path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2021796/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.cs... | code |
128044990/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_23 | [
"text_html_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, ward
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold... | code |
128044990/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram... | code |
128044990/cell_39 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, ward
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold... | code |
128044990/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_32 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, ward
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold... | code |
128044990/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_35 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, ward
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold... | code |
128044990/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram, ward
sns.set_theme(style='white', palette='viridis'... | code |
128044990/cell_37 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram, ward
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold... | code |
128044990/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold
from scipy.cluster.hierarchy import dendrogram... | code |
104128883/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.info() | code |
104128883/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.describe() | code |
104128883/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.head() | code |
104128883/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104128883/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.duplicated() | code |
104128883/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.duplicated()
insurance.sort_values(by='charges', ascending=1)
Q2 = insurance['charges'].median()
print(Q2) | code |
104128883/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape | code |
104128883/cell_14 | [
"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
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.duplicated()
plt.figure(figsize=(6, 7))
sns.boxplot(insurance['charges'])
... | code |
104128883/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.duplicated()
insurance['region'].unique() | code |
104128883/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum()
insurance.duplicated()
data_dummies = pd.get_dummies(insurance)
data_dummies.head() | code |
104128883/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
insurance = pd.read_csv('/kaggle/input/insurance/insurance.csv')
insurance.shape
insurance.isna().sum() | code |
89122142/cell_42 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_25 | [
"image_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import pathlib
import random
def plot_loss_curves(history):
"""
Returns separate loss curves for training and validation metrics.
Args:
history: TensorFlow model History object (see: https://www.t... | code |
89122142/cell_23 | [
"text_plain_output_1.png"
] | import os
def walk_through_dir(dir_path):
"""
Walks through dir_path returning its contents.
Args:
dir_path (str): target directory
Returns:
A print out of:
number of images (files) in each subdirectory
name of each subdirectory
"""
walk_through_dir('101_foo... | code |
89122142/cell_44 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_55 | [
"text_plain_output_1.png"
] | import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files.
Stores log files with the filepath: "dir_name/experiment_name/current_datetime/"
Args:
dire_name: target directory to store TensorBoard... | code |
89122142/cell_39 | [
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import matplotlib.pyplot as plt
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard ca... | code |
89122142/cell_26 | [
"image_output_1.png"
] | import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import pathlib
import random
def plot_loss_curves(history):
"""
Returns separate loss curves for training and validation metrics.
Args:
history: TensorFlow model History object (see: https://www.t... | code |
89122142/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_54 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_50 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_52 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_45 | [
"image_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import matplotlib.pyplot as plt
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard ca... | code |
89122142/cell_18 | [
"text_plain_output_1.png"
] | !wget https://storage.googleapis.com/ztm_tf_course/food_vision/101_food_classes_10_percent.zip | code |
89122142/cell_28 | [
"text_plain_output_1.png"
] | import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files.
Stores log files with the filepath: "dir_name/experiment_name/current_datetime/"
Args:
dire_name: target directory to store TensorBoard... | code |
89122142/cell_35 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_43 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_53 | [
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
89122142/cell_37 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.models import Sequential
import datetime
import tensorflow as tf
def create_tensorboard_callback(dir_name, experiment_name):
"""
Creates a TensorBoard callback instand to store log files... | code |
73097309/cell_9 | [
"image_output_1.png"
] | from skimage.transform import resize
import SimpleITK as sitk
import SimpleITK as sitk
import os
import os
import os
import os
import pandas as pd
import pandas as pd
import torch
import torch
import torch
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
... | code |
73097309/cell_23 | [
"image_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from skimage.util import montage
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import matplotlib.pyplot as plt
import matpl... | code |
73097309/cell_2 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import os
import time
from random import randint
from keras.callbacks import CSVLogger
import numpy as np
from scipy import stats
import pandas as pd
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.model... | code |
73097309/cell_19 | [
"text_html_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import numpy as np
import numpy as np
import numpy as np
import numpy as np
... | code |
73097309/cell_15 | [
"text_plain_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import numpy as np
import numpy as np
import numpy as np
import numpy as np
... | code |
73097309/cell_16 | [
"image_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy ... | code |
73097309/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
labels = pd.read_csv('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train_labels.csv')
labels.head() | code |
73097309/cell_24 | [
"text_plain_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from skimage.util import montage
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import matplotlib.pyplot as plt
import matpl... | code |
73097309/cell_14 | [
"text_html_output_1.png"
] | from albumentations import Compose, HorizontalFlip
from skimage.transform import resize
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Dataset,DataLoader
import SimpleITK as sitk
import SimpleITK as sitk
import numpy as np
