path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
89136278/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import torch
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader('./style.jpg')
content_img = image_loader('./content.jpg')
assert style_img.size() == content_img.size(), 'we need to import style and content images of the same size'
unloader = transforms.ToPILImage()
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone()
image = image.squeeze(0)
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001)
plt.figure()
imshow(style_img, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image') | code |
89136278/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !wget -O style.jpg "https://cdn.britannica.com/89/196489-138-8770A1D5/Vincent-van-Gogh-life-work.jpg"
!wget -O content.jpg "https://www.indiewire.com/wp-content/uploads/2016/08/big-totoro-e1538413562225.jpeg" | code |
2032991/cell_9 | [
"text_plain_output_1.png"
] | import nltk
import re
null_text = X.comment_text[2]
X.shape
X.isnull().sum()
y = X[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']]
try:
X.drop(['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1, inplace=True)
except:
pass
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
X.comment_text = X.comment_text.apply(lambda x: preprocess_input(x))
X.head() | code |
2032991/cell_4 | [
"text_plain_output_1.png"
] | null_text = X.comment_text[2]
X.shape
X.isnull().sum() | code |
2032991/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
import re
null_text = X.comment_text[2]
X.shape
X.isnull().sum()
y = X[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']]
try:
X.drop(['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1, inplace=True)
except:
pass
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
X.comment_text = X.comment_text.apply(lambda x: preprocess_input(x))
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.tokenize import word_tokenize
vect = TfidfVectorizer(min_df=5, max_df=0.7, ngram_range=(1, 2), strip_accents='unicode', smooth_idf=True, sublinear_tf=True, max_features=10000)
vect = vect.fit(X['comment_text'])
X_vect = vect.transform(X['comment_text'])
X_vect.shape | code |
2032991/cell_19 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import nltk
import pandas as pd
import re
null_text = X.comment_text[2]
X.shape
X.isnull().sum()
y = X[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']]
try:
X.drop(['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1, inplace=True)
except:
pass
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
X.comment_text = X.comment_text.apply(lambda x: preprocess_input(x))
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.tokenize import word_tokenize
vect = TfidfVectorizer(min_df=5, max_df=0.7, ngram_range=(1, 2), strip_accents='unicode', smooth_idf=True, sublinear_tf=True, max_features=10000)
vect = vect.fit(X['comment_text'])
X_vect = vect.transform(X['comment_text'])
X_vect.shape
test = pd.read_csv('../input/test.csv')
test.fillna(value=null_text, inplace=True)
t_id = test['id']
test.drop(['id'], axis=1, inplace=True)
test.comment_text = test.comment_text.apply(lambda z: preprocess_input(z))
X_test = vect.transform(test['comment_text'])
X_test.shape
y.iloc[:, 1]
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
y_pred = pd.read_csv('../input/sample_submission.csv')
for c in cols:
clf = LogisticRegression(C=4, solver='sag')
clf.fit(X_vect, y[c])
y_pred[c] = clf.predict_proba(X_test)[:, 1]
pred_train = clf.predict_proba(X_vect)[:, 1]
print('log loss:', log_loss(y[c], pred_train)) | code |
2032991/cell_1 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
X = pd.read_csv('../input/train.csv')
X.head() | code |
2032991/cell_7 | [
"text_plain_output_1.png"
] | import nltk
import re
null_text = X.comment_text[2]
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
preprocess_input(null_text) | code |
2032991/cell_18 | [
"text_html_output_1.png"
] | null_text = X.comment_text[2]
X.shape
X.isnull().sum()
y = X[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']]
try:
X.drop(['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1, inplace=True)
except:
pass
y.iloc[:, 1] | code |
2032991/cell_15 | [
"text_html_output_1.png"
] | import nltk
import pandas as pd
import re
null_text = X.comment_text[2]
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
test = pd.read_csv('../input/test.csv')
test.fillna(value=null_text, inplace=True)
t_id = test['id']
test.drop(['id'], axis=1, inplace=True)
