path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
73067082/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
miss... | code |
73067082/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
... | code |
73067082/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
miss... | code |
73067082/cell_5 | [
"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)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
missing_values_count = X_full.isnull().sum()
print('Total ... | code |
17120078/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection impo... | code |
17120078/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
... | code |
17120078/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import time
import re, nltk
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from collections import Counter
from subprocess import check_output
print(c... | code |
17120078/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes im... | code |
17120078/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import Gri... | code |
17120078/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import Gri... | code |
18136679/cell_13 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr... | from gensim.models import Word2Vec
from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile... | code |
18136679/cell_6 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content... | code |
18136679/cell_2 | [
"text_plain_output_1.png"
] | !pip install python-docx | code |
18136679/cell_11 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content... | code |
18136679/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/research_data/research_data/')) | code |
18136679/cell_7 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import jieba
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
fi... | code |
18136679/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import docx
import jieba
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
fi... | code |
18136679/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content... | code |
18136679/cell_10 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content... | code |
18136679/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', cont... | code |
32070353/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us | code |
32070353/cell_9 | [
"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/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us | code |
32070353/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/Stat... | code |
32070353/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.head() | code |
32070353/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/Stat... | code |
32070353/cell_33 | [
"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/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy | code |
32070353/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns | code |
32070353/cell_29 | [
"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
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/Stat... | code |
32070353/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axi... | code |
32070353/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axi... | code |
32070353/cell_2 | [
"text_html_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 |
32070353/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us ... | code |
32070353/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T | code |
32070353/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us ... | code |
32070353/cell_35 | [
"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/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axi... | code |
32070353/cell_31 | [
"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
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/Stat... | code |
32070353/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/Stat... | code |
32070353/cell_37 | [
"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/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axi... | code |
121150055/cell_9 | [
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.isnull().sum() | code |
121150055/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 =... | code |
121150055/cell_6 | [
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.head() | code |
121150055/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 |
121150055/cell_8 | [
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum() | code |
121150055/cell_15 | [
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.isnull().sum()
df2 = df2.replace(to_replace='[^0-9a-zA-Z ]... | code |
121150055/cell_3 | [
"text_html_output_1.png"
] | import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet') | code |
121150055/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 =... | code |
121150055/cell_14 | [
"text_plain_output_2.png",
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum()
df = df.replace(to_replace='[^0-9a-zA-Z ]+',... | code |
121150055/cell_22 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classifica... | code |
121150055/cell_5 | [
"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/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.head() | code |
32072941/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train.head(2) | code |
32072941/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 |
32072941/cell_5 | [
"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('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test.head(2) | code |
74060349/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = Tr... | code |
74060349/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = Tr... | code |
74060349/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = Tr... | code |
74060349/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = Tr... | code |
104127018/cell_21 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from yellowbrick.cluster import KElbowVisualizer
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
... | code |
104127018/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster imp... | code |
104127018/cell_34 | [
"image_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScal... | code |
104127018/cell_23 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.clus... | code |
104127018/cell_29 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sk... | code |
104127018/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.clus... | code |
104127018/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from... | code |
104127018/cell_7 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster imp... | code |
104127018/cell_32 | [
"text_html_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScal... | code |
104127018/cell_16 | [
"text_plain_output_1.png"
] | """#SKEWNESS
def check_skew(df_skew, column):
skew = stats.skew(df_skew[column])
skewtest = stats.skewtest(df_skew[column])
plt.title('Distribution of ' + column)
sns.histplot(df_skew[column],color = "g")
print("{}'s: Skew: {}, : {}".format(column, skew, skewtest))
return
plt.figure(figsize=(9, ... | code |
104127018/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yel... | code |
104127018/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.clus... | code |
104127018/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster imp... | code |
104127018/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster imp... | code |
104114369/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.read_csv('data/imdb/train.csv').sample(5)
pd.read_csv('data/imdb/valid.csv').sample(5) | code |
104114369/cell_2 | [
"text_html_output_1.png"
] | !pip install 'lightning-flash[text]' -q | code |
104114369/cell_11 | [
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
import flash
import torch
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', lab... | code |
104114369/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.read_csv('data/imdb/train.csv').sample(5) | code |
104114369/cell_3 | [
"text_html_output_1.png"
] | !pip install 'lightning-flash[serve]' -q | code |
104114369/cell_17 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
import flash
import torch
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', lab... | code |
104114369/cell_10 | [
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', labels=datamodule.labels) | code |
105212784/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | place = input('Enter the place you want to visit:')
budget = int(input('Enter your budget:'))
if place == 'sea':
print('we can go')
if budget >= 3000:
print('hurry up, tickets are ready') | code |
48162853/cell_13 | [
"text_plain_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/... | code |
48162853/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso
model = Lasso(0.05)
model.fit(X_train, y_train) | code |
48162853/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluat... | code |
48162853/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso
import matplotlib.pyplot as plt
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/train.csv'
TEST_DATASET_PATH = ... | code |
48162853/cell_12 | [
"text_plain_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluat... | code |
129033938/cell_21 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np... | code |
129033938/cell_13 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in ... | code |
129033938/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.lis... | code |
129033938/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np... | code |
129033938/cell_6 | [
"text_html_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.lis... | code |
129033938/cell_26 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from keras.utils import load_img, img_to_array
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensor... | code |
129033938/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import glob
import cv2 | code |
129033938/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in ... | code |
129033938/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np... | code |
129033938/cell_28 | [
"text_plain_output_1.png"
] | from IPython.display import FileLink
from IPython.display import FileLink
FileLink('cancer_detection_using_VGG19.h5') | code |
129033938/cell_15 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in ... | code |
129033938/cell_16 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in ... | code |
129033938/cell_17 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import seaborn as sns
import numpy as np
import pandas as pd
import os
for di... | code |
129033938/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
l... | code |
129033938/cell_12 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in ... | code |
129033938/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.lis... | code |
2042602/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(np.sqrt(metrics.mean_squared_error(y_test, predictions))) | code |
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