path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2042602/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
plt.scatter(y_test, predictions) | code |
2042602/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import Line... | code |
2042602/cell_2 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import Line... | code |
2042602/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(metrics.mean_absolute_error(y_test, predictions)) | code |
2042602/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import Line... | code |
2042602/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train) | code |
2042602/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(predictions) | code |
2042602/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import Line... | code |
2042602/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import seaborn as sns
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
sns.distplot(y_test - predictions) | code |
2042602/cell_12 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(metrics.mean_squared_error(y_test, predictions)) | code |
72109830/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
dict_cl_min = {'ph': 6.5, 'Hardness': 151, 'Solids': 0, 'Chloramines': 0, 'Sulfate': 0, 'Conductivity': 0, 'Organic_carbon': 0, 'Trihalomethanes': 0, 'Turbidity': 0}
dict_cl_max = {'ph': 8.5, 'Hardness': 300, 'Solids': 1200, ... | code |
72109830/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
df_water.describe() | code |
72109830/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
dict_cl_min = {'ph': 6.5, 'Hardness': 151, 'Solids': 0, 'Chloramines': 0, 'Sulfate': 0, 'Conductivity': 0, 'Organic_carbon': 0, 'Trihalomethanes': 0, 'Turbidity': 0}
dict_cl_max = {'ph': 8.5, 'Hardness': 300, 'Solids': 1200, ... | code |
73080358/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
X = features.copy()
print(X.shape)
X_test = test.copy()
print(X_test.shape)
categorical_cols = [cname... | code |
73080358/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=... | code |
73080358/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from tpot import TPOTRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
feature... | code |
73080358/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train... | code |
74052792/cell_6 | [
"image_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
import os
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, G... | code |
74052792/cell_11 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
import os
from collect... | code |
74052792/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe ... | code |
74052792/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe ... | code |
50239687/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data.head(10) | code |
50239687/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_1 | [
"image_output_1.png"
] | from IPython.display import Image
import os
from IPython.display import Image
Image(filename='../input/titlecw/title.png') | code |
50239687/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
sns.heatmap(da... | code |
50239687/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
print(data.siz... | code |
50239687/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dr... | code |
50239687/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data.head(10) | code |
122258520/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forec... | code |
122258520/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv'... | code |
122258520/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv'... | code |
122258520/cell_12 | [
"text_plain_output_1.png"
] | from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/god... | code |
122258520/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv'... | code |
105190901/cell_13 | [
"text_plain_output_1.png"
] | data = get_data(80) | code |
105190901/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # to show result
df = pd.DataFrame(data=data)
df.head(1) | code |
105190901/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # to show result
df = pd.DataFrame(data=data)
print(f'Number of rows is {df.shape[0]}')
print(f'Number of Nones is {df.isna().sum().sum()} in a column {df.columns[df.isna().any()].tolist()[0]}') | code |
18105662/cell_13 | [
"text_plain_output_1.png"
] | from concurrent.futures import ProcessPoolExecutor as PoolExecutor, as_completed
from google.cloud import automl_v1beta1
from tqdm import tqdm
import operator
import os
import pandas as pd
model_id = 'ICN8032497920993558639'
score_threshold = 1e-06
gcp_service_account_json = '/kaggle/input/gcloudserviceaccountkey... | code |
18105662/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | #AutoML package
!pip install google-cloud-automl | code |
34134222/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34134222/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
time_last = master_df[['gameId', 'gameDuration']].drop_duplicates().gameDuration.agg(['min', 'mean', 'max']).to_frame()
time_last.gameDuration = time_last.gameDuratio... | code |
34134222/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
time_last = master_df[['gameId', 'gameDuration']].drop_duplicates().gameDuration.agg(['min', 'mean', 'max']).to_frame()
time_last.gameDur... | code |
34134222/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
master_df.head(8) | code |
74070921/cell_4 | [
"text_plain_output_1.png"
] | from configparser import ConfigParser
from configparser import ConfigParser
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import os
import os
import tensorflow as tf
import tensorflow as tf
import numpy as np
import pandas as pd
import os
# -*- coding: utf-8 -*-
# @Author: Yulin Li... | code |
74070921/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 |
74070921/cell_3 | [
"text_plain_output_1.png"
] | from configparser import ConfigParser
import matplotlib.pyplot as plt
import os
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
import numpy as np
import tensorflow as tf
import os
from configparser import ConfigParser
import matplotlib.pyplot as plt
class visual_graph:
def ... | code |
16147633/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_tra... | code |
16147633/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_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
test_df.dtypes | code |
16147633/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
train_df.head() | code |
16147633/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.asty... | code |
16147633/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import keras
import os
print(os.listdir('../input')) | code |
16147633/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes | code |
16147633/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_tra... | code |
16147633/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
test_df.head() | code |
16147633/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_tra... | code |
16147633/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.matshow(X_train[0]) | code |
16147633/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
model = Sequential()
model.add(Flatten(inpu... | code |
16147633/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape) | code |
105187784/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
house_rent_df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/House_Rent_Dataset.csv') | code |
50221247/cell_9 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import tensorflow as tf
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'),... | code |
50221247/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 |
50221247/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import tensorflow as tf
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'),... | code |
50221247/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')... | code |
50221247/cell_14 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kagg... | code |
50221247/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kaggle/input/digit-recognizer/train.c... | code |
32074095/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.head() | code |
32074095/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/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 |
32074095/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees | code |
32074095/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
32074095/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index(... | code |
104120345/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.info() | code |
104120345/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.head() | code |
104120345/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 |
104120345/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)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
print(len(data)) | code |
104120345/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.describe() | code |
2012241/cell_6 | [
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import math
from IPython.display import HTML
from su... | code |
2012241/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import math
from IPython.display import HTML
from subprocess import check_output
print(check_output(['ls', '../input']).deco... | code |
90123938/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import matplotlib.pyplot as plt # 导入绘图工具包
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(... | code |
90123938/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
... | code |
90123938/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras import models
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = models.Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.ad... | code |
90123938/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from keras.datasets import mnist
(X_train_image, y_train_lable), (X_test_image, y_test_lable) = mnist.load_data() | code |
90123938/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tu... | code |
90123938/cell_8 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tu... | code |
90123938/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
... | code |
90123938/cell_5 | [
"text_plain_output_1.png"
] | print('第一个数据样本的标签：', y_train_lable[0]) | code |
32071949/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | def convert_news_text_to_specter_json(news_text_file):
d = {}
with open(news_text_file, 'r') as f:
print('test')
for i, l in enumerate(f):
print(l)
if i == 0:
d['paper_id'] = l
elif i == 1:
d['url'] = l
elif i == 2:
... | code |
2036996/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_... | code |
2036996/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
from sklearn import svm
import xgboost as xgb
fro... | code |
2036996/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sampleSubmission = pd.read_csv('../input/samp... | code |
2036996/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv'... | code |
88079861/cell_21 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.