path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130003964/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp
salary = df.Salary.values
salary
x = year... | code |
130003964/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp
salary = df.Salary.values
salary
x = years_exp
y = salary
plt.scatter(x, y, color='blue')
p... | code |
130003964/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(x_train, y_train)
y_predict = lr.predict([[1.2], [3.3]])
y_predict | code |
130003964/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(x_train, y_train)
y_predict = lr.predict([[1.2], [3.3]])
y_predict
lr.score(x_test, y_test) * 100 | code |
130003964/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/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns | code |
130003964/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(x_train, y_train)
y_predict = lr.predict([[1.2], [3.3]])
y_predict
lr.score(x_test, y_test) * 100
y_predict = lr.predict(x_test)
y_predict | code |
130003964/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(x_train, y_train) | code |
130003964/cell_12 | [
"text_plain_output_1.png"
] | (x_test, len(x_test)) | code |
130003964/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/salary-dataset-simple-linear-regression/Salary_dataset.csv')
df.columns
years_exp = df.YearsExperience.values
years_exp | code |
128048739/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_transform(sentences)
X.toarray() | code |
128048739/cell_33 | [
"text_html_output_1.png"
] | from transformers import AutoTokenizer, BertModel
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased') | code |
128048739/cell_20 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this th... | code |
128048739/cell_26 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this th... | code |
128048739/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_transform(sentences)
X.toarray()
cv.get_feature_names_out() | code |
128048739/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this th... | code |
128048739/cell_15 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_35 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, BertModel
import torch
from transformers import AutoTokenizer, BertModel
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')
example_word = 'cyber'
example_token_id = tokenizer.convert_tokens_to_ids([example_wor... | code |
128048739/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
128048739/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this th... | code |
128048739/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sentences = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?']
cv = CountVectorizer()
X = cv.fit_trans... | code |
32067919/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedCases'] >= 0]
train_.head() | code |
32067919/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedCases'] >= 0]
EMPTY_VAL = 'EMPTY_VAL'
def fillState(state, country):
if state == EMPTY_VAL:
re... | code |
32067919/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedCases'] >= 0]
EMPTY_VAL... | code |
32067919/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedC... | code |
32067919/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train.head() | code |
32067919/cell_17 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedC... | code |
32067919/cell_14 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedC... | code |
32067919/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedCases'] >= 0]
EMPTY_VAL... | code |
32067919/cell_12 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.optimize as opt
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_ = train[train['ConfirmedC... | code |
32068517/cell_13 | [
"text_plain_output_1.png"
] | PATH = '/kaggle/input/covid19-global-forecasting-week-4/'
train_df = pd.read_csv(PATH + 'train.csv', parse_dates=['Date'])
test_df = pd.read_csv(PATH + 'test.csv', parse_dates=['Date'])
add_datepart(train_df, 'Date', drop=False)
add_datepart(test_df, 'Date', drop=False)
train_df.shape
PATH1 = '/kaggle/input/covid19-co... | code |
32068517/cell_20 | [
"text_html_output_1.png"
] | PATH = '/kaggle/input/covid19-global-forecasting-week-4/'
train_df = pd.read_csv(PATH + 'train.csv', parse_dates=['Date'])
test_df = pd.read_csv(PATH + 'test.csv', parse_dates=['Date'])
add_datepart(train_df, 'Date', drop=False)
add_datepart(test_df, 'Date', drop=False)
train_df.shape
PATH1 = '/kaggle/input/covid19-co... | code |
32068517/cell_2 | [
"text_plain_output_1.png"
] | !pip install fastai2
!pip install fast_tabnet | code |
32068517/cell_3 | [
"text_plain_output_1.png"
] | import os
from fastai2.basics import *
from fastai2.tabular.all import *
from fast_tabnet.core import *
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068517/cell_17 | [
"text_plain_output_1.png"
] | PATH = '/kaggle/input/covid19-global-forecasting-week-4/'
train_df = pd.read_csv(PATH + 'train.csv', parse_dates=['Date'])
test_df = pd.read_csv(PATH + 'test.csv', parse_dates=['Date'])
add_datepart(train_df, 'Date', drop=False)
add_datepart(test_df, 'Date', drop=False)
train_df.shape
PATH1 = '/kaggle/input/covid19-co... | code |
32068517/cell_5 | [
"text_html_output_1.png"
] | PATH = '/kaggle/input/covid19-global-forecasting-week-4/'
train_df = pd.read_csv(PATH + 'train.csv', parse_dates=['Date'])
test_df = pd.read_csv(PATH + 'test.csv', parse_dates=['Date'])
add_datepart(train_df, 'Date', drop=False)
add_datepart(test_df, 'Date', drop=False)
