path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2020968/cell_79 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
columns = ['SibSp', 'Parch', ... | code |
2020968/cell_30 | [
"image_output_1.png"
] | from sklearn.preprocessing import minmax_scale
import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=T... | code |
2020968/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_60 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_69 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
columns = ['SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
chance_survive ... | code |
2020968/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_49 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_58 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_38 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
columns = ['SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
chance_survive = len(train[train['Survived'] == 1]) / len(train['Sur... | code |
2020968/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_77 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, ... | code |
2020968/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
chance_survive = len(train[train['Survived'] == 1]) / len(train['Survived'])
def plot_survival(df, index, color='blue', use_index=True, num_xticks=0, xticks='', position=0.5, lege... | code |
2020968/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
columns = ['SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']
holdout[columns].describe() | code |
2020630/cell_4 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
data = pd.read_csv('../input/scrubbed.csv')
usa = data[data.country == 'us']
texas = data[data.state == 'tx']
illinois = data[data.st... | code |
2020630/cell_2 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
data = pd.read_csv('../input/scrubbed.csv')
usa = data[data.country == 'us']
usa.head() | code |
2020630/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
data = pd.read_csv('../input/scrubbed.csv')
data.head() | code |
2020630/cell_3 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
data = pd.read_csv('../input/scrubbed.csv')
usa = data[data.country == 'us']
texas = data[data.state == 'tx']
texas.head() | code |
2020630/cell_5 | [
"text_html_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
data = pd.read_csv('../input/scrubbed.csv')
usa = data[data.country == 'us']
texas = data[data.state == 'tx']
illinois = data[data.st... | code |
50224851/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
plt.figure(figsize=(10, 8))
sns.countplot(df_train['label'], edgecolor='black', pal... | code |
50224851/cell_20 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
path = '../input/cassava-leaf-disease-classification/train_... | code |
50224851/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
df_train.info() | code |
50224851/cell_18 | [
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
path = '../input/cassava-leaf-disease-classification/train_... | code |
50224851/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
path = '../input/cassava-leaf-disease-classification/train_... | code |
50224851/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
path = '../input/cassava-leaf-disease-classification/train_... | code |
50224851/cell_12 | [
"text_html_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train['label'] = df_train['label'].astype(str)
sns.set_style('whitegrid')
path = '../input/cassava-leaf-disease-classification/train_... | code |
50224851/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df_train.head() | code |
2010942/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
all_data.head() | code |
2010942/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import skew
from scipy.stats.stats import pearsonr
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train = train.drop(['Name'], axis=1)
test = test.drop(['Nam... | code |
2010942/cell_7 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'Fare': train['Fare'], 'log(price + 1)': np.log1p(train['Fare'... | code |
2010942/cell_8 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'Fare': train['Fare'], 'log(price + 1)': np.log1p(train['Fare'... | code |
2010942/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'Fare': train['Fare'], 'log(price + 1)': np.log1p(train['Fare'])})
prices.hist() | code |
2010942/cell_10 | [
"text_html_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import LogisticRegression
import matplotlib
import numpy as np
import pandas as pd
all_data = pd.concat((train.loc[:, 'Pclass':'Embarked'], test.loc[:, 'Pclass':'Embarked']))
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'Fare': t... | code |
121151188/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
121151188/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/test.csv')
train.isnull().s... | code |
121151188/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/test.csv')
train.info() | code |
121151188/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
121151188/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/test.csv')
train.head() | code |
121151188/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/test.csv')
train_labels.hea... | code |
121151188/cell_15 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