import numpy as np
import numpy as np
import numpy as np
... | code |
73097309/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from skimage.transform import resize
import SimpleITK as sitk
import SimpleITK as sitk
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import os
import os
import os
import pandas as pd
import pandas as pd
import torch
import torch
import torch
labels = pd.read_csv('../input/rsna-... | code |
73066496/cell_21 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique()
df_train = df_train.drop(['id'], axis=1)
df_train = shuffle(... | code |
73066496/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_test.nunique() | code |
73066496/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.info() | code |
73066496/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique()
df_train = df_train.drop(['id'], axis=1)
df_train = shuffle(... | code |
73066496/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique() | code |
73066496/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique()
df_train = df_train.drop(['id'], axis=1)
df_train = shuffle(... | code |
73066496/cell_7 | [
"image_output_11.png",
"application_vnd.jupyter.stderr_output_24.png",
"application_vnd.jupyter.stderr_output_16.png",
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_15.png",
"application_vnd.jupyter.stderr_output_18.png",
"text_plain_output_9.png",
"ima... | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.head() | code |
73066496/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique()
df_train = df_train.drop(['id'], axis=1)
df_train = shuffle(... | code |
73066496/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.describe() | code |
73066496/cell_22 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.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_8.png",
"text_plain_outp... | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_train.nunique()
df_train = df_train.drop(['id'], axis=1)
df_train = shuffle(... | code |
73066496/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
df_test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
df_test.info() | code |
72062718/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/blending-tool-tps-aug-... | code |
72062718/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearnex import patch_sklearn
from sklearnex import patch_sklearn
patch_sklearn() | code |
72062718/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/blending-tool-tps-aug-... | code |
72062718/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/blending-tool-tps-aug-... | code |
72062718/cell_32 | [
"text_plain_output_1.png"
] | !pip install scikit-learn-intelex -q --progress-bar off | code |
72062718/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/bl... | code |
72062718/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/blending-tool-tps-aug-... | code |
72062718/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('../input/tabular-playground-series-aug-2021/test.csv')
sample_sub = pd.read_csv('../input/tabular-playground-series-aug-2021/sample_submission.csv')
pseudo = pd.read_csv('../input/blending-tool-tps-aug-... | code |
128020426/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
df_train.describe() | code |
128020426/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
df_train.info() | code |
128020426/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
plt.figure(figsize=(16, 5))
sns.heatmap(df_train.corr(), cmap='c... | code |
128020426/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128020426/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
pp = sns.pairplot(data=df_train, y_vars=['yield'], x_vars=['frui... | code |
128020426/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/playground... | code |
128020426/cell_16 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
... | code |
128020426/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
df_train.head() | code |
128020426/cell_14 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv', index_col='id')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv', index_col='id')
X_train_fu... | code |
122247764/cell_57 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import cross_val_score
cl... | code |
122247764/cell_56 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import cross_val_score
cl... | code |
122247764/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
student = pd.read_csv('/kaggle/input/higher-education-predictors-of-student-retention/dataset.csv')
student.shape
student.columns
student.sample(4)
student.drop(student.index[student['Target'] == 'Enrolled'], inplace=True)
student.dtypes | code |
122247764/cell_55 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import cross_val_score
clf = RandomForestClassifier(ma... | code |
122247764/cell_54 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection import cross_val_score
clf = RandomForestClassifier(ma... | code |
122247764/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
clf = RandomForestClassifier(max_depth=10, random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Without Scaling and without CV: ', accuracy_score... | code |
122247764/cell_49 | [
"text_plain_output_1.png"
] | print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape) | code |
122247764/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.preproce... | code |
122247764/cell_46 | [
"text_html_output_1.png"
] | import pandas as pd
student = pd.read_csv('/kaggle/input/higher-education-predictors-of-student-retention/dataset.csv')
student.shape
student.columns
student.sample(4)
student.drop(student.index[student['Target'] == 'Enrolled'], inplace=True)
student.dtypes
student.corr()['Target']
student_df = student.iloc[:, ... | code |
122247764/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
student = pd.read_csv('/kaggle/input/higher-education-predictors-of-student-retention/dataset.csv')
student.shape
student.columns
student.sample(4)
student.drop(student.index[student['Target'] == 'Enrolled'], inplace=True)
student.dtypes
student.describe() | code |
122247764/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
student = pd.read_csv('/kaggle/input/higher-education-predictors-of-student-retention/dataset.csv')
student.shape
student.columns
student.sample(4)
student.drop(student.index[student['Target'] == 'Enrolled'], inplace=True)
student | code |
122247764/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.model_selection import cross_val_score
clf = RandomForestClassifier(max_depth=10, random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.pre... | code |
129012199/cell_21 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_13 | [
"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 numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_9 | [
"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 numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_23 | [
"text_html_output_1.png"
] | 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
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
129012199/cell_11 | [
"text_html_output_1.png"
] | 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
df = pd.read_csv('/kaggle/input/videogamesales/vgsales.csv')
df
most_common_pub = df.groupby('Publisher').count().sort_values(by='Rank', ascending=False).head(1).index[0]
most_common_pub
... | code |
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