path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
89130056/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedCo... | code |
89130056/cell_25 | [
"text_plain_output_1.png"
] | nn_data = ImageDataset(file_path, active_df, LabEnc, img_size=224, normalize=True, crop=False) | code |
89130056/cell_34 | [
"image_output_1.png"
] | hog_data = ImageData(file_path, active_df, LabEnc, hog_mode=[9, (8, 8), (2, 2)], sift_mode=False, img_size=224) | code |
89130056/cell_29 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from skimage.feature import hog
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
f... | code |
89130056/cell_28 | [
"text_plain_output_1.png"
] | input_height = nn_data[0][0].shape[1]
input_width = nn_data[0][0].shape[2]
conv_channels = [nn_data[0][0].shape[0], 4, 16, 64, 128]
kernels = [3, 3, 3, 3]
maxpools = [2, 2, 2, 2]
lin_channels = [256, 128, 20]
dropout = 0.25
learning_rate = 1e-05
weight_decay = 1e-06
patience = 10
verbose_ct = 1
epochs = 2500
model = CN... | code |
89130056/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from torchvision import transforms, models
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
... | code |
89130056/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL ... | code |
89130056/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | learning_rate = 5e-05
weight_decay = 1e-06
patience = 10
verbose_ct = 1
epochs = 2500
model_conv = models.resnet18(pretrained=True)
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 256)
model_conv.fc2 = nn.Linear(256, 20)
model_conv.sfact = nn.Softmax(1)
model_conv = model_conv.to(device)
loss_f... | code |
89130056/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL ... | code |
89130056/cell_36 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | hog_classifier = svm.SVC(kernel='rbf', gamma=1.5, C=0.3)
hog_classifier.fit(X_train, y_train) | code |
50229480/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted ... | code |
50229480/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df.describe() | code |
50229480/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum() | code |
50229480/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b... | code |
50229480/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b... | code |
50229480/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.info() | code |
50229480/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c... | code |
50229480/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c... | code |
50229480/cell_48 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted ... | code |
50229480/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c... | code |
50229480/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df.head() | code |
50229480/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e... | code |
50229480/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.describe() | code |
50229480/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d... | code |
50229480/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b... | code |
50229480/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e... | code |
50229480/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum() | code |
50229480/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
df... | code |
50229480/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d... | code |
50229480/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b... | code |
50229480/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d... | code |
50229480/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df.head() | code |
50229480/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a... | code |
50229480/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e... | code |
50229480/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Precip Type'].value_counts() | code |
50229480/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted ... | code |
50229480/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df['Formatted Date'].value_counts() | code |
50229480/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50229480/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted ... | code |
129022822/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] ... | code |
129022822/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['valu... | code |
129022822/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980] | code |
129022822/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129022822/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['valu... | code |
129022822/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time']... | code |
129022822/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time']... | code |
129022822/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time']... | code |
129022822/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] ... | code |
129022822/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] ... | code |
129022822/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['valu... | code |
106202262/cell_21 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: ... | code |
106202262/cell_25 | [
"text_html_output_10.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_9.png",
"text_html_output_1.png",
"text_html_output_12.png",
"text_html_output_11.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text... | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: ... | code |
106202262/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df | code |
106202262/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.dro... | code |
106202262/cell_26 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.dro... | code |
106202262/cell_2 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import plotly.express as px
from plotly.offline import init_notebook_mode, iplot
from tashaphyne.stemming import ArabicLightStemme... | code |
106202262/cell_1 | [
"text_plain_output_1.png"
] | !pip install Arabic-Stopwords
!pip install emoji
!pip install Tashaphyne | code |
106202262/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum() | code |
106202262/cell_15 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.re... | code |
106202262/cell_17 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.re... | code |
72088017/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(train_df.shape)
display(train_df.head())
display(train_df.info()) | code |
72088017/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df[train_df.columns[2:]].std().hist()
plt.title('Distribu... | code |
72088017/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/d... | code |
72088017/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
from sklearn.metrics import roc_auc_score
y_val.shape
model = Lasso(alpha=0.0299)
model1 = RidgeClassifier(alpha=0.005)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
print('T... | code |
72088017/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 |
72088017/cell_18 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
corrs = train_df.corr().abs().unstack().sort_values(kind='quick... | code |
72088017/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/d... | code |
72088017/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df[train_df.columns[2:]].mean().hist()
plt.title('Distrib... | code |
72088017/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
print(train_df.duplicated().sum())
print(train_df.duplicated().... | code |
72088017/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
print(train_df.isnull().any().any())
print(test_df.isnull().any... | code |
72088017/cell_22 | [
"text_plain_output_1.png"
] | y_val.shape | code |
72088017/cell_10 | [
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(train_df.describe())
display(test_df.describe()) | code |
72088017/cell_12 | [
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df['target'].value_counts() | code |
72088017/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(test_df.shape)
display(test_df.head())
display(test_df.info()) | code |
129011222/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_40 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_41 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../in... | code |
129011222/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/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 |
129011222/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRA... | code |
129011222/cell_37 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../in... | code |
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