test.comment_text = test.comment_text.apply(lambda z: preprocess_input(z))
len(test) | code |
2032991/cell_3 | [
"text_plain_output_1.png"
] | null_text = X.comment_text[2]
X.shape | code |
2032991/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
import pandas as pd
import re
null_text = X.comment_text[2]
X.shape
X.isnull().sum()
y = X[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']]
try:
X.drop(['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'], axis=1, inplace=True)
except:
pass
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
X.comment_text = X.comment_text.apply(lambda x: preprocess_input(x))
from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.tokenize import word_tokenize
vect = TfidfVectorizer(min_df=5, max_df=0.7, ngram_range=(1, 2), strip_accents='unicode', smooth_idf=True, sublinear_tf=True, max_features=10000)
vect = vect.fit(X['comment_text'])
X_vect = vect.transform(X['comment_text'])
test = pd.read_csv('../input/test.csv')
test.fillna(value=null_text, inplace=True)
t_id = test['id']
test.drop(['id'], axis=1, inplace=True)
test.comment_text = test.comment_text.apply(lambda z: preprocess_input(z))
X_test = vect.transform(test['comment_text'])
X_test.shape | code |
2032991/cell_14 | [
"text_plain_output_1.png"
] | import nltk
import pandas as pd
import re
null_text = X.comment_text[2]
import re
import nltk
stop_words = set(nltk.corpus.stopwords.words('english'))
def preprocess_input(t):
t = t.strip()
z = re.findall('[A-Za-z]+', t)
z = [a for a in z if len(a) > 3]
wnlemma = nltk.stem.WordNetLemmatizer()
z = [wnlemma.lemmatize(a) for a in z]
z = [a for a in z if not a in stop_words]
t = ' '.join(z)
return t
test = pd.read_csv('../input/test.csv')
test.fillna(value=null_text, inplace=True)
t_id = test['id']
test.drop(['id'], axis=1, inplace=True)
test.comment_text = test.comment_text.apply(lambda z: preprocess_input(z))
test.head() | code |
2032991/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
null_text = X.comment_text[2]
test = pd.read_csv('../input/test.csv')
test.fillna(value=null_text, inplace=True)
test.head() | code |
88102456/cell_21 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
dfErrorsOrg = df_new[df_new['errorbalanceOrg'] != 0]
dfErrorsOrg[['errorbalanceOrg', 'isFraud']].groupby('isFraud').count()
dfErrorsOrg = df_new[df_new['errorbalanceOrg'] == 0]
dfErrorsOrg[['errorbalanceOrg', 'isFraud']].groupby('isFraud').count() | code |
88102456/cell_9 | [
"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('/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
frauds = df[df['isFraud'] == 1]
frauds['type'].unique() | code |
88102456/cell_25 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
df_new['hour_of_day'] = df_new['step'] % 24 | code |
88102456/cell_20 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
dfErrorsOrg = df_new[df_new['errorbalanceOrg'] != 0]
dfErrorsOrg[['errorbalanceOrg', 'isFraud']].groupby('isFraud').count() | code |
88102456/cell_2 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns | code |
88102456/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest | code |
88102456/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 |
88102456/cell_7 | [
"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('/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
first17 = df[df['step'] < 17 * 24]
fraudcount = first17[first17['isFraud'] == 1].count()
totalcount = first17.count()
fraudcount / totalcount | code |
88102456/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style='darkgrid')
print(df.type.value_counts())
f, ax = plt.subplots(1, 1, figsize=(8, 8))
df.type.value_counts().plot(kind='bar', title='Transaction type', ax=ax, figsize=(8, 8))
plt.ticklabel_format(style='plain', axis='y')
for p in ax.patches:
ax.annotate(str(format(int(p.get_height()), ',d')), (p.get_x(), p.get_height()))
plt.show() | code |
88102456/cell_24 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
dfErrorsDest = df_new[df_new['errorbalanceDest'] != 0]
dfErrorsDest[['errorbalanceDest', 'isFraud']].groupby('isFraud').count()
dfErrorsDest = df_new[df_new['errorbalanceDest'] == 0]
dfErrorsDest[['errorbalanceDest', 'isFraud']].groupby('isFraud').count() | code |
88102456/cell_14 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
incorrectlyFlaggedFraud = df[(df['isFlaggedFraud'] == 1) & (df['isFraud'] == 0)]
incorrectlyFlaggedFraud['type'].unique() | code |
88102456/cell_22 | [
"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('/kaggle/input/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
