path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73079642/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from tensorflow.keras import losses
inputs = Input(shape=(28, 28, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = MaxPool2D()(x)
x = Dropout(0.2)(x)
x = Conv2D(32, 3, activation='relu', p... | code |
73079642/cell_33 | [
"image_output_1.png"
] | from keras.datasets import mnist, cifar10
from keras.layers import Conv2DTranspose, BatchNormalization, add, LeakyReLU
from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.optimizers import Adam
from tensorflow.keras import layers, losses
import ... | code |
73079642/cell_6 | [
"image_output_1.png"
] | from keras.datasets import mnist, cifar10
import matplotlib.pyplot as plt
import numpy as np
def preprocess(array1, array2, channel):
"""
Normalizes/scales [0,1], divinding by the supplied array and reshapes
it into the appropriate format.
"""
if channel == 1:
ar1 = array1.astype('float3... | code |
73079642/cell_29 | [
"text_plain_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.models import load_model
from tensorflow.keras import layers, losses
from tensorflow.keras import losses
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
def noise(a1... | code |
73079642/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Conv2D, MaxPool2D, UpSampling2D, Dense, Dropout
import tensorflow as tf
from keras.models import Model
from keras.datasets import mnist, cifar10 | code |
73079642/cell_7 | [
"image_output_1.png"
] | from keras.datasets import mnist, cifar10
import matplotlib.pyplot as plt
import numpy as np
def preprocess(array1, array2, channel):
"""
Normalizes/scales [0,1], divinding by the supplied array and reshapes
it into the appropriate format.
"""
if channel == 1:
ar1 = array1.astype('float3... | code |
73079642/cell_32 | [
"image_output_1.png"
] | from keras.datasets import mnist, cifar10
from keras.layers import Conv2DTranspose, BatchNormalization, add, LeakyReLU
from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.optimizers import Adam
from tensorflow.keras import layers, losses
import ... | code |
73079642/cell_28 | [
"image_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from tensorflow.keras import layers, losses
from tensorflow.keras import losses
import matplotlib.pyplot as plt
import tensorflow as tf
# Visualization for mnist, cifar10, noisy, denoised/predictions data
... | code |
73079642/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from tensorflow.keras import losses
inputs = Input(shape=(28, 28, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = MaxPool2D()(x)
x = Dropout(0.2)(x)
x = Conv2D(32, 3, activation='relu', p... | code |
73079642/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from tensorflow.keras import losses
import matplotlib.pyplot as plt
# Visualization for mnist, cifar10, noisy, denoised/predictions data
def display(rows, cols, a, b, check=False ):
'''rows: defining no.... | code |
73079642/cell_35 | [
"text_plain_output_1.png"
] | from keras.datasets import mnist, cifar10
from keras.layers import Conv2DTranspose, BatchNormalization, add, LeakyReLU
from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.optimizers import Adam
from tensorflow.keras import layers, losses
from te... | code |
73079642/cell_27 | [
"image_output_1.png"
] | from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from tensorflow.keras import layers, losses
from tensorflow.keras import losses
import tensorflow as tf
inputs = Input(shape=(28, 28, 1))
x = Conv2D(32, 3, activation='relu', padding='same')(inputs)
x = Max... | code |
73079642/cell_37 | [
"text_plain_output_1.png"
] | from keras.datasets import mnist, cifar10
from keras.layers import Conv2DTranspose, BatchNormalization, add, LeakyReLU
from keras.layers import Input,Conv2D,MaxPool2D, UpSampling2D,Dense, Dropout
from keras.models import Model
from keras.models import load_model
from keras.models import load_model
from keras.op... | code |
129014335/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df['Species'].value_counts() | code |
129014335/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
model_LR = LogisticRegression()
model_LR.fit(X_train, y_train) | code |
129014335/cell_30 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model_DTC = DecisionTreeClassifier()
model_DTC.fit(X_train, y_train)
predictionDTC = model... | code |
129014335/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df.head() | code |
129014335/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
model_DTC = DecisionTreeClassifier()
model_DTC.fit(X_train, y_train) | code |
129014335/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC
from sklearn.svm import SVC
model_SVC = SVC()
model_SVC.fit(X_train, y_train) | code |
129014335/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
sns.pairplot(df, hue='Species') | code |
129014335/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df.describe() | code |
129014335/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import numpy as np
from sklearn.svm import SVC
model_SVC = SVC()
model_SVC.fit(X_train, y_train)
predictionSVC = model_SVC.predict(X_test)
from sklearn.metri... | code |
129014335/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df.info() | code |
129014335/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns | code |
129014335/cell_17 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df.isnull().sum()
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['Species'] = le.fit_transform(df['Species'])
print(df.head())
print(df[50:55])... | code |
129014335/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
