path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50244797/cell_9 | [
"image_output_1.png"
] | from nltk.corpus import stopwords
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
print(stop_words) | code |
50244797/cell_23 | [
"text_html_output_1.png"
] | print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)
print(x_cv.shape)
print(y_cv.shape) | code |
50244797/cell_30 | [
"image_output_1.png"
] | """in this what we are doing as it is told that we have to use log loss for comparing and the thing is while
using auc we know that for random numbers auc is 0.5 so we have to make our model with accuracy more than 0.5
same thing here as we are using log loss so first we will determine log loss for random numbers with... | code |
50244797/cell_33 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | """by both obervation we can say that thershold is approximately 2.5 which means we have to build our model
having log loss less than 2.5""" | code |
50244797/cell_44 | [
"image_output_1.png"
] | x_train['Gene'].shape | code |
50244797/cell_55 | [
"text_plain_output_1.png"
] | """now by looking at all the three log losses we can say that we have to keep gene feature as log loss is less by keeping
only this feature as compared to random log loss""" | code |
50244797/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zip', sep='\\|\\|', engine='python', names=['ID'... | code |
50244797/cell_40 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.metrics.classification import accuracy_score, log_loss
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processin... | code |
50244797/cell_39 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-... | code |
50244797/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
train_class_distribution = y_train['Class'].value_counts()
train_class_distribution.plot(kind='bar')
plt.grid() | code |
50244797/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
gene_vectorizer = CountVectorizer()
train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(x_train['Gene'])
test_gene_feature_onehotCoding = gene_vectorizer.transform(x_test['Gene'])
cv_gene... | code |
50244797/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 |
50244797/cell_54 | [
"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 nltk.corpus import stopwords
from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.metrics.classification import accuracy... | code |
50244797/cell_11 | [
"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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_19 | [
"text_html_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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_45 | [
"image_output_1.png"
] | x_test['Gene'].shape | code |
50244797/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
gene_vectorizer = CountVectorizer()
train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(x_train['Gene'])
test_gene_feature_onehotCoding = gene_vectorizer.transform(x_test['Gene'])
cv_gene... | code |
50244797/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.metrics.classification import accuracy_score, log_loss
import numpy as np
import numpy as np # linear algebra
from sklearn.metrics.classification import accuracy_score, log_loss
cross_validation_size = y_cv.shape[0]
random_cross_valida... | code |
50244797/cell_51 | [
"text_plain_output_1.png"
] | from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.metrics.classification import accuracy_score, log_loss
import numpy as n... | code |
50244797/cell_58 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_38 | [
"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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_17 | [
"text_html_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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics.classification import accuracy_score, log_loss
import numpy as np
import numpy as np # linear algebra
from sklearn.metrics.classification import accuracy_score, log_loss
cross_validation_size = y_cv.shape[0]
random_cross_validation = np.zeros((cross_validation_size, 9))
for i in range(cross_vali... | code |
50244797/cell_46 | [
"text_plain_output_1.png"
] | x_cv['Gene'].shape | code |
50244797/cell_24 | [
"text_plain_output_1.png"
] | print('total training data = ', x_train.shape[0])
print('total cross validating data = ', x_cv.shape[0])
print('total testing data = ', x_test.shape[0]) | code |
50244797/cell_22 | [
"text_html_output_1.png"
] | print(x_tr.shape)
print(x_test.shape)
print(y_tr.shape)
print(y_test.shape) | code |
50244797/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.metrics.classification import accuracy_score, log_loss
import numpy as n... | code |
50244797/cell_27 | [
"text_plain_output_1.png"
] | test_class_distribution = y_test['Class'].value_counts()
test_class_distribution.plot(kind='bar') | code |
50244797/cell_37 | [
"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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_12 | [
"text_html_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
variant = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_variants.zip')
text_data = pd.read_csv('/kaggle/input/msk-redefining-cancer-treatment/training_text.zi... | code |
50244797/cell_36 | [
"text_plain_output_1.png"
] | """now we will do analysis of each feature one by one so lets start with gene""" | code |
72065978/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
sns.countplot(data=hotel_data, x='hotel', hue='is_canceled') | code |
72065978/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.head() | code |
72065978/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape | code |
72065978/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
print('Set Up') | code |
72065978/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
sns.countplot(data=hotel_data, x='hotel') | code |
72065978/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 |
72065978/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
hotel_data.info() | code |
72065978/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
hotel_data.describe() | code |
72065978/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
hotel_data['reserved_room_type'].unique() | code |
72065978/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)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data | code |
72065978/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