dfErrorsDest = df_new[df_new['errorbalanceDest'] != 0]
dfErrorsDest[['errorbalanceDest', 'isFraud']].groupby('isFraud').count() | code |
88102456/cell_10 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
frauds = df[df['isFraud'] == 1]
frauds['type'].unique()
frauds['day'] = round(frauds['step'] / 24)
frauds_by_day = frauds[['step', 'day']].groupby('day').count()
frauds_by_day = frauds_by_day.rename(columns={'step': 'count'})
frauds_by_day.plot(kind='bar', figsize=(10, 5), title='Number of frauds per day') | code |
88102456/cell_27 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
df_new = df.loc[(df.type == 'TRANSFER') | (df.type == 'CASH_OUT')]
df_new['errorbalanceOrg'] = df_new.newbalanceOrig + df_new.amount - df_new.oldbalanceOrg
df_new['errorbalanceDest'] = df_new.oldbalanceDest + df_new.amount - df_new.newbalanceDest
df_new['isFraud'].groupby('hour_of_day').count() | code |
88102456/cell_12 | [
"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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount
correctlyFlaggedFraud = df[(df['isFlaggedFraud'] == 1) & (df['isFraud'] == 1)]
correctlyFlaggedFraud['type'].unique() | code |
88102456/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/paysim1/PS_20174392719_1491204439457_log.csv')
df.columns
fraudcount = df[df['isFraud'] == 1].count()
totalcount = df.count()
fraudcount / totalcount | code |
32068294/cell_21 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.drop('Formatted Date', axis=1, inplace=True)
df.head(2) | code |
32068294/cell_13 | [
"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
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
df.head(5) | code |
32068294/cell_4 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df.describe() | code |
32068294/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.drop('Formatted Date', axis=1, inplace=True)
df.iloc[:, 9].value_counts()
df.iloc[:, 9].nunique() | code |
32068294/cell_20 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.head(2) | code |
32068294/cell_26 | [
"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
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.drop('Formatted Date', axis=1, inplace=True)
df.iloc[:, 9].value_counts()
df.iloc[:, 9].nunique()
df.isnull().sum(axis=0) | code |
32068294/cell_2 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.head(4) | code |
32068294/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 |
32068294/cell_7 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0) | code |
32068294/cell_8 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_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
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts() | code |
32068294/cell_15 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.head(3) | code |
32068294/cell_3 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0) | code |
32068294/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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.drop('Formatted Date', axis=1, inplace=True)
df.iloc[:, 9].value_counts() | code |
32068294/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)
import pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts() | code |
32068294/cell_27 | [
"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
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
summary_dict = {'Partly Cloudy': 0, 'Mostly Cloudy': 1, 'Overcast': 2, 'Clear': 3, 'Foggy': 4, 'Breezy': 5, 'Windy': 6, 'Dry': 7, 'Rain': 8, 'Humid': 9}
df.shape[0]
df = df.replace({'Summary': summary_dict})
cols_to_transform = ['Precip Type']
df_with_dummies = pd.get_dummies(df, columns=cols_to_transform, drop_first=True)
df = df_with_dummies
df.drop('Dummy', axis=1, inplace=True)
df.drop('Formatted Date', axis=1, inplace=True)
df.iloc[:, 9].value_counts()
df.iloc[:, 9].nunique()
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0) | code |
32068294/cell_12 | [
"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 pandas as pd
import numpy as np
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum(axis=0)
df = df.dropna(how='any', axis=0)
df.isnull().sum(axis=0)
df.iloc[:, 1].value_counts()
df.iloc[:, 1].value_counts()
df.shape[0] | code |
32065291/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import matplotlib.pyplot as plt
plt.style.use('dark_background')
import warnings
warnings.filterwarnings('ignore')
import seaborn
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
test_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