train_df.shape | code |
16164174/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_... | code |
16164174/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df | code |
16164174/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_cutting_df = simple_feature_cutting_df.dropna()
simple_feature_cutting_df = pd.get_dummies... | code |
16164174/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16164174/cell_8 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_cutting_df = simple_feature_cutting_d... | code |
16164174/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df
simple_feature_cutting_df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)
simple_feature_... | code |
16164174/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('../input/train.csv')
df
import matplotlib.pyplot as plt
df['Age'].hist(bins=20) | code |
128027681/cell_4 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_s... | code |
128027681/cell_1 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import m... | code |
128027681/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_s... | code |
17120125/cell_42 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import pandas as pd
import pickle
import tensorflow as tf
IMG_SIZE = 512
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
import os
train = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')
test = pd.read_csv('..... | code |
17120125/cell_21 | [
"text_plain_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import pickle
IMG_SIZE = 512
import pickle
pickle_in_train_x = open('../input/preprocessed-data-aptos2019blindnessdetection/train_x_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_train_y = open('../input/preprocessed-data-aptos2019blindnessdetec... | code |
17120125/cell_34 | [
"text_plain_output_1.png"
] | """
# https://www.youtube.com/watch?v=HxtBIwfy0kM
checkpoint_path = 'cp_model_1_aptos2019-blindness-detection.ckpt'
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weigh... | code |
17120125/cell_26 | [
"text_plain_output_1.png"
] | """
pickle_out_train_x = open('train_x_aptos2019-blindness-detection.pickle', 'wb')
pickle.dump(x, pickle_out_train_x)
pickle_out_train_x.close()
pickle_out_train_y = open('train_y_aptos2019-blindness-detection.pickle', 'wb')
pickle.dump(y, pickle_out_train_y)
pickle_out_train_y.close()
pickle_out_test_x = open('test_x... | code |
17120125/cell_11 | [
"text_plain_output_1.png"
] | import pickle
import pickle
pickle_in_train_x = open('../input/preprocessed-data-aptos2019blindnessdetection/train_x_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_train_y = open('../input/preprocessed-data-aptos2019blindnessdetection/train_y_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_test_x = open... | code |
17120125/cell_28 | [
"text_plain_output_1.png"
] | """
pickle_in_train_x = open('../input/preprocessed-data-aptos2019blindnessdetection/train_x_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_train_y = open('../input/preprocessed-data-aptos2019blindnessdetection/train_y_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_test_x = open('../input/preprocessed-d... | code |
17120125/cell_17 | [
"text_plain_output_1.png"
] | """
n = 10
cols = 5
rows = np.ceil(n/cols)
fig = plt.gcf()
fig.set_size_inches(cols * n, rows * n)
for i in range(n):
plt.subplot(rows, cols, i+1)
plt.imshow(train_x[i])
plt.title(train['diagnosis'][i], fontsize=40)
plt.axis('off')
""" | code |
17120125/cell_35 | [
"text_plain_output_1.png"
] | """
train_predicted = model_1.predict(train_x)
train_predicted = [np.argmax(i) for i in train_predicted]
from sklearn.metrics import cohen_kappa_score
cohen_kappa_score(train_predicted, train_y, weights='quadratic')
""" | code |
17120125/cell_31 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import pickle
import tensorflow as tf
IMG_SIZE = 512
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
import os
import pickle
pickle_in_train_x = open('../input/preprocessed-data-aptos2019blindnessdetection/train_x_aptos2019-bl... | code |
17120125/cell_22 | [
"image_output_1.png"
] | import cv2
import numpy as np
import pickle
IMG_SIZE = 512
import pickle
pickle_in_train_x = open('../input/preprocessed-data-aptos2019blindnessdetection/train_x_aptos2019-blindness-detection.pickle', 'rb')
pickle_in_train_y = open('../input/preprocessed-data-aptos2019blindnessdetection/train_y_aptos2019-blindness-... | code |
17120125/cell_10 | [
"text_plain_output_1.png"
] | """
n = 10
cols = 5
rows = np.ceil(n/cols)
fig = plt.gcf()
fig.set_size_inches(cols * n, rows * n)
for i in range(n):
plt.subplot(rows, cols, i+1)
plt.imshow(train_x[i])
plt.title(train['diagnosis'][i], fontsize=40)
plt.axis('off')
""" | code |
17120125/cell_37 | [
"text_plain_output_1.png"
] | """ Memory error here
# https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1./255,
rotation_range=30,
brightness_range=[0.5, 1.5],
zoom_range=[0.8, 1.2],
horizontal_flip=True,
vertical_flip... | code |
17120125/cell_5 | [
"text_html_output_1.png"
] | import tensorflow as tf
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
import os
print('Tensorflow version:', tf.__version__) | code |
1005795/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['twp'].value_counts().head(5) | code |
1005795/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'Traffic']['Sub-Category'].value_counts().head(6) | code |
1005795/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['Category'].value_counts() | code |
