path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
130003860/cell_49 | [
"text_html_output_1.png"
] | from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
gb = GradientBoostingClassifier(n_estimators=1000, max_depth=9, subsample=0.8,... | code |
130003860/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum() | code |
130003860/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
plt... | code |
130003860/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
sns... | code |
130003860/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data.columns
x = data.iloc[:, :-1]
y = data.iloc[:, -1]
(... | code |
130003860/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
test = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/test.csv')
test.drop('Id', axis=1, inplace=True)
test.shape | code |
130003860/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data[['BQ', 'DU', 'EL', 'FC', 'FL', 'FS', 'GL', 'CB', 'CC']... | code |
130003860/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object'] | code |
130003860/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/icr-identify-age-related-conditions/train.csv')
data.shape
data.isnull().sum()
data.dtypes[data.dtypes == 'object']
data.dtypes[data.dtypes == 'object'].isnull()
data.drop('Id', axis=1, inplace=True)
data.columns
data['EJ'].head() | code |
130003860/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
(x_train.shape, y_train.shape, x_test.shape, y_test.shape)
gb = GradientBoostingClassifier(n_estimators=1000, max_depth=9, subsample=0.8, max_features='log2', min_samples_leaf=9, random_state=42)
gb.fit(x_train, y_train) | code |
129002850/cell_4 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Activation
from keras.models import Sequential
from keras.optimizers import Adam
from sklearn.metrics import mean_squared_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import numpy as np
import random
import random
import random
import numpy as np
from ... | code |
129002850/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import random
import random
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
def perform_operation(a, b, op... | code |
90104804/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
graffiti_filepath = '../input/graffiti-incidents/Graffiti_Incidents.csv'
graffiti_data = pd.read_csv(graffiti_filepath, parse_dates=True, low_memory=False)
graffiti_data.head() | code |
90104804/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import seaborn as sns
print('Setup Complete') | code |
122264476/cell_4 | [
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
img = cv2.imread('/kaggle/input/brain-tumor-mri-images-44c/Carcinoma T2/112._big_gallery.jpeg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold_value = 70
max_value = 255
threshold_type = cv2.THRESH_BINARY
_, binary_img = cv2.threshold(gray_img, threshold_value, m... | code |
122264476/cell_3 | [
"image_output_1.png"
] | import cv2
img = cv2.imread('/kaggle/input/brain-tumor-mri-images-44c/Carcinoma T2/112._big_gallery.jpeg')
print(img.shape)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(gray_img.shape)
threshold_value = 70
max_value = 255
threshold_type = cv2.THRESH_BINARY
_, binary_img = cv2.threshold(gray_img, threshold_va... | code |
122264476/cell_10 | [
"text_plain_output_1.png"
] | from matplotlib import animation, rc
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import os
img = cv2.imread('/kaggle/input/brain-tumor-mri-images-44c/Carcinoma T2/112._big_gallery.jpeg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold_value = 70
max_value = 255
threshold_... | code |
122264476/cell_5 | [
"text_html_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
img = cv2.imread('/kaggle/input/brain-tumor-mri-images-44c/Carcinoma T2/112._big_gallery.jpeg')
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshold_value = 70
max_value = 255
threshold_type = cv2.THRESH_BINARY
_, binary_img = cv2.threshold(gray_img, threshold_value, m... | code |
17141097/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
2003283/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
test_corpus = np.load('../input/preprocessing/test_corp.npy')
train_corpus = np.load('../input/preprocessing/train_corp.npy')
glove_table = pd.read_csv('../input/preprocessing/filled_glove_table.csv', index_col=0)
glove_table.describe() | code |
2003283/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
test_corpus = np.load('../input/preprocessing/test_corp.npy')
train_corpus = np.load('../input/preprocessing/train_corp.npy')
glove_table = pd.read_csv('../input/preprocessing/filled_glove_table.csv', index_col=0)
glove_table.loc[['man', 'woman', 'man']].as_matrix().shape | code |
18127333/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
train.head() | code |
18127333/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18127333/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
122259999/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/review/diabetes.csv')
X = df.drop('Outcome', axis=1).values
y =... | code |
122259999/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
df = pd.read_csv('/kaggle/input/review/diabetes.csv')
X = df.drop('Outcome', axis=1).values
y = df['Outcome'].values
X_train, X_test, y_train, y_test = train_test_split(X... | code |
122259999/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = pm.confusion_matrix(y_test, y_pred).rav... | code |
122259999/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