121151188/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
121151188/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
121151188/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
train = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train.csv')
train_labels = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play/train_labels.csv')
test = pd.read_csv('/kaggle/input/predict-student-performance-from-game-play... | code |
128008497/cell_4 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import pandas as pd
from tqdm import tqdm
tqdm.pandas()
df = pd.read_csv('/kaggle/input/demand-forecasting-kernels-only/train.csv')
df
df['date'] = df.progress_apply(lambda x: f"{x['date']} {x['store']}:{x['item']}", axis=1)
df
df['date'] = pd.to_datetime(df['date'])
df = d... | code |
128008497/cell_1 | [
"text_html_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import pandas as pd
from tqdm import tqdm
tqdm.pandas()
df = pd.read_csv('/kaggle/input/demand-forecasting-kernels-only/train.csv')
df | code |
128008497/cell_7 | [
"text_plain_output_1.png"
] | !git clone https://github.com/mnansary/Informer2020HDFC.git | code |
128008497/cell_16 | [
"text_html_output_1.png"
] | from exp.exp_informer import Exp_Informer
from utils.tools import dotdict
import torch
args = dotdict()
args.model = 'informer'
args.data = 'custom'
args.root_path = '/kaggle/working/'
args.data_path = 'converted.csv'
args.features = 'S'
args.target = 'sales'
args.freq = 't'
args.checkpoints = './informer_checkpoint... | code |
128008497/cell_3 | [
"text_html_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import pandas as pd
from tqdm import tqdm
tqdm.pandas()
df = pd.read_csv('/kaggle/input/demand-forecasting-kernels-only/train.csv')
df
df['date'] = df.progress_apply(lambda x: f"{x['date']} {x['store']}:{x['item']}", axis=1)
df | code |
128008497/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from utils.tools import dotdict
import torch
args = dotdict()
args.model = 'informer'
args.data = 'custom'
args.root_path = '/kaggle/working/'
args.data_path = 'converted.csv'
args.features = 'S'
args.target = 'sales'
args.freq = 't'
args.checkpoints = './informer_checkpoints'
args.seq_len = 96
args.label_len = 48
ar... | code |
128008497/cell_5 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import pandas as pd
import pandas as pd
from tqdm import tqdm
tqdm.pandas()
df = pd.read_csv('/kaggle/input/demand-forecasting-kernels-only/train.csv')
df
df['date'] = df.progress_apply(lambda x: f"{x['date']} {x['store']}:{x['item']}", axis=1)
df
df['date'] = pd.to_datetime(df['date'])
df = d... | code |
104119002/cell_13 | [
"image_output_1.png"
] | from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np
def plot_examples(colormaps):
"""
Helper function to plot data with associated colormap.
"""
np.random.seed(19680801)
data = np.random.randn(30, 30)
n = len(colormaps)
... | code |
104119002/cell_34 | [
"text_html_output_1.png"
] | from google.colab import drive
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Caps... | code |
104119002/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import h3
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import numpy as np
import calendar
import geopandas as gpd
from shapely.geometry import Point, Polygon
... | code |
104119002/cell_11 | [
"text_plain_output_1.png"
] | from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np
def plot_examples(colormaps):
"""
Helper function to plot data with associated colormap.
"""
np.random.seed(19680801)
data = np.random.randn(30, 30)
n = len(colormaps)
... | code |
104119002/cell_28 | [
"text_html_output_1.png"
] | from google.colab import drive
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Caps... | code |
104119002/cell_15 | [
"image_output_1.png"
] | import calendar
months_order = [7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6]
month_names = {i: name for i, name in enumerate(calendar.month_name) if i != 0}
print(month_names)
days = {name: i + 1 for i, name in enumerate(calendar.day_name)}
print(days)
days_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'S... | code |
104119002/cell_17 | [
"image_output_1.png"
] | from google.colab import drive
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Capstone Projec... | code |
104119002/cell_35 | [
"text_html_output_1.png"
] | from google.colab import drive
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Caps... | code |
104119002/cell_24 | [
"text_html_output_1.png"
] | from google.colab import drive
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Caps... | code |
104119002/cell_22 | [
"text_plain_output_1.png"
] | from google.colab import drive
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Notebooks/Google Caps... | code |
104119002/cell_37 | [
"text_html_output_1.png"
] | from google.colab import drive
import calendar
import h3
import pandas as pd
kaggle = False
try:
from google.colab import drive
drive.mount('/content/drive')
except ModuleNotFoundError:
path = '../input/google-bike-share/clean_df.csv'
kaggle = True
else:
path = '/content/drive/MyDrive/Colab Note... | code |
104119002/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import matplotlib.pyplot as plt
import numpy as np
def plot_examples(colormaps):
"""
Helper function to plot data with associated colormap.