model_LR = LogisticRegression()
model_LR.fit(X_train, y_train)
predictionLR = model_LR.predict(X_test)
from sklearn.metrics import accuracy_score
print(accuracy_score(y_t... | code |
129014335/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
df = iris.drop(['Id'], axis=1)
df.isnull().sum() | code |
129014335/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.svm import SVC
model_SVC = SVC()
model_SVC.fit(X_train, y_train)
predictionSVC = model_SVC.predict(X_test)
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, prediction... | code |
129014335/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
iris = pd.read_csv('/kaggle/input/iris/Iris.csv')
iris.head() | code |
128047546/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold | code |
128047546/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston['description'] | code |
128047546/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
X = boston_df.drop('MEDV', axis=1)
y = boston_df['MEDV']
y | code |
128047546/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.model_selection import KFold
import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean... | code |
128047546/cell_7 | [
"text_plain_output_1.png"
] | column_desc = "Boston House Prices dataset\n===========================\n\nNotes\n------\nData Set Characteristics: \n\n :Number of Instances: 506 \n\n :Number of Attributes: 13 numeric/categorical predictive\n \n :Median Value (attribute 14) is usually the target\n\n :Attribute Information (in order):\... | code |
128047546/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
kf = KFold(n_splits=3, shuffle=True)
kf | code |
128047546/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
X = boston_df.drop('MEDV', axis=1)
X.shape
kf =... | code |
128047546/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys() | code |
128047546/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold
import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
X = boston_df.drop('MEDV', axis=1)
X.shape
kf =... | code |
128047546/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_predict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import pandas as pd
bost... | code |
128047546/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.model_selection import KFold
import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean... | code |
128047546/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
X = boston_df.drop('MEDV', axis=1)
X.shape | code |
128047546/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
X = boston_df.drop('MEDV', axis=1)
y = boston_df['MEDV']
y.shape | code |
128047546/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
boston = pd.read_pickle('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-ML240EN-SkillsNetwork/labs/data/boston_housing_clean.pickle')
boston.keys()
boston_df = boston['dataframe']
boston_df | code |
90107754/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.describe() | code |
90107754/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.describe() | code |
90107754/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Dense
from keras.layers import Dense,Dropout,Embedding,LSTM
from keras.layers import Embedding
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from keras.preprocessing.text import Tokenizer
from... | code |
90107754/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Dense
from keras.layers import Dense,Dropout,Embedding,LSTM
from keras.layers import Embedding
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from keras.preprocessing.text import Tokenizer
from... | code |
90107754/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.head() | code |
90107754/cell_19 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90107754/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 |
90107754/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.head() | code |
90107754/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90107754/cell_32 | [
"text_plain_output_1.png"
] | from keras.layers import Bidirectional
from keras.layers import Dense
from keras.layers import Dense,Dropout,Embedding,LSTM
from keras.layers import Embedding
from keras.layers import LSTM
from keras.models import Sequential
from keras.models import Sequential
from tensorflow.keras.layers import Dense, Embedding... | code |
90107754/cell_8 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.info() | code |
90107754/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90107754/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull(... | code |
90107754/cell_24 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_d... | code |
90107754/cell_14 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape | code |
90107754/cell_22 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnul... | code |
90107754/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape | code |
90107754/cell_12 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.info() | code |
90116977/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/lates... | code |
90116977/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_1 | [
"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 datetime as dt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90116977/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/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)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
90116977/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = '../input/latest-covid19-india-statewise-data/Latest Covid-19 India Status.csv'
data2 = '../input/covid-time-series-data-india-till-31oct21/case_time_series.csv'