train_data.rename(columns={'Province_State': 'Province'}, inplace=True)
test_data.rename(columns={'Province_State': 'Province'}, inplace=True)
india = train_data.loc[train_data['Country'] == 'India']
plt.xticks(rotation=90)
confirmed = train_data.groupby('Date').sum()['ConfirmedCases'].reset_index()
deaths = train_data.groupby('Date').sum()['Fatalities'].reset_index()
plt.xticks(rotation=90)
train_data['Date'] = pd.to_datetime(train_data['Date'])
test_data['Date'] = pd.to_datetime(test_data['Date'])
train_data['Province'] = train_data.apply(lambda row: str(row['Country']) if pd.isnull(row['Province']) else row['Province'], axis=1)
test_data['Province'] = test_data.apply(lambda row: str(row['Country']) if pd.isnull(row['Province']) else row['Province'], axis=1)
train_data.drop(['Id'], axis=1, inplace=True)
train_data.head() | code |
32065291/cell_4 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
print(train_data.shape)
print(test_data.shape) | code |
32065291/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import matplotlib.pyplot as plt
plt.style.use('dark_background')
import warnings
warnings.filterwarnings('ignore')
import seaborn
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
test_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
train_data.rename(columns={'Province_State': 'Province'}, inplace=True)
test_data.rename(columns={'Province_State': 'Province'}, inplace=True)
india = train_data.loc[train_data['Country'] == 'India']
plt.figure(figsize=(20, 10))
plt.bar(india.Date, india.ConfirmedCases)
plt.bar(india.Date, india.Fatalities)
plt.title('INDIA Circumstances')
plt.xticks(rotation=90)
plt.show() | code |
32065291/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 |
32065291/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import warnings
import matplotlib.pyplot as plt
plt.style.use('dark_background')
import warnings
warnings.filterwarnings('ignore')
import seaborn
train_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test_data = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
test_data.rename(columns={'Country_Region': 'Country'}, inplace=True)
train_data.rename(columns={'Province_State': 'Province'}, inplace=True)
test_data.rename(columns={'Province_State': 'Province'}, inplace=True)
india = train_data.loc[train_data['Country'] == 'India']
plt.xticks(rotation=90)
confirmed = train_data.groupby('Date').sum()['ConfirmedCases'].reset_index()
deaths = train_data.groupby('Date').sum()['Fatalities'].reset_index()
plt.figure(figsize=(22, 9))
plt.bar(confirmed['Date'], confirmed['ConfirmedCases'])
plt.title('World Circumstances')
plt.bar(deaths['Date'], deaths['Fatalities'])
plt.xticks(rotation=90)
plt.show() | code |
18114582/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
work_data = pd.concat([data['cnt'], data['workingday']], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x="workingday", y="cnt", data=work_data)
fig.axis(ymin=0, ymax=1000);
# hypothesis was valid
# the overall mean for accidents on a workingday is higher than
week_data = pd.concat([data['cnt'], data['weekday']], axis=1)
fig = sns.boxplot(x='weekday', y='cnt', data=week_data)
fig.axis(ymin=0, ymax=1000) | code |
18114582/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
sns.distplot(data['cnt']) | code |
18114582/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0 | code |
18114582/cell_7 | [
"image_output_1.png"
] | import pandas as pd
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
data.describe() | code |
18114582/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
work_data = pd.concat([data['cnt'], data['workingday']], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x="workingday", y="cnt", data=work_data)
fig.axis(ymin=0, ymax=1000);
# hypothesis was valid
# the overall mean for accidents on a workingday is higher than
week_data = pd.concat([data['cnt'], data['weekday']], axis=1)
fig = sns.boxplot(x="weekday", y="cnt", data=week_data)
fig.axis(ymin=0, ymax=1000);
# hypothesis was valid
# the overall mean for accidents on a workingday is higher than on non working days
sns.catplot(x='weathersit', y='cnt', hue='yr', data=data, height=6, kind='bar', palette='muted') | code |
18114582/cell_3 | [
"image_output_1.png"
] | import pandas as pd
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
data.head(5) | code |
18114582/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
print('Skewness: %f' % data['cnt'].skew())
print('Kurtosis: %f' % data['cnt'].kurt()) | code |
18114582/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
