path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
106198134/cell_17 | [
"text_html_output_1.png"
] | clean_names(dailyActivity) | code |
106198134/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | library(tidyverse)
library(readr)
library(here)
library(skimr)
library(dplyr)
library(janitor) | code |
50242358/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of... | code |
50242358/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_2 | [
"text_plain_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50242358/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of... | code |
50242358/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug... | code |
50242358/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
50242358/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 50)
import os
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
TRAIN_PATH = '/kaggle/input/hr-analytics-job-change-of-data-scientists/aug_train.csv'
TEST_PATH = '/kaggle/input/hr-analytics-job-... | code |
2029228/cell_4 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_dat... | code |
2029228/cell_6 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_dat... | code |
2029228/cell_8 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_dat... | code |
2029228/cell_3 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_dat... | code |
2029228/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
def draw_plot(data, start_range, end_range, total_dat... | code |
129037105/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().... | code |
129037105/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique() | code |
129037105/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().... | code |
129037105/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.describe() | code |
129037105/cell_55 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seab... | code |
129037105/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().... | code |
129037105/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop... | code |
129037105/cell_52 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seab... | code |
129037105/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 |
129037105/cell_45 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop... | code |
129037105/cell_49 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # it's is core library for numeric and scientific computing
import numpy as np # linear algebra
import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seab... | code |
129037105/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.info() | code |
129037105/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().... | code |
129037105/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape | code |
129037105/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
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/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.... | code |
129037105/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=True)
df.nunique()
df.isnull().... | code |
129037105/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size | code |
129037105/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.head() | code |
129037105/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # it's is core library for data manipulation and data analysis
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/credit-card-buyers/train data credit card.csv')
df.size
df.shape
df.drop('ID', axis=1, inplace=... | code |
33107227/cell_21 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vo... | code |
33107227/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_29 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_W... | code |
33107227/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 |
33107227/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
confusion_matrix(y_valid, y_pred) | code |
33107227/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train) | code |
33107227/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
vodafone_subset_6.head(10) | code |
33107227/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_valid)
accuracy_score(y_pred, y_valid) | code |
33107227/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np # lin... | code |
33107227/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
33107227/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
vodafone_subset_6 = pd.read_csv('../input/vodafone6nm/vodafone-subset-6.csv')
df = vodafone_subset_6[['target', 'ROUM', 'phone_value', 'DATA_VOLUME_WEEKDAYS', 'DATA_VOLUME_WEEKENDS', 'device_brand', 'software_os_vendor', 'software_os_name', 'softw... | code |
128015258/cell_9 | [
"text_plain_output_1.png"
] | prompt2 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', ... | code |
128015258/cell_2 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, OPTForCausalLM
from transformers import AutoTokenizer, OPTForCausalLM
model = OPTForCausalLM.from_pretrained('facebook/opt-350m')
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
prompt = 'Hey, are you consciours? Can you talk to me?' | code |
128015258/cell_11 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | prompt2 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nNow complete the following:\nTable Schema: 'title', 'Birth Name', 'Born', ... | code |
128015258/cell_1 | [
"text_plain_output_1.png"
] | pip install transformers | code |
128015258/cell_7 | [
"text_plain_output_1.png"
] | prompt1 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nExamples:\n\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Oc... | code |
128015258/cell_10 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | prompt1 = "Task Description:\nIn this task, your goal is to convert a given sentence grounded in the input table schema into a question whose answer can be the given sentence. You should use the table schema provided to generate the question.\n\nExamples:\n\nTable Schema: 'title', 'Birth Name', 'Born', 'Residence', 'Oc... | code |
128015258/cell_5 | [
"text_plain_output_1.png"
] | from transformers import AutoTokenizer, OPTForCausalLM
from transformers import AutoTokenizer, OPTForCausalLM
model = OPTForCausalLM.from_pretrained('facebook/opt-350m')
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
prompt = 'Hey, are you consciours? Can you talk to me?'
generate(prompt) | code |
74058313/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleanse... | code |
74058313/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleanse... | code |
74058313/cell_11 | [
"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 seaborn as sns
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings =... | code |
74058313/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import matplotlib.pyplot as plt
import seaborn as sns
from pylab import *
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74058313/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id', 'host_is_superhost', 'host_identity_verified', 'neighbourhood_cleanse... | code |
74058313/cell_10 | [
"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)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id',... | code |
74058313/cell_12 | [
"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)
listings = pd.read_csv('/kaggle/input/airbnb-buenosaires/listings.csv')
calendar = pd.read_csv('/kaggle/input/airbnb-buenosaires/calendar.csv')
listings = listings.loc[:, ['id',... | code |
106214297/cell_20 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeli... | code |
106214297/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys() | code |
106214297/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHo... | code |
106214297/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHo... | code |
106214297/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import VotingClassifier
voting = VotingClassifier(estimators=[('sgd', SGDClassifier()), ('randForest', Ran... | code |
106214297/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/titanic/train.csv')
train_data.keys()
drop_train_data = train_data.drop(['Name', 'PassengerId', 'Cabin', 'Ticket'], axis=1).dropna(axis=0)
train_data['Family'] = train_data['SibSp'] + train_data['Parch']
train_data.corr() | code |
106214297/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV, train_test_... | code |
106214297/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessi... | code |
106214297/cell_12 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV,train_test_split
from sklearn.pipeli... | code |
106214297/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler,OneHotEncoder
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTr... | code |
2007984/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_trai... | code |
2007984/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
x_train.shape
x_train = x_trai... | code |
2007984/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].... | code |
2007984/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32') | code |
2007984/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
x_train = train[:, 1:].values.astype('float32')
y_train = train[:, 0].values.astype('int32')
x_test = test.values.astype('float32')
y_train.shape | code |
2007984/cell_29 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
from keras.preprocessing import image
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
import numpy as np # linear a... | code |
2007984/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Dropout, Activation,Lambda,Flatten
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv(... | code |
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