path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72101516/cell_3 | [
"image_output_1.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
72101516/cell_24 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot, plot
import pandas as pd
import plotly.graph_objs as go
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-o... | code |
72101516/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import nltk
import os
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
fr... | code |
72101516/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olymp... | code |
2021927/cell_13 | [
"text_plain_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['Compensat... | code |
2021927/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['CompensationAmount'].str.replace('[^\\w\\s]', '')
df['Compens... | code |
2021927/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df.head() | code |
2021927/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from statsmodels.graphics.gofplots import ProbPlot | code |
2021927/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['CompensationAmount'].str.replace('[^\\w\\s]', '')
df['Compens... | code |
2021927/cell_8 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['Compensat... | code |
2021927/cell_16 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['Compensat... | code |
2021927/cell_3 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df[['CompensationAmount', 'Age']].info() | code |
2021927/cell_14 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['Compensat... | code |
2021927/cell_12 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/multipleChoiceResponses.csv', encoding='ISO-8859-1')
df = df[['CompensationAmount', 'Age']]
df['CompensationAmount'] = df['Compensat... | code |
129018141/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/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 |
129018141/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/cell_15 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_... | code |
129018141/cell_16 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_... | code |
129018141/cell_3 | [
"text_plain_output_1.png"
] | import pandas
import pandas
pandas.__version__ | code |
129018141/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
129018141/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)
train = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
test = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
submission = pd.read_csv('/kaggle/input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12... | code |
73078708/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import chi2
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes
... | code |
73078708/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import matplotlib.... | code |
73078708/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
df.info() | code |
73078708/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import pandas as p... | code |
73078708/cell_30 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import pandas as p... | code |
73078708/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes
df['intent_id'] = df['intent'].factorize()[0]
intent_id_df = df[['intent', 'inte... | code |
73078708/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import pandas as p... | code |
73078708/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
for i in df['intent'].value_counts().index:
print(i)
print(df[df['intent'] == i]['clean_text']) | code |
73078708/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # ploting library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes
df['intent_id'] = df['intent'].factorize()[0]
intent_id_df = df[['i... | code |
73078708/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
for i, row in df.iterrows():
print(row['text'], ' -> ', row['intent']) | code |
73078708/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes
df['intent_id'] = df['intent'].factorize()[0]
intent_id_df = df[['intent', 'intent_id']].drop_duplicates()
intent_to_id = dict(intent_id_df.v... | code |
73078708/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
df[df['intent'] == 'warm weather'] | code |
73078708/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes | code |
73078708/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
df.head() | code |
73078708/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
classes = df.intent.unique()
classes
len(classes) | code |
73078708/cell_24 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import pandas as p... | code |
73078708/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('../input/machathon-20-final-round/train_ara.csv')
df | code |
73078708/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
for i, row in df.iterrows():
print(row['text'], ' -> ', row['clean_text'], ' -> ', row['intent']) | code |
73078708/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import pandas as p... | code |
73078708/cell_12 | [
"text_plain_output_1.png"
] | from nltk.stem.isri import ISRIStemmer
import re
import string
def remove_punc(s):
punctuations = '`÷×؛ʿˇ<>(‚)*&^%][،/:ღ"┈؟.,\'{}~¦+ ، 》《|﴾»«﴿!”…“–❒ـ۞✦✩☜ ̷ ﮼☻\U000fe334❥*،“¸.•°``°•.`•.¸.•♫♡—' + string.punctuation
punctuations = ''.join(set(punctuations) - {'ـ'})
for c in punctuations:
s = s.repla... | code |
73078708/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/machathon-20-final-round/train_ara.csv')
df['intent'].value_counts() | code |
33110896/cell_13 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_9 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_n... | code |
33110896/cell_11 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/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 |
33110896/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/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_n... | code |
33110896/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = df.Dates.dt.year
df['Month_n... | code |
33110896/cell_15 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_16 | [
"text_plain_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_3 | [
"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/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
df.head() | code |
33110896/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_10 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_12 | [
"text_html_output_1.png"
] | from datetime import date