sum(data.duplicated(subset='index')) == 0
work_data = pd.concat([data['cnt'], data['workingday']], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x='workingday', y='cnt', data=work_data)
fig.axis(ymin=0, ymax=1000) | code |
18114582/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.DataFrame(pd.read_csv('../input/motorbike_ambulance_calls.csv'))
data.info() | code |
88095865/cell_4 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('taxi').getOrCreate() | code |
88095865/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import DecisionTreeRegressor, GBTRegressor
si = StringIndexer(inputCol='store_and_fwd_flag', outputCol='store_and_fwd_flag_si', handleInvalid='skip')
va = VectorAssembler(inputCols=['VendorID', 'day', 'hour', 'passenger_count', 'trip_distance', 'RateCodeID', 'store_and_fwd_flag_si', 'PULocationID', 'DOLocationID', 'payment_type'], outputCol='features')
gbt = GBTRegressor(featuresCol='features', labelCol='total_amount', maxDepth=5, maxIter=5, seed=42)
pipeline = Pipeline(stages=[si, va, gbt])
model = pipeline.fit(train)
predictions = model.transform(test)
predictions.select('prediction', 'total_amount', 'features').show(5) | code |
88095865/cell_26 | [
"text_plain_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import DecisionTreeRegressor, GBTRegressor
si = StringIndexer(inputCol='store_and_fwd_flag', outputCol='store_and_fwd_flag_si', handleInvalid='skip')
va = VectorAssembler(inputCols=['VendorID', 'day', 'hour', 'passenger_count', 'trip_distance', 'RateCodeID', 'store_and_fwd_flag_si', 'PULocationID', 'DOLocationID', 'payment_type'], outputCol='features')
gbt = GBTRegressor(featuresCol='features', labelCol='total_amount', maxDepth=5, maxIter=5, seed=42)
pipeline = Pipeline(stages=[si, va, gbt])
model = pipeline.fit(train) | code |
88095865/cell_2 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | #Installing pyspark
!pip install pyspark | code |
88095865/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.types import StringType, IntegerType, StructType, StructField, TimestampType, DoubleType
spark = SparkSession.builder.appName('taxi').getOrCreate()
schema = StructType([StructField('VendorID', IntegerType(), True), StructField('tpep_pickup_datetime', TimestampType(), True), StructField('tpep_dropoff_datetime', TimestampType(), True), StructField('passenger_count', IntegerType(), True), StructField('trip_distance', DoubleType(), True), StructField('RatecodeID', IntegerType(), True), StructField('store_and_fwd_flag', StringType(), True), StructField('PULocationID', IntegerType(), True), StructField('DOLocationID', IntegerType(), True), StructField('payment_type', IntegerType(), True), StructField('fare_amount', DoubleType(), True), StructField('extra', DoubleType(), True), StructField('mta_tax', DoubleType(), True), StructField('tip_amount', DoubleType(), True), StructField('tolls_amount', IntegerType(), True), StructField('improvement_surcharge', DoubleType(), True), StructField('total_amount', DoubleType(), True), StructField('congestion_surcharge', DoubleType(), True)])
df = spark.read.csv('/kaggle/input/newyork-yellow-taxi-trip-data-2020-2019/*2020*', schema=schema, header=True)
len(df.columns)
df.show(5) | code |
88095865/cell_19 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.types import StringType, IntegerType, StructType, StructField, TimestampType, DoubleType
import pyspark.sql.functions as F
spark = SparkSession.builder.appName('taxi').getOrCreate()
schema = StructType([StructField('VendorID', IntegerType(), True), StructField('tpep_pickup_datetime', TimestampType(), True), StructField('tpep_dropoff_datetime', TimestampType(), True), StructField('passenger_count', IntegerType(), True), StructField('trip_distance', DoubleType(), True), StructField('RatecodeID', IntegerType(), True), StructField('store_and_fwd_flag', StringType(), True), StructField('PULocationID', IntegerType(), True), StructField('DOLocationID', IntegerType(), True), StructField('payment_type', IntegerType(), True), StructField('fare_amount', DoubleType(), True), StructField('extra', DoubleType(), True), StructField('mta_tax', DoubleType(), True), StructField('tip_amount', DoubleType(), True), StructField('tolls_amount', IntegerType(), True), StructField('improvement_surcharge', DoubleType(), True), StructField('total_amount', DoubleType(), True), StructField('congestion_surcharge', DoubleType(), True)])
df = spark.read.csv('/kaggle/input/newyork-yellow-taxi-trip-data-2020-2019/*2020*', schema=schema, header=True)
len(df.columns)
