path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
105190732/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
df_train.info()
print('----------------------------------')
df_test.info() | code |
105190732/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
label = df_train['Survived']
label.unique()
label.value_counts().plot.pie(auto... | code |
105190732/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
print('Amount of missing data in Fare for train:', df_train.Fare.isnull().sum())... | code |
105190732/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
print(df_train.Age.isnull().sum())
print(df_test.Age.isnull().sum()) | code |
105190732/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
PATH = '../input/titanic/'
df_train = pd.read_csv(f'{PATH}/train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}/test.csv', low_memory=False)
df_train.dtypes
label = df_train['Survived']
label.unique() | code |
16111090/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.metrics import accuracy_score
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
for i in range(len(x_train)):
distance = np.sqrt(np.sum(np.square(x_test - x_train.values[i, :])... | code |
16111090/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.metrics import accuracy_score
import numpy as np
import numpy as np # linear algebra
def predict(x_train, y_train, x_test, k):
distances = []
targets = []
for i in range(len(x_train)):
distance = np.sqrt(np.sum(np.square(x_test - x_train.values[i, :])... | code |
16111090/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16111090/cell_3 | [
"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 pandas as pd
import numpy as np
import math
import operator
df = pd.read_csv('../input/Iris.csv')
print(df.head())
df.shape
from collections import Counter | code |
16111090/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import math
import operator
df = pd.read_csv('../input/Iris.csv')
df.shape
from collections import Counter
from sklearn.model_selecti... | code |
18137353/cell_4 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
data_path = '../input/all-dogs/all-dogs/'
lable_path = '../input/annotation/Annotation/'
all_image_paths = os.listdir(data_path)
IMG_SIZE = 64
BUFFER_SIZE = 20579
BATCH_SIZE = 256
def get_images(directory):
Images = []
for image_file in all_im... | code |
18137353/cell_1 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import glob, os, imageio, PIL, time, cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from IPython import display
from sklearn.utils import shuffle
print(tf.__version__) | code |
106199219/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data | code |
106199219/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/segment/Segmentation_dataset.csv')
data.describe() | code |
72098912/cell_2 | [
"text_html_output_1.png"
] | import glob
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import glob
import pandas as pd
path = '../input/pandastasks/Pandas-Data-Science-Tasks-master/SalesAnalysis/Sales_Data'
filenames = glob.glob(path + '/*.csv')
dfs = ... | code |
129020869/cell_9 | [
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.columns[df_flight.dtypes == object]
num_cols = df_flight.s... | code |
129020869/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.head() | code |
129020869/cell_23 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.info() | code |
129020869/cell_26 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_2 | [
"text_plain_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
import warnings
warnings.filterwarnings('ignore') | code |
129020869/cell_19 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/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 |
129020869/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum() | code |
129020869/cell_28 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_17 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_24 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.p... | 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
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_22 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_27 | [
"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)
import seaborn as sns
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.co... | code |
129020869/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.isnull().sum()
cat_cols = df_flight.columns[df_flight.dtypes == object]
num_cols = df_flight.s... | code |
129020869/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_flight = pd.read_csv('/kaggle/input/airindia-monthly-passenger-traffic/AirIndia (International).csv')
df_flight.tail() | code |
73097621/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
data.isna().sum()
y = data.target
features_1 = ['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']
features_2 ... | code |
73097621/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
data.isna().sum()
y = data.target
features_1 = ['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']
features_2 ... | code |
73097621/cell_2 | [
"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/30-days-of-ml/train.csv')
data.isna().sum() | code |
73097621/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 |
17097984/cell_21 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_13 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_9 | [
"text_html_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5] | code |
17097984/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_30 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_6 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path | code |
17097984/cell_29 | [
"text_html_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_26 | [
"image_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_7 | [
"image_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls() | code |
17097984/cell_18 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_28 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_15 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_16 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_17 | [
"image_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_24 | [
"text_html_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_14 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_27 | [
"image_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
17097984/cell_12 | [
"text_plain_output_1.png"
] | pets = 'https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet'
path = untar_data(pets)
path
path.ls()
path_anno = path / 'annotations'
path_img = path / 'images'
fnames = get_image_files(path_img)
fnames[:5]
np.random.seed(2)
pat = '/([^/]+)_\\d+.jpg$'
data = ImageDataBunch.from_name_re(path_img, fnames, pat... | code |
121151048/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)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.head(5) | code |
121151048/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/... | code |
121151048/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)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
print(training_set.shape, testing_set.shape) | code |
121151048/cell_39 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/... | code |