GNB_model = GaussianNB()
GNB_model.fit(X_train, y_train)
y_pred = GNB_model.predict(X_test)
GNB_model.predict([[5.1, 2.5, 3.0, 1.1]]) | code |
122259999/cell_6 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred) | code |
122259999/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pylab as plt
import seaborn as sns
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred)
t... | code |
122259999/cell_19 | [
"image_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pylab as plt
import matplotlib.pylab as plt
import seaborn as sns
import seaborn as sns
import sklearn.metrics as pm
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn... | code |
122259999/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = pm.confusion_matrix(y_test, y_pred).rav... | code |
122259999/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pylab as plt
import matplotlib.pylab as plt
import seaborn as sns
import sklearn.metrics as pm
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_t... | code |
122259999/cell_8 | [
"text_plain_output_1.png"
] | print(60 / (60 + 49))
print(60 / (60 + 46))
print(2 * (60 / (60 + 49) * 60 / (60 + 46)) / (60 / (60 + 49) + 60 / (60 + 46))) | code |
122259999/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
nb_model = MultinomialNB()
nb_model.fit(X_train, y_train)
y_pred = nb_model.predict(X_test)
print(y_pred... | code |
122259999/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pylab as plt
import matplotlib.pylab as plt
import seaborn as sns
import sklearn.metrics as pm
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_t... | code |
122259999/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
df = pd.read_csv('/kaggle/input/review/diabetes.csv')
X = df.drop('Outcome', axis=1).values
y = df['Outcome'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101)
print(df.head()) | code |
122259999/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pylab as plt
import matplotlib.pylab as plt
import seaborn as sns
import sklearn.metrics as pm
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_t... | code |
122259999/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
GNB_model = GaussianNB()
GNB_model.fit(X_train, y_train)
y_pred = GNB_model.predict(X_test)
GNB_model.predict([[5.1, 2.5, 3.0, 1.1]])
GNB_model.predict([[6.5, 3.0, 5.5, 1.8]]) | code |
122259999/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import sklearn.metrics as pm
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
import sklearn.metrics as pm
pm.confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = pm.confusion_matrix(y_test, y_pred).rav... | code |
122259999/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
knn_model = KNeighborsClassifier(n_neighbors=7)
knn_model.fit(X_train, y_train)
y_pred = knn_model.predict(X_test)
print(y_pred)
print(y_test) | code |
2003618/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.tail() | code |
2003618/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_ = train['comment_text']
test_ = test['comment_text']
alldata = pd.concat([train_, test_], axis=0)
alldata = pd.DataFrame(alldata... | code |
2003618/cell_18 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
import lightgbm as lgbm
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train_ = train['comment_text']
test_ = test['comment_tex... | code |
128011911/cell_6 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
df = pd.read_csv('/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv', encoding='latin', header=None)
df.columns = ['sentiment', 'id', 'date', 'quer... | code |
128011911/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 |
128011911/cell_18 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
i... | code |
128011911/cell_15 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
i... | code |
128011911/cell_16 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
i... | code |
128011911/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv', encoding='latin', header=None)
df.columns = ['sentiment', 'id', 'date', 'query', 'user_id', 'text']
df = df.drop(['id', 'date', 'query', '... | code |
128011911/cell_5 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import string
df = pd.read_csv('/kaggle/input/sentiment140/training.1600000.processed.noemoticon.csv', encoding='latin', header=None)
df.columns = ['sentiment', 'id', 'date', 'quer... | code |
73089211/cell_2 | [
"text_html_output_1.png"
] | 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', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.describe(include='all') | code |
73089211/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 |
73089211/cell_3 | [
"text_html_output_1.png"
] | 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', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.head() | code |
73089211/cell_5 | [
"text_plain_output_1.png"
] | 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', index_col=0)
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
X_full.isna().sum() | code |
33115444/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection... | code |
33115444/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33115444/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklea... | code |
33115444/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33115444/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection... | code |
33115444/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
import os
for dir... | code |
