path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90133854/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum()
ge1 = ge.copy()
cat_ge = list(ge.select_dtypes(exclude='float64').columns)
cat_ge | code |
90133854/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum() | code |
90133854/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum()
ge1 = ge.copy()
cat_ge = list(ge.select_dtypes(exclude='float64... | code |
90133854/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum()
ge1 = ge.copy()
cat_ge = list(ge.select_dtypes(exclude='float64').columns)
num_ge = list(ge.select_dtypes(include='flo... | code |
90133854/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T
ge.isnull().sum()
ge1 = ge.copy()
cat_ge = list(ge.select_dtypes(exclude='float64').columns)
num_ge = list(ge.sel... | code |
90133854/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge | code |
90133854/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
ge = pd.read_csv('../input/gender-classification-dataset/gender_classification_v7.csv')
ge
x = 0
for i in ge.columns:
x = x + 1
ge.describe().round(2).T | code |
2022945/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
2022945/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv', index_col=0)
test = pd.read_csv('../input/test.csv', index_col=0)
train = train[train['GrLivArea'] < 4000]
labels = train['SalePrice']
train = train.drop('SalePrice', axis=1)
all_data = pd.concat([train, test])
nums = all_data.select_dtypes(exclude=['ob... | code |
106196332/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
pokemon = pd.read_csv('../input/pokemon/Pokemon.csv')
print(pokemon.info())
print(pokemon.describe()) | code |
72063406/cell_13 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn import model_selection
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_sub = pd.r... | code |
72063406/cell_11 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn import model_selection
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_sub = pd.r... | code |
72063406/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 |
72063406/cell_12 | [
"text_plain_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn import model_selection
from sklearn.metrics import mean_squared_error
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_sub = pd.r... | code |
18144904/cell_6 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
train_datagen = Ima... | code |
18144904/cell_2 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255, horizontal_flip=True, vertical_flip=True, validation_split=0.2)
def get_generator(path, subset):
return train_datagen.flow_from_directory(path, target_size=(200, 400), batch_size=32, class_mode='categoric... | code |
18144904/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator | code |
18144904/cell_8 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
train_datagen = Ima... | code |
18144904/cell_5 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Dense, Conv2D, Flatten
from keras.models import Sequential, Model
from keras.optimizers import Adagrad
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255... | code |
128034943/cell_4 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
import glob
import librosa
import numpy as np
import os
import pandas as pd
import random
def load_metadata(file_path):
re... | code |
128034943/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import glob
import random
import concurrent.futures
import numpy as np
import pandas as pd
import librosa
from sklearn.model_selection import train_test_split, StratifiedKFold, RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from joblib import ... | code |
122257222/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)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.describe() | code |
122257222/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
122257222/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
122257222/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.head() | code |
122257222/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline, make_pipeline
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_pr... | code |
122257222/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)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data.info() | code |
122257222/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
122257222/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 |
122257222/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.tail() | code |
122257222/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspiration', 'doornumber', ... | code |
122257222/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape | code |
122257222/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique() | code |
122257222/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.head() | code |
122257222/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspiration', 'doornumber', ... | code |
122257222/cell_24 | [
"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 seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
122257222/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()] | code |
122257222/cell_22 | [
"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 seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
122257222/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample() | code |
122257222/cell_27 | [
"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 seaborn as sns
data = pd.read_csv('/kaggle/input/car-price-prediction/car_price.csv')
data.shape
data.sample()
data[data.duplicated()]
data.nunique()
features = data.copy()
features.drop(columns=['fueltype', 'aspi... | code |
128020140/cell_6 | [
"image_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV
import pandas as pd
learning_rate = 0.001
weight_decay = 0.0001
batch_size = 256
num_epochs = 1000
image_size = 72
patch_size = 6
num_patches = (image_s... | code |
128020140/cell_16 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import tensorflow as tf
import tensorflow_addons as tfa
learnin... | code |
128020140/cell_17 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
import ... | code |
121153835/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/inp... | code |
121153835/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
121153835/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
print(f'the competition dataset shape is {df.shape}') | code |
121153835/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStreng... | code |
121153835/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
from sklearn.model_selection import train_test_split, KFold
from sklearn.metrics import mean_squared_error
import lightgbm as lgbm
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in ... | code |
121153835/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
121153835/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/inp... | code |
