6 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
... | code |
74050322/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
data.head() | code |
74050322/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set
data = pd.read_csv('../input/boston-housing-dataset/HousingData.csv')
X = data.drop('MEDV', axis=1).values
Y = data['MEDV'].values
Room_numb... | code |
18127820/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Input
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers.merge import concatenate
from keras.layers.pooling import MaxPooling2D
from keras.models import Model, load_model
from keras.optimizers import Adam
from skimage.io import imread
from skimage.transform ... | code |
18127820/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !pip3 install git+https://github.com/qubvel/segmentation_models
from segmentation_models import Unet
# model = Unet('densenet121',encorder_weights='imagenet',freeze_encorder=True) | code |
18127820/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import sys
import random
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from keras.models import Model, load_model
from keras.layers import Input
from keras.layers.core import Dropout, Lambda
from keras.layers.convolutional import Conv2D, Conv2DTra... | code |
18127820/cell_5 | [
"text_plain_output_1.png"
] | from skimage.io import imread
from skimage.transform import resize
from tqdm import tqdm
import numpy as np
import os
import random
TRAIN_PATH = '../input/train/'
TEST_PATH = '../input/test/'
seed = 42
random.seed = seed
np.random.seed = seed
tot_num = 5635
IMG_HEIGHT = 128
IMG_WIDTH = 128
files = os.listdir(TRAI... | code |
2014936/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_25 | [
"text_plain_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
st... | code |
2014936/cell_4 | [
"text_html_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2014936/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('.... | code |
2014936/cell_20 | [
"text_plain_output_1.png"
] | import datetime
import pandas as pd
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read... | code |
2014936/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv') | code |
2014936/cell_26 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
st... | code |
2014936/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
train_sample.head() | code |
2014936/cell_28 | [
"image_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('.... | code |
2014936/cell_15 | [
"text_plain_output_1.png"
] | import datetime
import pandas as pd
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
import datetime
train_sample['date'] = train_sample['date'].apply(datetime.datetime.strptime, args=('%Y-%m-%d',))
train_sample.sort_values(by='date', inplace=True)
train_sample['onpromotion'].fillna... | code |
2014936/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_24 | [
"text_plain_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
st... | code |
2014936/cell_14 | [
"text_plain_output_1.png"
] | import datetime
import pandas as pd
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
import datetime
train_sample['date'] = train_sample['date'].apply(datetime.datetime.strptime, args=('%Y-%m-%d',))
train_sample.sort_values(by='date', inplace=True)
print(train_sample.head())
print(tr... | code |
2014936/cell_22 | [
"text_plain_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
st... | code |
2014936/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
stores = pd.read_csv('../input/stores.csv')
test = pd.read_csv('../input/test.csv')
transactions = pd.read_csv('../... | code |
2014936/cell_27 | [
"image_output_1.png"
] | import datetime
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
train_sample = train.sample(frac=0.05)
holiday_events = pd.read_csv('../input/holidays_events.csv')
items = pd.read_csv('../input/items.csv')
oil = pd.read_csv('../input/oil.csv')
st... | code |
129012165/cell_21 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix,classification_report
import itertools
import keras
import matplotlib.image as img
import matplotlib.pyplot as plt
import numpy as np
import pathlib
path = pathlib.Path('/kaggle/input/5-flower-types-classificat... | code |
129012165/cell_20 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix,classification_report
import keras
import numpy as np
train_gen = ImageDataGenerator(rotation_range=10, rescale=1.0 / 255, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False, z... | code |
129012165/cell_18 | [
"image_output_2.png",
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
import keras
import numpy as np
train_gen = ImageDataGenerator(rotation_range=10, rescale=1.0 / 255, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False, zoom_range=0.1, shear_range=0.1, brightness_range=[0.8, 1.2], fill_mo... | code |
129012165/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.image as img
import matplotlib.pyplot as plt
import pathlib
path = pathlib.Path('/kaggle/input/5-flower-types-classification-dataset/flower_images')
lilly = list(path.glob('Lilly/*'))[:1000]
lotus = list(path.glob('Lotus/*'))[:1000]
orchid = list(path.glob('Orchid/*'))[:1000]
sunflower = list(path... | code |
129012165/cell_16 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
import keras
train_gen = ImageDataGenerator(rotation_range=10, rescale=1.0 / 255, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False, zoom_range=0.1, shear_range=0.1, brightness_range=[0.8, 1.2], fill_mode='nearest', valida... | code |
129012165/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
import keras
import matplotlib.image as img
import matplotlib.pyplot as plt
import pathlib
path = pathlib.Path('/kaggle/input/5-flower-types-classification-dataset/flower_images')
lilly = list(path.glob('Lilly/*'))[:1000]
lotus = list(path.glob('Lotus/*'))[... | code |
129012165/cell_14 | [
"text_plain_output_1.png"
] | import keras
cnn = keras.models.Sequential()
cnn.add(keras.layers.Conv2D(filters=32, kernel_size=3, padding='valid', activation='relu', input_shape=(224, 224, 3)))
cnn.add(keras.layers.MaxPool2D(pool_size=2, strides=2))
cnn.add(keras.layers.Flatten())
cnn.add(keras.layers.Dense(45, activation='relu'))
cnn.add(keras.la... | code |
129012165/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import confusion_matrix,classification_report
import itertools
import keras
import matplotlib.image as img
import matplotlib.pyplot as plt
import numpy as np
import pathlib
path = pathlib.Path('/kaggle/input/5-flower-types-classificat... | code |
129012165/cell_10 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_gen = ImageDataGenerator(rotation_range=10, rescale=1.0 / 255, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True, vertical_flip=False, zoom_range=0.1, shear_range=0.1, brightness_range=[0.8, 1.2], fill_mode='nearest', validation_split=0.2... | code |
32068475/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, mean_squared_log_error
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 xgboost as xgb
class CovidModel:
def _... | code |
32068475/cell_6 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, mean_squared_log_error
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 xgboost as xgb
class CovidModel:
def _... | code |
32068475/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 |
32068475/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, mean_squared_log_error
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 xgboost as xgb
class CovidModel:
def _... | code |
32068475/cell_8 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, mean_squared_log_error
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 xgboost as xgb
class CovidModel:
def _... | code |
122260046/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape) | code |
122260046/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (... | code |
122260046/cell_6 | [
"text_plain_output_2.png",
"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/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
train.info() | code |
122260046/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"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/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.shape, test.shape)
A = test.values / 255.0
A = A.reshape(-1, 28, 28, 1)
A.shape
ids = [i + ... | code |
122260046/cell_26 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
from keras.models import Sequential
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
import pandas as pd
import pandas as pd # data processing, CSV file I/O (... | code |
122260046/cell_11 | [
"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)
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
(train.sha... | code |
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