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ebola-outbreak-20142016-complete-dataset/ebola_2014_2016_clean.csv')
import datetime as dt
from datetime import date
df['Dates'] = pd.to_datetime(df['Date'])
df['Year'] = d... | code |
33110896/cell_5 | [
"text_plain_output_1.png"
] | from datetime import date
d0 = date(2014, 8, 29)
d1 = date(2016, 3, 23)
delta = d1 - d0
print(delta) | code |
89138170/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 seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts()
... | code |
89138170/cell_13 | [
"text_plain_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('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.describe() | code |
89138170/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)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts()
df.quality.value_count... | code |
89138170/cell_29 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89138170/cell_11 | [
"text_plain_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('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.info() | code |
89138170/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts() | code |
89138170/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 |
89138170/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)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.head(7) | code |
89138170/cell_18 | [
"text_plain_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('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique() | code |
89138170/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)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique() | code |
89138170/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89138170/cell_14 | [
"text_html_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('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique() | code |
89138170/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)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape | code |
89138170/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)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum() | code |
50211041/cell_13 | [
"image_output_1.png"
] | import lightgbm
import lightgbm
lgbreg = lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, learning_rate=0.1, n_estimators=100)
lgbreg.fit(X_train, Y_train)
print('train score', lgbreg.score(X_train, Y_train))
print('test score', lgbreg.score(X_test, Y_test)) | code |
50211041/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df.info() | code |
50211041/cell_11 | [
"image_output_1.png"
] | import catboost
import catboost
cboost = catboost.CatBoostRegressor(loss_function='RMSE', verbose=False)
cboost.fit(X_train, Y_train)
print('train score', cboost.score(X_train, Y_train))
print('test score', cboost.score(X_test, Y_test)) | code |
50211041/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 |
50211041/cell_7 | [
"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 seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50211041/cell_8 | [
"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 seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
50211041/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
Begging = RandomForestRegressor(max_depth=30, n_estimators=300)
Begging.fit(X_train, Y_train)
print('train score', Begging.score(X_train, Y_train))
print('test score... | code |
50211041/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import StackingRegressor
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
import warnings
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR
from sklearn.ensemble import RandomForestRegressor
... | code |
50211041/cell_10 | [
"text_plain_output_1.png"
] | from sklearn import ensemble
import sklearn
sklearn_boost = ensemble.GradientBoostingRegressor(loss='ls', learning_rate=0.1, n_estimators=100)
sklearn_boost.fit(X_train, Y_train)
print('train score', sklearn_boost.score(X_train, Y_train))
print('test score', sklearn_boost.score(X_test, Y_test)) | code |
50211041/cell_12 | [
"image_output_1.png"
] | import xgboost
import xgboost
xgBoost = xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, booster='gbtree')
xgBoost.fit(X_train, Y_train)
print('train score', xgBoost.score(X_train, Y_train))
print('test score', xgBoost.score(X_test, Y_test)) | code |
50211041/cell_5 | [
"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 seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
def NanColums(df):
percent_nan = 100 * df.isnull().sum() / len(df)
percent_nan = percent_nan[percent... | code |
122255715/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.... | code |
122255715/cell_9 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.... | code |
122255715/cell_4 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.index.name = 'Date'
df.index = pd... | code |
122255715/cell_6 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.... | code |
122255715/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.... | code |
122255715/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 |
122255715/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.index.name = 'Date'
df.index = pd... | code |
122255715/cell_5 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/temp2021/Temp2021.csv', skiprows=[i for i in range(1, 98)], parse_dates=['Var1'], index_col=['Var1'])
df.index.name = 'Date'
df.index = pd... | code |
90119657/cell_4 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
sub1 = pd.read_csv('../input/ncaam-2022/stage2_seeds_sample_submission.csv')
sub1.sort_values(by=['ID'], inplace=True)
sub2 = pd.read_csv('../input/mens-march-mania-2022/MDataFiles_Stage2/MSampleSubmissionStage2... | code |
90119657/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 |
1007485/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[(data['director_name'].isin(D_Name))]
New_D.pivot_table(index=['di... | code |
1007485/cell_4 | [
"image_output_1.png"
] | data.shape
for i in data.columns:
print(i, end='; ') | code |
1007485/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[data['director_name'].isin(D_Name)]
New_D.pivot_table(index=['dire... | code |
1007485/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data.shape
Color_Count = data.color.value_counts()
idx = range(2)
labels = ['Color', 'Black & White']
plt.xticks(idx, labels)
Director = data.director_name.value_counts()
D_Name = Director.head(n=10).index
New_D = data[(data['director_name'].isin(D_Name))]
New_D.p... | code |
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