path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
121149216/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
pvt =... | code |
121149216/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
mo['b... | code |
121149216/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mo = pd.read_csv('../input/ukrainian-market-mobile-phones-data/phones_data.csv')
mo.shape
mo.columns
mo.dtypes
mo.isnull().sum()
viz_data = mo.copy(True)
viz_data['release_date'].value_counts(normalize=True).sort_values(ascending=False)
viz_d... | code |
49127755/cell_4 | [
"text_plain_output_1.png"
] | import tensorflow as tf
model = tf.keras.models.load_model('../input/cassava-abhinay/model')
model.summary() | code |
49127755/cell_6 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test_datagen_v2 = ImageDataGenerator(rescale=1.0 / 255)
test_dir = '../input/cassava-leaf-disease-classification/test_images/'
test = pd.DataFrame()
test['image_id'] = ... | code |
49127755/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow as tf
model = tf.keras.models.load_model('../input/cassava-abhinay/model')
model.summa... | code |
130011540/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20) | code |
130011540/cell_25 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
print(np.version)
print(np.__version__) | code |
130011540/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_40 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_29 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_39 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10) | code |
130011540/cell_45 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100 | code |
130011540/cell_32 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
print(arr_10to50) | code |
130011540/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25) | code |
130011540/cell_38 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_43 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1) | code |
130011540/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
print(arr_10to50_even) | code |
130011540/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_37 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3)
np.random.rand(1)
np.random.randn(25)
np.arange(1, 101).reshape(10, 10) / 100
np.linspace(0.01, 1, 100).reshape(10, 10)
np.linspace(0, 1, 20)
arr_2_d = np.arange(1, 26).re... | code |
130011540/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np
arr_10to50 = np.arange(10, 51)
arr_10to50_even = np.arange(10, 51, 2)
np.arange(1, 10).reshape(3, 3) | code |
50234943/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=1000, random_state=42)
rf.fit(train_features, train_labels) | code |
50234943/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
features.iloc[:, 5:].head(5) | code |
50234943/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
print('The shape of our features is:', features.shape) | code |
50234943/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
import numpy as np
labels = np.array(features['actual'])
features = features.drop('actual', axis=1)
feature_list = ... | code |
50234943/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features.head(5) | code |
50234943/cell_7 | [
"text_plain_output_1.png"
] | print('Training Features Shape:', train_features.shape)
print('Training Labels Shape:', train_labels.shape)
print('Testing Features Shape:', test_features.shape)
print('Testing Labels Shape:', test_labels.shape) | code |
50234943/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features.describe() | code |
50234943/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
import numpy as np
import pandas as pd
import pandas as pd
features = pd.read_csv('../input/tempscsv/temps.csv')
features = pd.get_dummies(features)
import numpy as np
labels = np.array(features['actual'])
features = features.drop('actual', axis=1)
feature_list = ... | code |
130016222/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = ... | code |
130016222/cell_2 | [
"text_plain_output_1.png"
] | !ls ../input/nlp-getting-started | code |
130016222/cell_11 | [
"text_plain_output_1.png"
] | from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = ... | code |
130016222/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 |
130016222/cell_18 | [
"text_plain_output_1.png"
] | !ls | code |
130016222/cell_8 | [
"text_plain_output_1.png"
] | from transformers import pipeline
clsfr = pipeline('text-classification', device=0) | code |
130016222/cell_15 | [
"text_plain_output_1.png"
] | from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = ... | code |
130016222/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import numpy as np
import torch | code |
130016222/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datasets import load_dataset, Features, Value, Dataset
from transformers import pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftrn = pd.read_csv('../input/nlp-getting-started/train.csv')
dftes = pd.read_csv('../input/nlp-getting-started/test.csv')
dfsub = ... | code |
106201490/cell_21 | [
"text_plain_output_1.png"
] | from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = ... | code |
106201490/cell_9 | [
"text_plain_output_1.png"
] | from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
from vncorenlp import VnCoreNLP
vncorenlp = VnCoreNLP('../input/vncorenlp/VnCoreNLP-1.1.1.jar', annotators='wseg,pos,ner,parse', max_heap_size='-Xmx2g')
vncorenlp_example = vncorenlp.tokenize('nắng chiếu lung linh trên hoa vàng')
print(vncorenlp_exa... | code |
106201490/cell_25 | [
"text_plain_output_1.png"
] | from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = ... | code |
106201490/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
print(df.shape)
df.head() | code |
106201490/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.neural_network import MLPClassifier
mlp_classifier = MLPClassifier(learning_rate_init=0.0001, random_state=0, max_iter=300, hidden_layer_sizes=(256, 64)).fit(X_train, y_train)
print('Evaluate:')
p... | code |
106201490/cell_23 | [
"text_plain_output_1.png"
] | !pip install tqdm | code |
106201490/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.neural_network import MLPClassifier
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.neural_network import MLPClassifier
mlp_classifier = MLPClassifier(learning_rate_init=0.0001, random_state=0, max_iter=300, hidden_layer_sizes=(256, 64)).fit(X_train, y_train) | code |
106201490/cell_6 | [
"text_plain_output_1.png"
] | with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
token_stop_words = ['_'.join(stop_word.split()) for stop_word in stop_words]
print(token_stop_words[:10]) | code |
106201490/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import Perceptron
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
from sklearn.linear_model import Perceptron
perceptron = Perceptron(tol=2e-05, random_state=0)
perceptron.fit(X_train, y_train)
print('Evaluate:')
print(f'... | code |
106201490/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
print(labels.shape) | code |
106201490/cell_2 | [
"text_plain_output_1.png"