121153835/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
print(f'the addition dataset ... | code |
121153835/cell_14 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
... | code |
121153835/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(column... | code |
121153835/cell_12 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
... | code |
121153835/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv')
df.drop(columns='id', inplace=True)
df_add = pd.read_csv('/kaggle/input/predict-concrete-strength/ConcreteStrengthData.csv')
df_add.rename(columns={'CementComponent ': 'CementComponent'}, inplace=True)
df = pd.concat([df, df_add])
... | code |
106198993/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/a-dataset-of-art-and-history-book-pruchases/ArtHistBooks.csv')
df.describe() | code |
106198993/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 |
106198993/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from scipy.stats import binom
import numpy as np # linear algebra
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
sns.set(rc={'figure.figsize': (1.6 * 8, 8)})
from scipy.stats import binom
x = np.arange(0, 1, 0.01)
L = binom.pmf(k=301, n=1000, p=x)
prior_prob = ... | code |
106198993/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/a-dataset-of-art-and-history-book-pruchases/ArtHistBooks.csv')
df | code |
106198993/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/a-dataset-of-art-and-history-book-pruchases/ArtHistBooks.csv')
df_ArtPurchase = df.loc[df['ArtBooks'] > 0]
df_ArtPurchase | code |
72094873/cell_4 | [
"image_output_1.png"
] | import os
flairs = t1ws = t2ws = t1gds = 0
study = {}
for p in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'):
for i in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/' + p):
study[p] = {}
if i == 'FLAIR':
flairs = len(os... | code |
72094873/cell_18 | [
"text_html_output_1.png"
] | from ipywidgets import interact
import matplotlib.pyplot as plt
import os
import pandas as pd
import pydicom as dcm
import seaborn as sns
flairs = t1ws = t2ws = t1gds = 0
study = {}
for p in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'):
for i in os.listdir('../input/rsna-mi... | code |
72094873/cell_8 | [
"image_png_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
flairs = t1ws = t2ws = t1gds = 0
study = {}
for p in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'):
for i in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/' + p... | code |
72094873/cell_17 | [
"image_output_2.png",
"image_output_1.png"
] | s = Study('00000')
s.show('FLAIR', 100) | code |
72094873/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
flairs = t1ws = t2ws = t1gds = 0
study = {}
for p in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'):
for i in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/' + p... | code |
72094873/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
flairs = t1ws = t2ws = t1gds = 0
study = {}
for p in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train'):
for i in os.listdir('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/' + p... | code |
88099239/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set... | code |
88099239/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
88099239/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import LinearSVC, SVC # Linear Support Vector Classification
import numpy as np
import panda... | code |
88099239/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import LinearSVC, SVC # Linear Support Vector Classification
import numpy as np
import pandas as pd
import pandas as pd
import numpy as n... | code |
88099239/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
88099239/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
88099239/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
88099239/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import LinearSVC, SVC # Linear Support Vector Classification
import numpy as np
import panda... | code |
88099239/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
import numpy as np
import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set... | code |
88099239/cell_22 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import LinearSVC, SVC # Linear Support Vector Classification
import numpy as np
import panda... | code |
88099239/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
88099239/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
payments = pd.read_csv('../input/credit-risk-classification-dataset/payment_data.csv')
payments = payments.set_index('id')
customers = pd.read_csv('../input/credit-risk-classification-dataset/customer_data.csv')
customers = customers.set_index('id')
customer_data = customers.join(payments)
customer... | code |
72068232/cell_25 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts()
df = df[df.sentiment != 'anger']
df = ... | code |
72068232/cell_34 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from gingerit.gingerit import GingerIt
from tqdm.notebook import tqdm
from symspellpy.symspellpy import SymSpell, Verbosity
import pkg_resources
import re, string, json
import spacy
def normalization_pipeline(sentences):
sentences = simplify_punctuation_and_whitespace(sentences)
sentences = normalize_contract... | code |
72068232/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts()
df = df[df.sentiment != 'anger']
df = ... | code |
72068232/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
from gingerit.gingerit import GingerIt
from sklearn.model_selection import train_test_split
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/em... | code |
72068232/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:] | code |
72068232/cell_18 | [
"text_html_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts()
df = df[df.sentiment != 'anger']
df = df[df.sentiment != 'boredom']
df = df[df.sentiment != ... | code |
72068232/cell_28 | [
"text_plain_output_1.png"
] | # Install spaCy (run in terminal/prompt)
import sys
!{sys.executable} -m pip install spacy
# Download spaCy's 'en' Model
!{sys.executable} -m spacy download en
!pip install -U symspellpy
!pip install gingerit
from gingerit.gingerit import GingerIt
#emoticons
!pip install emot --upgrade
import emot
emot_obj = emot.cor... | code |
72068232/cell_17 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts()
df = df[df.sentiment != 'anger']
df = df[df.sentiment != 'boredom']
df = df[df.sentiment != ... | code |
72068232/cell_14 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.content.iloc[-10:]
df.sentiment.value_counts()
df = df[df.sentiment != 'anger']
df = df[df.sentiment != 'boredom']
df = df[df.sentiment != ... | code |
72068232/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
df = pd.read_csv('/kaggle/input/emotion-detection-from-text/tweet_emotions.csv')
df.head() | code |
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