df = df.sample(fraction=0.1, seed=42)
df = df.withColumn('day', F.dayofmonth(df.tpep_pickup_datetime))
df = df.withColumn('hour', F.hour(df.tpep_pickup_datetime))
df = df.select('VendorID', 'day', 'hour', 'passenger_count', 'trip_distance', 'RateCodeID', 'store_and_fwd_flag', 'PULocationID', 'DOLocationID', 'payment_type', 'total_amount')
df.printSchema() | code |
88095865/cell_31 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from pyspark.ml import Pipeline
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import DecisionTreeRegressor, GBTRegressor
si = StringIndexer(inputCol='store_and_fwd_flag', outputCol='store_and_fwd_flag_si', handleInvalid='skip')
va = VectorAssembler(inputCols=['VendorID', 'day', 'hour', 'passenger_count', 'trip_distance', 'RateCodeID', 'store_and_fwd_flag_si', 'PULocationID', 'DOLocationID', 'payment_type'], outputCol='features')
gbt = GBTRegressor(featuresCol='features', labelCol='total_amount', maxDepth=5, maxIter=5, seed=42)
pipeline = Pipeline(stages=[si, va, gbt])
model = pipeline.fit(train)
predictions = model.transform(test)
evaluator = RegressionEvaluator(labelCol='total_amount', predictionCol='prediction', metricName='rmse')
rmse = evaluator.evaluate(predictions)
print('Root Mean Squared Error (RMSE) on test data = %g' % rmse) | code |
88095865/cell_10 | [
"text_plain_output_1.png"
] | from pyspark.sql import SparkSession
from pyspark.sql.types import StringType, IntegerType, StructType, StructField, TimestampType, DoubleType
spark = SparkSession.builder.appName('taxi').getOrCreate()
schema = StructType([StructField('VendorID', IntegerType(), True), StructField('tpep_pickup_datetime', TimestampType(), True), StructField('tpep_dropoff_datetime', TimestampType(), True), StructField('passenger_count', IntegerType(), True), StructField('trip_distance', DoubleType(), True), StructField('RatecodeID', IntegerType(), True), StructField('store_and_fwd_flag', StringType(), True), StructField('PULocationID', IntegerType(), True), StructField('DOLocationID', IntegerType(), True), StructField('payment_type', IntegerType(), True), StructField('fare_amount', DoubleType(), True), StructField('extra', DoubleType(), True), StructField('mta_tax', DoubleType(), True), StructField('tip_amount', DoubleType(), True), StructField('tolls_amount', IntegerType(), True), StructField('improvement_surcharge', DoubleType(), True), StructField('total_amount', DoubleType(), True), StructField('congestion_surcharge', DoubleType(), True)])
df = spark.read.csv('/kaggle/input/newyork-yellow-taxi-trip-data-2020-2019/*2020*', schema=schema, header=True)
len(df.columns) | code |
105199619/cell_6 | [
"text_plain_output_1.png"
] | !pip uninstall -q -y transformers | code |
89124318/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
89124318/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor1 = LinearRegression()
regressor1.fit(X_train1, y_train1)
y_pred1 = regressor1.predict(X_test1)
mean_squared_error(y_test1, y_pred1) | code |
89124318/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show() | code |
89124318/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor1 = LinearRegression()
regressor1.fit(X_train1, y_train1)
y_pred1 = regressor1.predict(X_test1)
print(mean_squared_error(y_test1, y_pred1), explained_variance_score(y_test1, y_pred1), max_error(y_test1, y_pred1), mean_absolute_error(y_test1, y_pred1), mean_squared_error(y_test1, y_pred1), mean_squared_log_error(y_test1, y_pred1), median_absolute_error(y_test1, y_pred1), r2_score(y_test1, y_pred1), mean_poisson_deviance(y_test1, y_pred1), mean_gamma_deviance(y_test1, y_pred1), mean_tweedie_deviance(y_test1, y_pred1, power=1)) | code |
89124318/cell_29 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor1 = LinearRegression()
regressor1.fit(X_train1, y_train1) | code |
89124318/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1)
plt.scatter(X_test1, y_test1, color='red')
plt.plot(X_test1, lin_reg_2.predict(poly_reg.fit_transform(X_test1)), color='blue')
plt.title('Truth or Bluff(polynomial Regression)')
plt.xlabel('Position_level')
plt.ylabel('Salary')
plt.show() | code |
89124318/cell_26 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
mean_squared_error(y_test, y_pred) | code |
89124318/cell_41 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1)
valtopredictn2 = y_test1.reshape(-1, 1)
y_pred3 = lin_reg_2.predict(poly_reg.fit_transform(valtopredictn2))
mean_squared_error(y_test1, y_pred3) | code |
89124318/cell_19 | [
"text_plain_output_1.png"
] | print('Size of Xtrain', X_train.shape)
