path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
121154806/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif'... | code |
121154806/cell_1 | [
"text_plain_output_1.png"
] | !pip install -U segmentation-models
!pip install gif2numpy
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import segmentation_models as sm
import matplotlib.pyplot as plt
import os
from tqdm import tqdm
import gif2numpy | code |
121154806/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif'... | code |
121154806/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))... | code |
121154806/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(inpu... | code |
121154806/cell_12 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(inpu... | code |
90124098/cell_9 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
km = KMeans(n_clusters=i... | code |
90124098/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.info() | code |
90124098/cell_8 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
km = KMeans(n_clusters=i, init='k-means++', ... | code |
90124098/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.head() | code |
90124098/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
x.head() | code |
1006487/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.... | code |
1006487/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.... | code |
1006487/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from subprocess import check_output
import numpy as np
import pandas as pd
import nltk
import re
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from skle... | code |
1006487/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')... | code |
1006487/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')... | code |
1006487/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_10 | [
"text_plain_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')... | code |
1006487/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv'... | code |
1006487/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.... | code |
74045588/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import load_img, img_to_array
from te... | code |
74045588/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, targe... | code |
74045588/cell_7 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import EfficientNetB0
from tensor... | code |
74045588/cell_10 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, targe... | code |
74045588/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.applications import EfficientNetB0
efnb0 = Effi... | code |
104128103/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def settings():
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = [25... | code |
17120136/cell_21 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata... | code |
17120136/cell_25 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.regularizers import l1, l2
from nltk import FreqDist, bigrams, trigrams
from nltk i... | code |
17120136/cell_20 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata... | code |
17120136/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import nltk
from nltk.corpus import RegexpTokenizer as regextoken
from nltk.corpus import stopwords
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
import matplotlib
from matplotlib import pyplot ... | code |
17120136/cell_11 | [
"text_html_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_18 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_28 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
grouped = data.groupby(['name', ... | code |
17120136/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata... | code |
17120136/cell_15 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_17 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_31 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.regularizers import l1, l2
from nltk import FreqDist, bigrams, trigrams
from nltk i... | code |
17120136/cell_14 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
17120136/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
stop = stopwords.words('english')
print(stop) | code |
17120136/cell_12 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_... | code |
2029692/cell_13 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy.stats import skew
from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/t... | code |
2029692/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_4 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_6 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
train['SalePrice'].describe() | code |
2029692/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from scipy import stats
import matplotlib.pyplot as plt
from scipy.stats import skew
from subprocess import check_output
prin... | code |
2029692/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_8 | [
"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)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_10 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vm... | code |
2029692/cell_12 | [
"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"
] | from scipy.stats import skew
from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/t... | code |
2036121/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic) | code |
2036121/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts() | code |
2036121/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity... | code |
2036121/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.in... | code |
50223032/cell_21 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regpl... | code |
50223032/cell_9 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L = []
for f... | code |
50223032/cell_25 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50223032/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50223032/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50223032/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50223032/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y_train = dftrain.pop('target')
y_train | code |
50223032/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
plt.scatter(dftrain['299'], dftrain['1'])
plt.title('My PCA graph')
plt.xlabel('0 -{0}%'.format(dftrain['299']))
plt.ylabel('target -{0}%'.format(d... | code |
50223032/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftest | code |
50223032/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y = sns.regplot(x='1', y='target', data=dftrain) | code |
50223032/cell_18 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regpl... | code |
50223032/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
y.get_xlim() | code |
50223032/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftrain['127'].values | code |
50223032/cell_17 | [
"image_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftest | code |
50223032/cell_31 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-i... | code |
50223032/cell_14 | [
"image_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regpl... | code |
50223032/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y_train = dftrain.pop('target')
y_train.shape | code |
50223032/cell_10 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for fea... | code |
50223032/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regpl... | code |
50223032/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftrain | code |
72120846/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import cv2 as cv
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
pd.options.display.float_format = '{:.2f}'.format
training_labels = pd.read_csv('../input/landmark-recognition-2021/train.csv')
training_labels['path1'] = training_labels['... | code |
72120846/cell_5 | [
"image_output_1.png"
] | import matplotlib.image as mpimg
import pandas as pd
import pandas as pd
import numpy as np
import cv2 as cv
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
pd.options.display.float_format = '{:.2f}'.format
training_labels = pd.read_csv('../input/landmark-recognition-2021/train.csv')
training_l... | code |
50210665/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data.head() | code |
50210665/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'st... | code |
50210665/cell_2 | [
"text_html_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 |
50210665/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'st... | code |
50210665/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'st... | code |
50210665/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)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
print('Store Number {} has maximum Sales. Sum of Total Sales {}'.format(wa... | code |
73072460/cell_42 | [
"text_html_output_1.png"
] | from IPython.display import Image
Image(url='https://res.cloudinary.com/practicaldev/image/fetch/s--nUoflRuG--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://i.ibb.co/kG5vPdn/final-cnn.png', width=750, height=500) | code |
73072460/cell_21 | [
"image_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(datafram... | code |
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