fig = plt.figure(figsize=(12, 5))
sns.countplot(data=hotel_data,... | code |
72065978/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
hotel_data['hotel'].unique() | code |
72065978/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.shape
sns.countplot(data=hotel_data, x='is_canceled', hue='is_repeated_guest') | code |
72065978/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hotel_data = pd.read_csv('../input/hotel-booking-demand/hotel_bookings.csv')
hotel_data
hotel_data.tail() | code |
1004498/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
def load_colls(load_dict, df):
for item in lo... | code |
1004498/cell_8 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
def load_colls(load_dict, df):
for item in load_dict:
file_... | code |
1004498/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
def load_colls(load_dict, df):
for item in load_dict:
file_... | code |
72069892/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace... | code |
72069892/cell_4 | [
"image_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from pycaret.regression import setup, compare_models, blend_models, finalize_model, predict_model | code |
72069892/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape) | code |
72069892/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.drop('id... | code |
72069892/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.head() | code |
72069892/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.drop('id... | code |
72069892/cell_3 | [
"image_output_1.png"
] | ! pip install pycaret | code |
72069892/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.drop('id... | code |
72069892/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
(train.shape, test.shape, submission.shape)
train.drop('id... | code |
90157177/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
eqLosses = pd.read_csv('/kaggle/input/2022-ukraine-russian-war/russia_losses_equipment.csv')
personnelLosses = pd.read_csv('/kaggle/input/2022-ukraine-russian-war/russia_losses_personnel.csv') | code |
130006594/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
co... | code |
130006594/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any() | code |
130006594/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.info() | code |
130006594/cell_25 | [
"image_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
co... | code |
130006594/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
co... | code |
130006594/cell_33 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
model = LogisticRegression()
model.fit(X_train, y_train)
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(y_train, X_train_predi... | code |
130006594/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.describe() | code |
130006594/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup) | code |
130006594/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape | code |
130006594/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
model = LogisticRegression()
model.fit(X_train, y_train)
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(y_train, X_train_predi... | code |
130006594/cell_28 | [
"image_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
co... | code |
130006594/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.head() | code |
130006594/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any() | code |
130006594/cell_35 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, ind... | code |
130006594/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train) | code |
130006594/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = ... | code |
130006594/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum() | code |
130006594/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = ... | code |
130006594/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns | code |
130006594/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
path = '/kaggle/input/breast-cancer-wisconsin-data/data.csv'
df = pd.read_csv(path, index_col=0)
df.shape
df.columns
df.isnull().values.any()
df.isnull().sum()
df.drop('Unnamed: 32', axis=1, inplace=True)
df.isnull().values.any()
dup = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
co... | code |
130006594/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
model = LogisticRegression()
model.fit(X_train, y_train)
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(y_train, X_train_predi... | code |
48162513/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
48162513/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'C... | code |
48162513/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
48162513/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'C... | code |
48162513/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
48162513/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
48162513/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
48162513/cell_10 | [
"text_html_output_2.png"
] | import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
data = pd.read_csv('/kaggle/input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
questions = data.iloc[0, :].T
data = data.iloc[1:, :]
data.rename({'Q1': 'AGE GROUP'}, axis=1, inplace=True)
data.rename(... | code |
50222118/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
50222118/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
50222118/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.s... | code |
50222118/cell_4 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | set(test_features['sig_id']) & set(train_features['sig_id']) | code |
50222118/cell_30 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.s... | code |
50222118/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train_features[['cp_dose']].value_counts() | code |
50222118/cell_29 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.s... | code |
50222118/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
50222118/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 |
50222118/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | submit.reset_index().to_csv('submission.csv', index=None) | code |
50222118/cell_28 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import matplotlib.pyplot as plt
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)
def crea... | code |
50222118/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
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