] | import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
print(Config.seed) | code |
106201490/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 |
106201490/cell_7 | [
"text_plain_output_1.png"
] | # !pip3 install underthesea
!pip install vncorenlp | code |
106201490/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
print(df.head()) | code |
106201490/cell_32 | [
"text_plain_output_1.png"
] | (X_train.shape, y_train.shape, X_test.shape, y_test.shape) | code |
106201490/cell_28 | [
"text_plain_output_1.png"
] | import collections
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
value_labels = list(set(lab... | code |
106201490/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | !pip install underthesea | code |
106201490/cell_38 | [
"text_plain_output_1.png"
] | (X_train.shape, y_train.shape, X_test.shape, y_test.shape)
(X_train.shape, y_train.shape, X_test.shape, y_test.shape) | code |
106201490/cell_3 | [
"text_plain_output_1.png"
] | import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
print(string.punctuation) | code |
106201490/cell_17 | [
"text_plain_output_1.png"
] | from underthesea import word_tokenize
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
from vncorenlp import VnCoreNLP
vncorenlp = V... | code |
106201490/cell_35 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
... | code |
106201490/cell_14 | [
"text_plain_output_1.png"
] | with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
token_stop_words = ['_'.join(stop_word.split()) for stop_word in stop_words]
print(token_stop_words[:10]) | code |
106201490/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
seed = 44
data_path = ... | code |
106201490/cell_27 | [
"text_plain_output_1.png"
] | import collections
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_path = '../input/vietnamese-text-classification-dataset/train.csv'
df = pd.read_csv(data_path, header=None, names=['label', 'text'])
labels = df['label'].values
value_labels = list(set(labels))
print(value_labels)
import ... | code |
106201490/cell_12 | [
"text_plain_output_1.png"
] | import fasttext.util
cbow_pretrain = fasttext.load_model('../input/cbow-model/cc.vi.300.bin') | code |
106201490/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | with open('../input/vietnamese-stopwords/vietnamese-stopwords.txt', 'r') as f:
stop_words = f.read().split('\n')
print(len(stop_words))
print(stop_words[:10]) | code |
106201490/cell_36 | [
"image_output_1.png"
] | from tqdm import tqdm
from vncorenlp import VnCoreNLP
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
import string
class Config:
max_sequence_length = -1
word_vec = -1
punctuations = f'[{string.punctuation}\\d\n]'
... | code |
74054244/cell_21 | [
"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)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include... | code |
74054244/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
dat... | code |
74054244/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
prin... | code |
74054244/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
dat... | code |
74054244/cell_19 | [
"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)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include... | code |
74054244/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 |
74054244/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
data_zom.info() | code |
74054244/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
dat... | code |
74054244/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
num_fea = data_zom.select_dtypes(include=['int', 'float']).columns.to_list()
cat_fea = data_zom.select_dtypes(include='object').columns.to_list()
dat... | code |
74054244/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_zom = pd.read_csv('/kaggle/input/zomato-restaurants-in-delhi-ncr/DelhiNCR Restaurants.csv')
print('Size of dataset is: ', data_zom.shape)
data_zom.head() | code |
34150823/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import random
import torch
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as pd
from tqdm import tqdm_notebook as tqdm
from torch.optim.lr_scheduler import ReduceLROnPlateau
from sklearn.... | code |
34150823/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape | code |
34150823/cell_20 | [
"text_plain_output_1.png"
] | from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, Dataset, sampler
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import time
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Im... | code |
34150823/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
classes = labels['breed'].unique()
classes | code |
34150823/cell_19 | [
"text_html_output_1.png"
] | from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader, Dataset, sampler
from torchvision import models
import numpy as np
import os
import pandas as pd
import random
import time
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Im... | code |
34150823/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.shape
df1 = labels['breed']
df2 = labels['label_id']
df1 = pd.get_dummies(df1)
df = pd.concat([df2, df1], axis=1)
df.head() | code |
34150823/cell_18 | [
"text_plain_output_1.png"
] | from torch.optim.lr_scheduler import ReduceLROnPlateau
from torchvision import models
import numpy as np
import os
import random
import torch
import torch.nn as nn
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
import random
import numpy as np
import pandas as... | code |
34150823/cell_15 | [
"text_plain_output_1.png"
] | from torchvision import models
from torchvision import models
resnet = models.resnet18(pretrained=True, progress=True) | code |
34150823/cell_3 | [
"image_output_1.png"
] | import pandas as pd
labels = pd.read_csv('/kaggle/input/oxford-iiit/labels')
labels.head() | code |
34150823/cell_14 | [
"text_html_output_1.png"
] | from albumentations.pytorch import ToTensor
from torch.utils.data import DataLoader, Dataset, sampler
import albumentations as albu
import numpy as np
import os
import pandas as pd
import random
import torch
import warnings
import os
import cv2
from PIL import Image
import time
import copy
import warnings
impo... | code |
32065763/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
32065763/cell_2 | [
"text_plain_output_1.png"
] | !pip install PySastrawi | code |
32065763/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
32065763/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
32065763/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
32065763/cell_16 | [
"text_plain_output_1.png"
] | from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
import pandas as pd
import re
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1'... | code |
32065763/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
!ls '../input' | code |
32065763/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/data.csv', encoding='latin-1')
alay_dict = pd.read_csv('../input/indonesian-abusive-and-hate-speech-twitter-text/new_kamusalay.csv', encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: ... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.