path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34127932/cell_54 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.describe() | code |
34127932/cell_52 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_49 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_62 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import numpy as np #for mathematical manipulation of the data
import pandas as pd #for structuring the data
train_data = pd.read_csv('... | code |
34127932/cell_58 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabi... | code |
34127932/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.info() | code |
34127932/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
os.getcwd()
os.chdir('/kaggle/input')
os.listdir() | code |
34127932/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
34127932/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.head() | code |
34127932/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passeng... | code |
34127932/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1... | code |
16114195/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
train['SalePrice'].hist(bins=50)
y ... | code |
16114195/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
16114195/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16114195/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['SalePrice'].hist(bins=50) | code |
16114195/cell_10 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16114195/cell_12 | [
"text_plain_output_1.png",
"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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(... | code |
16114195/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
129020867/cell_2 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.models import Model
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import numpy as np
import tensorflow as tf
def seed_everything(SEED):
np.random.seed(SEED)
tf.random.set_seed(SEED)
seed = 42
seed_everything(seed)
'\nResUNet++ architecture in Ke... | code |
129020867/cell_7 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
from matplotlib import pyplot as plt
imshow(x_train.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_train.next()[0].astype(np.float32)))
plt.show()
imshow(x_val.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_val.next()[0].astype(np.float32)))
plt.show() | code |
129020867/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from skimage.io import imshow
from matplotlib import pyplot as plt
imshow(x_test.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_pred[0].astype(np.float32)))
plt.show()
imshow(y_test.next()[0].astype(np.float32))
plt.show() | code |
129020867/cell_17 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, ... | code |
129020867/cell_14 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, ... | code |
129020867/cell_10 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.... | code |
90147502/cell_21 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90147502/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape | code |
90147502/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape | code |
90147502/cell_57 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_d... | code |
90147502/cell_23 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_d... | code |
90147502/cell_6 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.head() | code |
90147502/cell_48 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/... | code |
90147502/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.info() | code |
90147502/cell_60 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seabo... | code |
90147502/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 |
90147502/cell_7 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.info() | code |
90147502/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90147502/cell_62 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | code |
90147502/cell_58 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_d... | code |
90147502/cell_28 | [
"text_plain_output_1.png"
] | import string
import string
import string
import re
string.punctuation | code |
90147502/cell_8 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.describe() | code |
90147502/cell_15 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90147502/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90147502/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/... | code |
90147502/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90147502/cell_24 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_d... | code |
90147502/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.head() | code |
90147502/cell_12 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.describe() | code |
90147502/cell_36 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
stopwords.words('english') | code |
72105010/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
featur... | code |
72105010/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
print(train.shape)
print(test.shape) | code |
72105010/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
sns.heatmap(train.isnull()) | code |
72105010/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from xgboost imp... | code |
72105010/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
features = train.drop(columns=['target', 'id'], axis=1... | code |
72105010/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 |
72105010/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
sns.pairplot(train) | code |
72105010/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
model_base = LinearRegression()
model_base.fit(X_train, y_train)
preds_valid_base = model_base.predict(X_valid)
print('MAE', mean_squared_error(y_valid, preds_valid_base, squared=False))
print('r2', r2_score(y_v... | code |
72105010/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
train.info() | code |
33110459/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --apt-packages libomp5 libopenblas-dev | code |
33110459/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(im... | code |
33110459/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33110459/cell_8 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(im... | code |
33110459/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33110459/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim a... | code |
33110459/cell_10 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in c... | code |
33110459/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(im... | code |
105188182/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df.plot(kind='box', subplots=True, figsize=(18, 15), layout=(5, 5))
plt.show() | code |
105188182/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns | code |
105188182/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
plt.figure... | code |
105188182/cell_40 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
plt.figure(figsize=(20, 10))
sns.heatmap(df.isnull()) | code |
105188182/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum() | code |
105188182/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
plt.figure... | code |
105188182/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
plt.figure(figsize=(20, 10))
sns.heatmap(df.isnull()) | code |
105188182/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Flight Distance', y='Satisfaction', data... | code |
105188182/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum() | code |
105188182/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum() | code |
105188182/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.dr... | code |
105188182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape | code |
74048227/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playgrou... | code |
74048227/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
train... | code |
74048227/cell_20 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', ... | code |
74048227/cell_19 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', ... | code |
74048227/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
print... | code |
74048227/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
train... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.