path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2035143/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn import preprocessing,cross_validation,neighbors
from sklearn import tree
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
import numpy as np
import pan... | code |
2035143/cell_3 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from sklearn import preprocessing,cross_validation,neighbors
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
from keras.models import Sequential
from keras.layers import Dense
from ker... | code |
2035143/cell_5 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn import preprocessing,cross_validation,neighbors
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
from k... | code |
128048859/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
desc = train.describe().T
desc['nunique'] = train.nunique()
desc['%unique'] = desc['nunique'] / le... | code |
128048859/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
test.head() | code |
128048859/cell_20 | [
"text_html_output_1.png"
] | import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
original ... | code |
128048859/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV
from xgboost import XGBRegressor
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/... | code |
128048859/cell_2 | [
"text_html_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn import... | code |
128048859/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
train.info() | code |
128048859/cell_7 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
original.head() | code |
128048859/cell_18 | [
"text_plain_output_1.png"
] | import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
original = original.rename(co... | code |
128048859/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
original = original.rename(columns={'Row': 'id'})
original.head() | code |
128048859/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
desc = train.describe().T
desc['nunique'] = train.nunique()
desc['%unique']... | code |
128048859/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV
from xgboost import XGBRegressor
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/... | code |
128048859/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
desc = train.describe().T
desc['nunique'] = train.nunique()
desc['%unique'] = desc['nunique'] / le... | code |
128048859/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
train['yield'].value_counts() | code |
128048859/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
desc = train.describe().T
desc['nunique'] = train.nunique()
desc['%unique'] = desc['nunique'] / le... | code |
128048859/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/playground-series-s3e14/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s3e14/test.csv')
original = pd.read_csv('/kaggle/input/originall/dataa.csv')
train.head() | code |
105186901/cell_4 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
base_image_dir = os.path.join('..', 'input', 'diabetic-retinopathy-detection')
retina_df = pd.read_csv(os.path.join(base_image_dir, 'trainLabels.csv.zip'))
retina_df
train = r... | code |
105186901/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 |
105186901/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
x_train = train_datagen.flow_from_directory('../input/diabetic-retinopathy-detection/train.zip.001', batch_size=64) | code |
105186901/cell_3 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
base_image_dir = os.path.join('..', 'input', 'diabetic-retinopathy-detection')
retina_df = pd.read_csv(os.path.join(base_image_dir, 'trainLabels.csv.zip'))
retina_df | code |
105186901/cell_5 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
base_image_dir = os.path.join('..', 'input', 'diabetic-retinopathy-detection')
retina_df = pd.read_csv(os.path.join(base_image_dir, 'trainLabels.csv.zip'))
retina_df
test = re... | code |
104131103/cell_6 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.p... | from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
from sklearn.model_selection im... | code |
104131103/cell_8 | [
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as n... | code |
128020186/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 |
1008497/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
feature_columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
X = data[feature_columns]
y = data['Species']
y.head() | code |
1008497/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
sns.violinplot(x='Species', y='PetalWidthCm', data=data) | code |
1008497/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
sns.pairplot(data.drop('Id', axis=1), hue='Species', size=2) | code |
1008497/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr() | code |
1008497/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
, # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas... | code |
1008497/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
sns.violinplot(x='Species', y='PetalLengthCm', data=data) | code |
1008497/cell_16 | [
"text_html_output_1.png"
] | from IPython.display import Image
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pydotplus
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
feature_columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWi... | code |
1008497/cell_3 | [
"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('../input/Iris.csv')
data.head() | code |
1008497/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)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
feature_columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
X = data[feature_columns]
y = data['Species']
X.corr() | code |
1008497/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # plotting
data = pd.read_csv('../input/Iris.csv')
data.drop('Id', axis=1).corr()
feature_columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']
X = data[feature_columns]
y = data['Species']
X.head() | code |
122256289/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.show() | code |
122256289/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
fig, ax = plt.subplots(figsize=(10, 10))
plt.show() | code |
122256289/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
122256289/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
fig, ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='An Example Axes', ylabel='Y-Axis', xlabel='X-Axis')
plt.show() | code |
122256289/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y... | code |
32067582/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
print('Number of unique keywords : ', df.keyword.nunique()) | code |
32067582/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape | code |
32067582/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
df.drop('location', axis=1, inplace=True)
