path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
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.To... | 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 nlt... | 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'],... | 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', 'i... | 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(... | 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 ... | 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', 'insul... | 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 ... | 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'... | 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'... | 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'... | 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'... | 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[... | 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:
a... | 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'... | 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['... | 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'... | 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[... | 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'... | 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['is... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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[:... | 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... | 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... | 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... | 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="w... | 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="w... | 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='w... | 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 = VectorAsse... | 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 = VectorAsse... | 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', TimestampTyp... | 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('... | 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_... | 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', TimestampTyp... | 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... | 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_varianc... | 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()... | 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()... | 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
r... | 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()... | 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
r... | 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), c... | 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)
... | 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.fi... | 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'... | 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'... | 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 tensorf... | 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'... | 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 tensorf... | 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 kera... | 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 kera... | code |
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