path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122247715/cell_22 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['age'].isnull().sum() | code |
122247715/cell_27 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic['embark_town'] = titanic['embark_town'].fillna(titanic['embark_town'].mode()[0])
titanic['embark_town'].isnull().sum() | code |
122247715/cell_36 | [
"text_plain_output_1.png"
] | import seaborn as sns
titanic = sns.load_dataset('titanic')
titanic.isnull().sum()
titanic.shape[0]
titanic.isnull().sum() / titanic.shape[0]
titanic.isnull().sum()
titanic.drop('deck', axis=1, inplace=True)
titanic.isnull().sum()
titanic['adult_male'].value_counts() | code |
88092005/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import random
import geocoder
import geopy
import plotly.express as px | code |
2005328/cell_13 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.... | code |
2005328/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.renam... | code |
2005328/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.head(... | code |
2005328/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import hypertools as hyp
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
messages = pd.read_csv('../input/spam.csv', encoding='lat... | code |
2005328/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Un... | code |
2005328/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.renam... | code |
2005328/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import p... | code |
2005328/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
import m... | code |
2005328/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)
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.head() | code |
2005328/cell_10 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(colu... | code |
128029591/cell_21 | [
"image_output_1.png"
] | 0.101 * 1141 + 16.57 | code |
128029591/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: ... | code |
128029591/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: ... | code |
128029591/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: 'Petrol', 2: 'Diesel', ... | code |
128029591/cell_20 | [
"image_output_1.png"
] | 0.101 * 1355 + 16.57 | code |
128029591/cell_29 | [
"text_plain_output_1.png"
] | 0.0604 * 998 + 57.087 | code |
128029591/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: ... | code |
128029591/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: ... | code |
128029591/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'... | code |
128029591/cell_28 | [
"text_plain_output_1.png"
] | 0.0604 * 1398 + 57.087 | code |
128029591/cell_16 | [
"image_output_1.png"
] | import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: 'Petrol', 2: 'Diesel', ... | code |
128029591/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data | code |
128029591/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'... | code |
128029591/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'... | code |
128029591/cell_22 | [
"text_html_output_1.png"
] | 0.101 * 1177 + 16.57 | code |
128029591/cell_27 | [
"text_plain_output_1.png"
] | 0.0604 * 1598 + 57.087 | code |
128029591/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'... | code |
128029591/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'] > 0) & (cars_data['CO2'] > 0)].copy()
cleaned_cars_data['PropulsionType'] = cars_data['PropulsionTypeId'].replace({1: 'Petrol', 2: 'Diesel', ... | code |
128029591/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
cars_data = pd.read_csv('../input/aqalds/AQA-large-data-set.csv')
cars_data
cleaned_cars_data = cars_data[(cars_data['EngineSize'] > 0) & (cars_data['Mass'... | code |
122255316/cell_25 | [
"text_plain_output_1.png"
] | cat_missing_cols = ['country']
cat_missing_cols | code |
122255316/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_33 | [
"text_html_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'to... | code |
122255316/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingRegressor
from sklearn.impute import KNNImputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-... | code |
122255316/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'to... | code |
122255316/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data | code |
122255316/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/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 |
122255316/cell_7 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'p... | code |
122255316/cell_45 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingRegressor
from sklearn.impute import KNNImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv... | code |
122255316/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_43 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingRegressor
from sklearn.impute import KNNImputer
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide... | code |
122255316/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'to... | code |
122255316/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
122255316/cell_10 | [
"text_html_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'p... | code |
122255316/cell_5 | [
"image_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'p... | code |
122255316/cell_36 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/covid-cases-and-deaths-worldwide/covid_worldwide.csv')
data
column_change = ['serial number', 'country', 'total_cases', 'total_deaths', 'total_recovered', 'active_cases', 'total_test', 'population']
data.columns =... | code |
73070655/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
df_test = pd.read_csv('../input/30-days-of-ml/test.c... | code |
73070655/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
df_test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
df_train.info() | code |
73070655/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
df_test = pd.read_csv('../input/30-days-of-ml/test.c... | code |
73070655/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
df_test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
df_train.head() | code |
18103775/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_9 | [
"image_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_23 | [
"text_html_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 1... | code |
18103775/cell_30 | [
"text_html_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.j... | code |
18103775/cell_40 | [
"image_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint
from keras.layers import Dropout, Dense, Flatten, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataG... | code |
18103775/cell_41 | [
"text_plain_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.callbacks import ModelCheckpoint
from keras.layers import Dropout, Dense, Flatten, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataG... | code |
18103775/cell_2 | [
"image_output_1.png"
] | from IPython.core.display import display, HTML
from PIL import Image
from io import BytesIO
import base64
plt.style.use('ggplot')
import tensorflow as tf
print(tf.__version__) | code |
18103775/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cv2
import seaborn as sns
from sklearn.metrics import f1_score
import os
print(os.listdir('../input'))
import warnings
warnings.filterwarnings('ignore')
from keras.applications.inception_v3 import InceptionV3, prep... | code |
18103775/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_8 | [
"image_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_16 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.j... | code |
18103775/cell_35 | [
"text_plain_output_1.png"
] | from keras.applications.inception_v3 import InceptionV3, preprocess_input
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height... | code |
18103775/cell_24 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 1... | code |
18103775/cell_14 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 1... | code |
18103775/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
18103775/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
main_folder = '../input/celeba-dataset/'
images_folder = main_folder + 'img_align_celeba/img_align_celeba/'
example_pic = images_folder + '000506.jpg'
training_sample = 10000
validation_sample = 2000
test_sample = 2000
img_width = 178
img_height = 218
batch_size = 16
num_epochs = 5
df_attr = pd.re... | code |
73090970/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=... | code |
73090970/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes | code |
73090970/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split, cross_val_score
import numpy as np
import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1',... | code |
73090970/cell_26 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split, cross_val_score
import numpy as np
import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-d... | code |
73090970/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'C... | code |
73090970/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.head() | code |
73090970/cell_18 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=... | code |
73090970/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=... | code |
73090970/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4'... | code |
73090970/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
73090970/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
crimes = pd.read_csv('../input/crime-in-los-angeles-data-from-2020-to-present/Crime_Data_from_2020_to_Present.csv')
crimes
crimes.drop(['DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', 'Crm Cd 4', 'Cross Street', 'LOCATION'], axis=1, inplace=True)
cri... | code |
72101116/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from optuna.visualization import plot_optimization_history, plot_param_importances
plot_param_importances(study) | code |
72101116/cell_13 | [
"text_html_output_1.png"
] | cat_features = ['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']
df_cat = feature_matrix[cat_features]
feature_matrix = feature_matrix.drop(cat_features, axis=1)
feature_matrix.head() | code |
72101116/cell_25 | [
"text_html_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.model_selection import KFold, train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklear... | code |
72101116/cell_19 | [
"text_html_output_1.png"
] | from optuna.visualization import plot_optimization_history, plot_param_importances
plot_optimization_history(study) | code |
72101116/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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.