path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17132381/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_46 | [
"text_plain_output_1.png"
] | grad = hook_g.stored[0][0].cpu()
grad.shape
grad_chan = grad.mean(1).mean(1)
grad_chan.shape | code |
17132381/cell_24 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_22 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_10 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
SEED = 999
seed_everything(S... | code |
17132381/cell_27 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_37 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
17132381/cell_36 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import os
import pandas as pd
import os
os.listdir('../input')
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.de... | code |
2021876/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum() | code |
2021876/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
print('Number rows and columns:', train.shape)
print('Number rows and columns:', test.shape) | code |
2021876/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull()... | code |
2021876/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T | code |
2021876/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
plt.figure(figsize=(16, 10))
sns.heatmap(cor, cmap='viridis') | code |
2021876/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
plt.figure(figsize=(12, 6))
sns.distplot(train['y'], bins=120)
plt.xlabel('y') | code |
2021876/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
test.isnull().sum().sum() | code |
2021876/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
2021876/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/'
train = pd.read_csv(path + 'train.csv', na_values=-1)
test = pd.read_csv(path + 'test.csv', na_values=-1)
train.head(5).T
cor = train.corr()
train.isnull().sum().sum()
test.isnull().sum().sum()
train_len = train.shape[0... | code |
50213631/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_13 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_4 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist() | code |
50213631/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_2 | [
"text_plain_output_1.png",
"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/indian-food-101/indian_food.csv')
df.head() | code |
50213631/cell_11 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_19 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/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 |
50213631/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df['flavor_profile'].unique() | code |
50213631/cell_15 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.info() | code |
50213631/cell_17 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_22 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
50213631/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique() | code |
50213631/cell_12 | [
"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/indian-food-101/indian_food.csv')
df.columns[df.isna().any()].tolist()
df = df[df['region'].notna()]
df = df[df['region'] != '-1']
df['region'].unique()
df = df[df['flavor_profile'] != '-1']
df['flavor_profile'].... | code |
33104934/cell_13 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import plotly.figure_factory as ff
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab... | code |
33104934/cell_9 | [
"text_html_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=Fals... | code |
33104934/cell_4 | [
"text_plain_output_1.png"
] | import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctTokenizer()
stop_words = nltk.corpus.stopwords.words('english')
def normaliz... | code |
33104934/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets | code |
33104934/cell_11 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=Fals... | code |
33104934/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
import scipy.cluster.hierarchy as sch
from scipy.cluster.hierarchy import ward, fcluster, lin... | code |
33104934/cell_7 | [
"text_html_output_1.png"
] | from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc
wpt = nltk.WordPunctToke... | code |
33104934/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=False)
tweets
doc = tweets['text']
doc | code |
33104934/cell_10 | [
"text_html_output_1.png"
] | from scipy import stats
from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.c... | code |
33104934/cell_12 | [
"text_plain_output_1.png"
] | from scipy.cluster.hierarchy import ward, fcluster, linkage
from scipy.spatial.distance import pdist
import nltk
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import re
import tensorflow_hub as hub
tweets = pd.read_csv('/kaggle/input/data-trab-2/Tweets.csv', error_bad_lines=Fals... | code |
33104934/cell_5 | [
"text_html_output_1.png"
] | import tensorflow_hub as hub
module_url = 'https://tfhub.dev/google/universal-sentence-encoder/4'
model = hub.load(module_url)
print('module %s loaded' % module_url)
def embed(input):
return model(input) | code |
130021822/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['HeatingQC'].value_counts() | code |
130021822/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['OverallCond'].value_counts() | code |
130021822/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot')
train_df['LotFrontage... | code |
130021822/cell_25 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(x='SalePrice', y='GarageArea') | code |
130021822/cell_23 | [
"image_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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot'... | code |
130021822/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum() | code |
130021822/cell_26 | [
"text_plain_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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot'... | code |
130021822/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(y='SalePrice', x='TotalBsmtSF') | code |
130021822/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 |
130021822/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df.plot.scatter(y='SalePrice', x='MasVnrArea') | code |
130021822/cell_15 | [
"text_plain_output_1.png"
] | 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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='OverallCond', data=train_df) | code |
130021822/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['ExterCond'].value_counts() | code |
130021822/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns | code |
130021822/cell_17 | [
"text_plain_output_1.png"
] | 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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='ExterCond', data=train_df) | code |
130021822/cell_31 | [
"text_plain_output_1.png",
"image_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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot'... | code |
130021822/cell_14 | [
"image_output_1.png"
] | 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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.barplot(y='SalePrice', x='HouseStyle', data=train_df) | code |
130021822/cell_22 | [
"image_output_1.png"
] | 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/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import seaborn as sns
sns.violinplot(data=train_df, y='SalePrice', x='HeatingQC') | code |
130021822/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['Utilities'].value_counts() | code |
130021822/cell_27 | [
"image_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
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
import matplotlib.pyplot as plt
plt.style.use('ggplot'... | code |
130021822/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.isnull().sum()
train_df['HouseStyle'].value_counts() | code |
130021822/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
train_df.columns
train_df.describe() | code |
105187067/cell_13 | [
"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)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', ax... | code |
105187067/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.head() | code |
105187067/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
test.head() | code |
105187067/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv... | code |
105187067/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', ax... | code |
105187067/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
train.head() | code |
105187067/cell_2 | [
"text_plain_output_1.png"
] | import warnings
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, PowerTransformer
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, cross_val_predict
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from skl... | code |
105187067/cell_19 | [
"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)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', ax... | code |
105187067/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 |
105187067/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
train.info() | code |
105187067/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', ax... | code |
105187067/cell_24 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, AdaBoostRegressor
from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
import numpy as np # linear algebra
import pandas as pd # data proc... | code |
105187067/cell_14 | [
"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)
train = pd.read_csv('../input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('../input/tabular-playground-series-sep-2022/test.csv')
df_total = train.append(test)
df_total.drop('row_id', ax... | code |
105187067/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor, AdaBoostRegressor
from sklearn.model_selection import cross_val_score, train_test_split,GridSearchCV, cross_val_predict
import numpy as np # linear algebra
import pandas as pd # data proc... | code |
72107191/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os, glob
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
from statsmodels.distributions.empirical_distribution import ECDF
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
prin... | code |
72107191/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_33 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_48 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.spl... | code |
72107191/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_38 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
72107191/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '../input/learnplatform-covid19-impact-on-digital-learning/engagement_data'
files = glob.glob(path + '/*.csv')
all_files = []
for filename in files:
df = pd.read_csv(filename, index_col=None, header=0)
district_id = filename.split('/')[4].split('.')[0]
df['district_id'] = distric... | code |
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