df.dropna(inplace=True)
df.shape
df.isnull().sum() | code |
32067582/cell_55 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
import nltk
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
df.drop('location',... | code |
32067582/cell_29 | [
"text_plain_output_1.png"
] | import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet') | code |
32067582/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts() | code |
32067582/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
df.drop('location', axis=1, inplace=True)
df.dropna(inplace=True)
df.shape | code |
32067582/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.head() | code |
32067582/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
print('Number of unique locations :', df.location.nunique()) | code |
32067582/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from nltk.corpus import stopwords
import nltk
import re
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
def Lower(text):
return text.lower()
def Tokenisation(text):
return nltk.word_tokenize(text)
test = Tokenisation('Hello there. How!... | code |
32067582/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
df.drop('location', axis=1, inplace=True)
df.dropna(inplace=True)
df.shape
df.isnull().sum()
df.reset_index(drop=True, inplace... | code |
32067582/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts() | code |
32067582/cell_53 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
import nltk
import pandas as pd
import re
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum()
df.target.value_counts()
df.keyword.value_counts()
df.drop('location',... | code |
32067582/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
df.shape
df.isnull().sum() | code |
105207855/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_std = df.rolling(window=12).std()
plt.xlabel = 'Dates'
... | code |
105207855/cell_30 | [
"text_plain_output_1.png"
] | from statsmodels.tools.eval_measures import rmse
from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
ro... | code |
105207855/cell_33 | [
"text_plain_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_... | code |
105207855/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
from pandas.plotting import autocorrelation_plot
from pandas import DataFrame
from pandas import concat
import numpy as np
from math import sqrt
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from ... | code |
105207855/cell_26 | [
"image_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_... | code |
105207855/cell_18 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean... | code |
105207855/cell_32 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_date... | code |
105207855/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.head() | code |
105207855/cell_17 | [
"image_output_1.png"
] | from matplotlib.pylab import rcParams
from statsmodels.tsa.seasonal import seasonal_decompose
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean... | code |
105207855/cell_24 | [
"image_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_... | code |
105207855/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_std = df.rolling(window=12).std()
plt.figure(figsize=(1... | code |
105207855/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
df.head() | code |
105207855/cell_27 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from statsmodels.tsa.statespace.sarimax import SARIMAX
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_... | code |
105207855/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/time-series-datasets/monthly-beer-production-in-austr.csv')
df.Month = pd.to_datetime(df.Month)
df = df.set_index('Month')
rolling_mean = df.rolling(window=12).mean()
rolling_std = df.rolling(window=12).std()
plt.figure(figsize=(10... | code |
1005562/cell_13 | [
"image_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
all_data = pd.concat((t... | code |
1005562/cell_57 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.cs... | code |
1005562/cell_56 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.cs... | code |
1005562/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv(... | code |
1005562/cell_55 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np... | code |
1005562/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv(... | code |
1005562/cell_41 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.cs... | code |
1005562/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice... | code |
1005562/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
all_data = pd.concat((t... | code |
1005562/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from keras.regularizers import l1
from scipy.stats import skew
from sklearn.preprocessing import StandardScaler
import matplotlib
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.r... | code |
1005562/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
1005562/cell_18 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice... | code |
1005562/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv(... | code |
1005562/cell_28 | [
"text_html_output_1.png"
] | from scipy.stats import skew
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv(... | code |
1005562/cell_8 | [
"image_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
prices.hist() | code |
1005562/cell_38 | [
"text_plain_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np... | code |
1005562/cell_46 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.models import Sequential
from keras.regularizers import l1
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split | code |
1005562/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
... | code |
1005562/cell_10 | [
"text_html_output_1.png"
] | import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
... | code |
1005562/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import skew
import matplotlib
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np... | code |
129012051/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
x_train = x_train.T
x_test = x_test.T
y_train = y_train.T
y_test = y_test.T
def initialize_weights_and_bias(dimension):
w = np.full((dimension, 1), 0.01)
b = 0.0
return (w, b)
def sigmoid(z):
y_head ... | code |
129012051/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from PIL import Image
import os
for dirname, _, filenames i... | code |
129012051/cell_7 | [
"text_plain_output_2.png",
"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/datacsv/data.csv')
data.info() | code |
128047268/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
GM_df = pd.read_excel('/kaggle/input/juan-anyang/GM.xlsx')
Mining_df = pd.read_excel('/kaggle/input/juan-anyang/Mining.xlsx')
GM_df.fillna(0, inplace=True)... | code |
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