33115444/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.model_selection import cross_validate
from sklearn.preprocessing import StandardScaler
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import num... | code |
33115444/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.preprocessing import StandardScaler
import numpy as np
import nu... | code |
33115444/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
33115444/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import pandas as pd
import os
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from skle... | code |
323155/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn.linear_model as sk
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full... | code |
323155/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory=False)
Pass_Plays = full_data_set.l... | code |
323155/cell_7 | [
"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)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory... | code |
323155/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.linear_model as sk
from sklearn import preprocessing
full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv', low_memory... | code |
89129128/cell_9 | [
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
pipeline = Pipeline(steps=[('vect', CountVectorizer()), ('cls', SVC())])
parameters = {'cls__C': (0.001, 0.01, 1, 10), 'cls_... | code |
89129128/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
len(df) | code |
89129128/cell_20 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np
import pandas... | code |
89129128/cell_11 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
... | code |
89129128/cell_1 | [
"text_plain_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 |
89129128/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
... | code |
89129128/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
pipeline = Pipeline(steps=[('vect', CountVectorizer()), ('cls', SVC())])
parameters = {'cls__C': (0.001, 0.01, 1, 10), 'cls_... | code |
89129128/cell_15 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
pipeline_other = Pipeline(steps=[('vect', CountVectorizer()), ('cls', SVC())])
parameters_other = {'cls__C': [0.01], 'cls__k... | code |
89129128/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
df.head() | code |
89129128/cell_17 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
... | code |
89129128/cell_12 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
... | code |
89129128/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas_profiling as pp
df = pd.read_csv('/kaggle/input/dementiapredictionnlp/Data.csv', sep=';')
profile = pp.ProfileReport(df)
profile | code |
2036115/cell_4 | [
"text_plain_output_1.png"
] | from gensim import corpora
from gensim import corpora, models, similarities
from nltk.corpus import stopwords
from string import punctuation
from subprocess import check_output
import os
import pandas as pd
import tempfile
import os
import pandas as pd
from gensim import corpora, models, similarities
from subpr... | code |
2036115/cell_6 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import OrderedDict
from gensim import corpora
from gensim import corpora, models, similarities
from nltk.corpus import stopwords
from string import punctuation
from subprocess import check_output
import os
import pandas as pd
import tempfile
import os
import pandas as pd
from gensim import cor... | code |
2036115/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import tempfile
import os
import pandas as pd
from gensim import corpora, models, similarities
from subprocess import check_output
import gensim
import logging
import tempfile
TEMP_FOLDER = tempfile.gettempdir()
print('Folder "{}" will be used to save temporary... | code |
2036115/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import tempfile
import os
import pandas as pd
from gensim import corpora, models, similarities
from subprocess import check_output
import gensim
import logging
import tempfile
TEMP_FOLDER = tempfile.gettempdir()
from gensim import corpora
from nltk.corpus impor... | code |
2036115/cell_5 | [
"text_plain_output_1.png"
] | from collections import OrderedDict
from gensim import corpora
from gensim import corpora, models, similarities
from nltk.corpus import stopwords
from string import punctuation
from subprocess import check_output
import os
import pandas as pd
import tempfile
import os
import pandas as pd
from gensim import cor... | code |
74046505/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder
numerical_pipeline = Pipeline([('scaling', MinMaxScaler())])
categoric_second_pipeline = ... | code |
74046505/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
sns.countplot(df.dtypes.map(str)) | code |
74046505/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df | code |
74046505/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape | code |
74046505/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-bikes-prices-in-india/Used_Bikes.csv')
df
df.shape
categoric_cols = [categoric for categoric in df.columns if df[categoric].dtype in ['object']]
categoric_cols
numeric_cols = [numeric for numeric in df.columns if df[numeric].dtype in ['float... | code |
74046505/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder, LabelEncoder
numerical_pipeline = Pipeline([('scaling', MinMaxScaler())])
categoric_second_pipeline = ... | code |
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