print('Length of ytrain', len(y_train))
print('Size of Xtest', X_test.shape)
print('Length of ytest', len(y_test)) | code |
89124318/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
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 |
89124318/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/linear-regression-dataset/Linear Regression - Sheet1.csv')
df.head() | code |
89124318/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVR
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1)
valtopredictn2 = y_test1.reshape(-1, 1)
y_pred3 = lin_reg_2.predict(poly_reg.fit_transform(valtopredictn2))
from sklearn.svm import SVR
regressor2 = SVR(kernel='rbf')
regressor2.fit(X_train1, y_train1)
y_pred2 = regressor2.predict(valtopredictn2)
mean_squared_error(y_test1, y_pred2) | code |
89124318/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/linear-regression-dataset/Linear Regression - Sheet1.csv')
df.info() | code |
89124318/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1)
plt.scatter(X_train1, y_train1, color='red')
plt.plot(X_train1, lin_reg_2.predict(poly_reg.fit_transform(X_train1)), color='blue')
plt.title('Truth or Bluff(polynomial Regression)')
plt.xlabel('Position_level')
plt.ylabel('Salary')
plt.show() | code |
89124318/cell_43 | [
"image_output_1.png"
] | from sklearn.svm import SVR
from sklearn.svm import SVR
regressor2 = SVR(kernel='rbf')
regressor2.fit(X_train1, y_train1) | code |
89124318/cell_46 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.svm import SVR
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1)
valtopredictn2 = y_test1.reshape(-1, 1)
y_pred3 = lin_reg_2.predict(poly_reg.fit_transform(valtopredictn2))
from sklearn.svm import SVR
regressor2 = SVR(kernel='rbf')
regressor2.fit(X_train1, y_train1)
y_pred2 = regressor2.predict(valtopredictn2)
plt.scatter(X_train1, y_train1, color='red')
plt.plot(X_train1, regressor2.predict(X_train1), color='blue')
plt.title('Truth or Bluff(polynomial Regression)')
plt.xlabel('Position_level')
plt.ylabel('Salary')
plt.show() | code |
89124318/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
plt.scatter(X_test, y_test, color='red')
plt.plot(X_test, regressor.predict(X_test), color='blue')
plt.title('Salary vs Experience (Test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show() | code |
89124318/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/linear-regression-dataset/Linear Regression - Sheet1.csv')
standard_scaler = StandardScaler()
standardized_data = standard_scaler.fit_transform(df)
pd.DataFrame(standardized_data, columns=df.columns) | code |
89124318/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=2)
X_poly = poly_reg.fit_transform(X_train1)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y_train1) | code |
89124318/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train1, y_train1) | code |
16148029/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
plt.figure()
plt.imshow(test_images[0].reshape(28, 28))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show() | code |
16148029/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.figure()
plt.imshow(train_images.iloc[0].as_matrix().reshape(28, 28))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
print(class_names[0]) | code |
16148029/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape | code |
16148029/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
predictions = model.predict(test_images)
np.argmax(predictions[0])
plt.colorbar()
plt.xticks([])
plt.yticks([])
x = np.argmax(predictions[999])
plt.figure()
plt.imshow(test_images[999].reshape(28, 28))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
print(class_names[x]) | code |
16148029/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
print(test_labels[0])
print(class_names[0]) | code |
16148029/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape | code |
16148029/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
predictions = model.predict(test_images)
np.argmax(predictions[0]) | code |
16148029/cell_1 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
print(tf.__version__) | code |
16148029/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
model.fit(train_images, train_labels, epochs=10) | code |
16148029/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.head() | code |
16148029/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('The test accuracy is: {} and test loss is: {}'.format(test_acc, test_loss)) | code |
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