state = '../input/latest-covid19-cases-maharashtra-india/Maharashtra Latest Cov... | code |
128046602/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
df = pd.read_csv('/kaggle/input/kullanlcaklar/Womens_Clothing_E-Commerce_Reviews_1.csv')
df = shuffle(df)
df = df.reset_index(drop=True)
df.head() | code |
128046602/cell_20 | [
"text_html_output_1.png"
] | from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pandas as pd
import random
import re
df = pd.read_csv('/kaggle/input/kullanlcaklar/Womens_Clothing_E-Commerce_Reviews_1.csv')
df = shuffle(df)
df = df.reset_index(drop=True)
N = 10
population_size = 100
mutation_rate = 0.3
generations = 200... | code |
128046602/cell_2 | [
"text_html_output_1.png"
] | import nltk
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
nltk.download('stopwords')
nltk.download('punkt') | code |
128046602/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/kullanlcaklar/Womens_Clothing_E-Commerce_Reviews_1.csv')
df.head() | code |
128046602/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.utils import shuffle
import pandas as pd
import random
import re
df = pd.read_csv('/kaggle/input/kullanlcaklar/Womens_Clothing_E-Commerce_Reviews_1.csv')
df = shuffle(df)
df = df.reset_index(drop=True)
N = 10
population_size = 100
mutation_rate = 0.3
generations = 200
text_column_name = 'Title'
rati... | code |
88076328/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.loc[(omicron_data['location'] == 'Thailand') & (omicron_data['variant'] == 'Omicron')] | code |
88076328/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.loc[(omicron_data['location'] == 'Thailand') & (omicron_data['variant'] == 'Omicron')]
omic... | code |
88076328/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
omicron_data = pd.read_csv('/kaggle/input/omicron-covid19-variant-daily-cases/covid-variants.csv')
omicron_data = pd.DataFrame(omicron_data)
omicron_data.head() | code |
74041571/cell_18 | [
"text_html_output_1.png"
] | from IPython.core.display import display, Markdown
from bs4 import BeautifulSoup
from pathlib import Path
import pandas as pd
PUB = Path('../input/30dmlleaderboards/public_lb.html')
PRIV = Path('../input/30dmlleaderboards/private_lb.html')
CSV_PUB = Path('../input/30dmlleaderboards/30-days-of-ml-publicleaderboard/3... | code |
74041571/cell_16 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from pathlib import Path
import pandas as pd
import plotly.express as px
PUB = Path('../input/30dmlleaderboards/public_lb.html')
PRIV = Path('../input/30dmlleaderboards/private_lb.html')
CSV_PUB = Path('../input/30dmlleaderboards/30-days-of-ml-publicleaderboard/30-days-of-ml-publiclead... | code |
74041571/cell_14 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from pathlib import Path
import pandas as pd
import plotly.express as px
PUB = Path('../input/30dmlleaderboards/public_lb.html')
PRIV = Path('../input/30dmlleaderboards/private_lb.html')
CSV_PUB = Path('../input/30dmlleaderboards/30-days-of-ml-publicleaderboard/30-days-of-ml-publiclead... | code |
74041571/cell_10 | [
"text_html_output_1.png"
] | from bs4 import BeautifulSoup
from pathlib import Path
import pandas as pd
import plotly.express as px
PUB = Path('../input/30dmlleaderboards/public_lb.html')
PRIV = Path('../input/30dmlleaderboards/private_lb.html')
CSV_PUB = Path('../input/30dmlleaderboards/30-days-of-ml-publicleaderboard/30-days-of-ml-publiclead... | code |
74041571/cell_12 | [
"text_html_output_2.png"
] | from bs4 import BeautifulSoup
from pathlib import Path
import pandas as pd
import plotly.express as px
PUB = Path('../input/30dmlleaderboards/public_lb.html')
PRIV = Path('../input/30dmlleaderboards/private_lb.html')
CSV_PUB = Path('../input/30dmlleaderboards/30-days-of-ml-publicleaderboard/30-days-of-ml-publiclead... | code |
1004763/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
DATA_FILE = '../input/uber-raw-data-aug14.csv'
uber_data = pd.read_csv(DATA_FILE)
uber_weekdays = uber_data.pivot_table(index=['DayOfWeekNum', 'DayOfWeek'], values='Base', aggfunc='count')
uber_weekdays.plot(kind='bar', figsize=(8, 6), color='red')
plt.ylabel('Numb... | code |
1004763/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_FILE = '../input/uber-raw-data-aug14.csv'
uber_data = pd.read_csv(DATA_FILE)
uber_data.head() | code |
1004763/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
DATA_FILE = '../input/uber-raw-data-aug14.csv'
uber_data = pd.read_csv(DATA_FILE)
uber_data['Base'].unique() | code |
1004763/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib import cm
print('Done') | code |
1004763/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
DATA_FILE = '../input/uber-raw-data-aug14.csv'
uber_data = pd.read_csv(DATA_FILE)
uber_data.head() | code |
1004763/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
DATA_FILE = '../input/uber-raw-data-aug14.csv'
uber_data = pd.read_csv(DATA_FILE)
uber_data.info() | code |
105191248/cell_21 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_13 | [
"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)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_9 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
youtube.info() | code |
105191248/cell_20 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
105191248/cell_6 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
youtube = pd.read_csv('/kaggle/input/youtube-new/GBvideos.csv')
missing_values_count = youtube.isnull().sum()
missing_values_count
total_cells = np.product(youtube.shape)
total_missing = missing_values_count.s... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.