1005795/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
plt.title('LOWER MERION Vehicle Accidents by timzone')
sns.countplot('timezone', data=dataset[(dataset['twp'] == 'LOWER MERION') & (dataset['Sub-Category'] == ' VEHICLE ACCIDENT')]) | code |
1005795/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset.info() | code |
1005795/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[(dataset['twp'] == 'LOWER MERION') & (dataset['Category'] == 'Traffic')]['Sub-Category'].value_counts() | code |
1005795/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[(dataset['twp'] == 'LEHIGH COUNTY') & (dataset['Category'] == 'EMS')]['Sub-Category'].value_counts() | code |
1005795/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['twp'].nunique() | code |
1005795/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['title'].value_counts().head(5) | code |
1005795/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('Category', data=dataset[dataset['twp'] == 'LEHIGH COUNTY']) | code |
1005795/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
plt.title('LOWER MERION Vehicle Accidents by month')
sns.countplot('Month', data=dataset[(dataset['twp'] == 'LOWER MERION') & (dataset['Sub-Category'] == ' VEHICLE ACCIDENT')]) | code |
1005795/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'EMS']['Sub-Category'].value_counts().head(6) | code |
1005795/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['dayofweek'].value_counts() | code |
1005795/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('dayofweek', data=dataset) | code |
1005795/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['twp'].value_counts(ascending=True).head(5) | code |
1005795/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('Category', data=dataset) | code |
1005795/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
sns.countplot('timezone', data=dataset) | code |
1005795/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
plt.title('LOWER MERION incidents by Category')
sns.countplot('Category', data=dataset[dataset['twp'] == 'LOWER MERION']) | code |
1005795/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset['title'].nunique() | code |
1005795/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dataset = pd.read_csv('../input/911.csv')
plt.title('Overall Vehicle Accidents by month')
sns.countplot('Month', data=dataset[dataset['Sub-Category'] == ' VEHICLE ACCIDENT']) | code |
1005795/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset[dataset['Category'] == 'Fire']['Sub-Category'].value_counts().head(6) | code |
1005795/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/911.csv')
dataset.head(5) | code |
130000822/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import classification_report, roc_auc_score, roc_curve,confusion_matrix
from sklearn.metrics import roc_curve, auc, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from tensorflow import keras
import cv2
import matplot... | code |
130000822/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import... | code |
130000822/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.metrics import roc_curve, auc, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_... | code |
130000822/cell_7 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import... | code |
130000822/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import classification_report, roc_auc_score, roc_curve,confusion_matrix
from sklearn.metrics import roc_curve, auc, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
import cv2
import matplotlib.image as mpimg
import mat... | code |
130000822/cell_3 | [
"image_output_1.png"
] | import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.metrics import roc_curve, auc, accuracy_score, precision_sc... | code |
130000822/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import cv2
import numpy as np
import os
import tensorflow as tf
import cv2
import numpy as np
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.metrics import roc_curve, a... | code |
17145266/cell_25 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_4 | [
"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 pandas as pd
df = pd.read_json('../input/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df.head() | code |
17145266/cell_23 | [
"text_html_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_30 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_6 | [
"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 pandas as pd
df = pd.read_json('../input/Sarcasm_Headlines_Dataset_v2.json', lines=True)
df.shape
df['headline'][0] | code |
17145266/cell_29 | [
"text_plain_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_39 | [
"text_plain_output_1.png",
"image_output_1.png"
] | (df_train.shape, df_valid.shape)
path = Path('../input/')
path.ls()
src = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
bs = 48
src_lm = ItemLists(path, TextList.from_df(df_train, path='.', cols=1), TextList.from_df(df_valid, path='.', cols=1))
data_lm... | code |
17145266/cell_2 | [
"text_plain_output_1.png"
] | !pip install pretrainedmodels
!pip install fastai==1.0.52
import fastai
from fastai import *
from fastai.vision import *
from fastai.text import *
from torchvision.models import *
import pretrainedmodels
from utils import *
import sys
from fastai.callbacks.tracker import EarlyStoppingCallback
from fastai.callbacks.trac... | code |
17145266/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
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