121151048/cell_2 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True) | code |
121151048/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121151048/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes | code |
121151048/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_3 | [
"text_plain_output_1.png"
] | from fastcore.basics import *
from fastcore.parallel import *
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_auc_score, roc_curve, precision_score, recall_score, f1_score, accuracy_score
from os import cpu_count
from math import floor
import pandas as pd
import numpy as np
from sklearn.... | code |
121151048/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet')
training_set.dtypes
training_set.attack_cat.value_c... | code |
121151048/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_testing-set.parquet... | code |
121151048/cell_37 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import seaborn as sns
training_set = pd.read_parquet('/kaggle/input/unswnb15/UNSW_NB15_training-set.parquet')
testing_set = pd.read_parquet('/kaggle/input/unswnb15/... | code |
73090574/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
parameters = {'axes.grid': True}
plt.rcParams.update(parameters)
imp... | code |
73090574/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
parameters = {'axes.grid': True}
plt.rcParams.update(parameters)
import optuna
from optuna.samplers import TPESampler
... | code |
73090574/cell_25 | [
"text_plain_output_1.png"
] | from optuna.samplers import TPESampler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn ... | code |
73090574/cell_23 | [
"text_html_output_1.png"
] | from optuna.samplers import TPESampler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn ... | code |
73090574/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
import math
import numpy... | code |
73090574/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
df_test.head() | code |
73090574/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
parameters = {'axes.grid': True}
plt.rcParams.u... | code |
73090574/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
df_test.drop('id', axis=1, inplace=True)
df_test.isnull().sum().max() == 0 | code |
73090574/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
df_train.head() | code |
73090574/cell_17 | [
"text_plain_output_1.png"
] | import xgboost as xgb
model = xgb.XGBRegressor(objective='reg:tweedie', tree_method='gpu_hist', predictor='gpu_predictor', sampling_method='gradient_based', max_bin=512, silent=False, random_state=17)
model.fit(X_train, y_train)
preds_test = model.predict(X_test)
preds_train = model.predict(X_train) | code |
73090574/cell_24 | [
"text_plain_output_1.png"
] | from optuna.samplers import TPESampler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn ... | code |
73090574/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
parameters = {'axes.grid': True}
plt.rcParams.update(parameters)
imp... | code |
73090574/cell_22 | [
"text_html_output_1.png"
] | from optuna.samplers import TPESampler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import optuna
import pandas as pd
import seaborn ... | code |
73090574/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import math
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
parameters = {'axes.grid': True}
plt.rcParams.update(parameters)
import optuna
from optuna.samplers import TPESampler
... | code |
73090574/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
df_test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
df_train.drop('id', axis=1, inplace=True)
df_train.isnull().sum().max() == 0 | code |
128006176/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.columns
dataset_counts = train_labels['dataset'].value_counts()
scen... | code |
128006176/cell_4 | [
"image_output_1.png"
] | !wget https://raw.githubusercontent.com/colmap/colmap/dev/scripts/python/read_write_model.py | code |
128006176/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.columns
dataset_counts = train_labels['dataset'].value_counts()
scen... | code |
128006176/cell_7 | [
"image_output_1.png"
] | import os
import pandas as pd
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.head() | code |
128006176/cell_18 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
import read_write_model
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.column... | code |
128006176/cell_8 | [
"image_output_1.png"
] | import os
import pandas as pd
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.columns | code |
128006176/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import os
import pandas as pd
import random
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
train_labels.columns
dataset_counts = train... | code |
128006176/cell_24 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import read_write_model
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
... | code |
128006176/cell_22 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import random
import read_write_model
root_dir = '/kaggle/input/image-matching-challenge-2023'
train_labels_file = os.path.join(root_dir, 'train/train_labels.csv')
train_labels = pd.read_csv(train_labels_file)
... | code |
34118365/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from glob import glob
from itertools import chain
from keras.applications.resnet_v2 import ResNet50V2
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, TensorBoard, ReduceLROnPlateau
from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
from keras.layer... | code |
34118365/cell_2 | [
"text_plain_output_1.png"
] | from glob import glob
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
all_xray_df = pd.read_csv('/kaggle/input/data/Data_Entry_2017.csv')
all_image_paths = {os.path.basename(x): x for x in glob(os.path.join('/kaggle/input/data', 'images*', '*', '*.png'))}
print('Scans found:', len(al... | code |
34118365/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import os
from glob import glob
import matplotlib.pyplot as plt
from itertools import chain
from random import sample
import scipy
import sklearn.model_selection as skl
from sklearn.utils import class_weight
import tensorflow as tf
from skimage import io
from keras.preprocessing.i... | code |
34118365/cell_7 | [
"image_output_1.png"
] | from glob import glob
from itertools import chain
from keras.preprocessing.image import ImageDataGenerator
from random import sample
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn.model_selection... | code |
34118365/cell_18 | [
"text_plain_output_1.png"
] | from glob import glob
from itertools import chain
from keras.applications.resnet_v2 import ResNet50V2
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, TensorBoard, ReduceLROnPlateau
from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
from keras.layer... | code |
34118365/cell_16 | [
"image_output_1.png"
] | from glob import glob
from itertools import chain
from keras.applications.resnet_v2 import ResNet50V2
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, TensorBoard, ReduceLROnPlateau
from keras.layers import Conv2D, SeparableConv2D, MaxPool2D, LeakyReLU, Activation
from keras.layer... | code |
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