"""
np.random.seed(19680801)
data = np.random.randn(30, 30)
n = len(colormaps)
... | code |
104119002/cell_5 | [
"text_html_output_1.png"
] | !pip install h3
!pip install geopandas | code |
88081278/cell_4 | [
"image_output_1.png"
] | import albumentations as A
import cv2
import matplotlib.pyplot as plt
import os
import random
import os
import random
from multiprocessing import Pool
import numpy as np
import cv2
import albumentations as A
import matplotlib.pyplot as plt
from tqdm import tqdm
INPUT_PATH = '../input/happy-whale-and-dolphin'
IMG_S... | code |
88081278/cell_3 | [
"text_plain_output_1.png"
] | import os
import random
import os
import random
from multiprocessing import Pool
import numpy as np
import cv2
import albumentations as A
import matplotlib.pyplot as plt
from tqdm import tqdm
INPUT_PATH = '../input/happy-whale-and-dolphin'
IMG_SIZE = 600
train_files = os.listdir(os.path.join(INPUT_PATH, 'train_image... | code |
88081278/cell_5 | [
"application_vnd.jupyter.stderr_output_116.png",
"application_vnd.jupyter.stderr_output_74.png",
"application_vnd.jupyter.stderr_output_268.png",
"application_vnd.jupyter.stderr_output_145.png",
"application_vnd.jupyter.stderr_output_362.png",
"application_vnd.jupyter.stderr_output_493.png",
"applicatio... | from multiprocessing import Pool
from tqdm import tqdm
import albumentations as A
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import os
import random
from multiprocessing import Pool
import numpy as np
import cv2
import albumentations as A
import matplotlib.pyplot as pl... | code |
88080932/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1),... | code |
88080932/cell_13 | [
"text_plain_output_1.png"
] | X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test_df.drop('quality', axis=1), test_df['quality'])
(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape) | code |
88080932/cell_9 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
from sklearn.model_selection import train_test_split
train_df, test_df = train_test_split(wine_quality_df, test_size=0.2, stratify=wine_quality_df['q... | code |
88080932/cell_4 | [
"image_output_1.png"
] | import pandas as pd
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
wine_quality_df.info() | code |
88080932/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
imp... | code |
88080932/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test_df.drop('quality', axis=1), test_df['quality'])
(X_train.sha... | code |
88080932/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
wine_quality_df['quality'].value_counts() | code |
88080932/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test... | code |
88080932/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test... | code |
88080932/cell_11 | [
"text_plain_output_1.png"
] | train_df.describe() | code |
88080932/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import tensorflow as tf
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_d... | code |
88080932/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test_df.drop('quality', axis=1), test_df['quality'])
(X_train.shape, y_train.shape, X... | code |
88080932/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
... | code |
88080932/cell_28 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import pandas as pd
import tensorflow as tf
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
X_train, y_train = (train_df.drop('quality', ax... | code |
88080932/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
wine_quality_df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv', index_col='Id')
wine_quality_df.head() | code |
88080932/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test... | code |
88080932/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test... | code |
88080932/cell_10 | [
"text_html_output_1.png"
] | train_df.corr()['quality'].plot.bar() | code |
88080932/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.utils.class_weight import compute_class_weight
import numpy as np
import tensorflow as tf
X_train, y_train = (train_df.drop('quality', axis=1), train_df['quality'])
X_val, y_val = (val_df.drop('quality', axis=1), val_df['quality'])
X_test, y_test = (test... | code |
106210100/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
106210100/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
106210100/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
for table in tables:
print(table.table_id) | code |
106210100/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
106210100/cell_2 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client() | code |
106210100/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
106210100/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
106210100/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('san_francisco_bikeshare', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref_regions = dataset_ref.table('bikeshare_regions')
tabl... | code |
90104537/cell_13 | [
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
90104537/cell_4 | [
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
90104537/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
90104537/cell_8 | [
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
90104537/cell_10 | [
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
90104537/cell_12 | [
"image_output_1.png"
] | import matplotlib.pylab as pylab
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'x-large', 'figure.figsize': (15, 10), 'axes.labelsize... | code |
32071924/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
train_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_df = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.... | code |
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