path
stringlengths 13
17
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sequencelengths 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 this datasets
and then we will try to built our model with log loss less than that random log loss"""
"we will take probablities of each class for all data points(for which we will divide class by total classes) \nIn a 'Random' Model, we generate the NINE class probabilites randomly such that they sum to 1." | 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', 'TEXT'], skiprows=1)
text_data | 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 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
train_class_distribution = y_train['Class'].value_counts()
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_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
from sklearn.metrics.classification import accuracy_score, log_loss
test_size = y_test.shape[0]
random_test_data = np.zeros((test_size, 9))
for i in range(test_size):
random_prob = np.random.rand(1, 9)
random_test_data[i] = (random_prob / sum(sum(random_prob)))[0]
unique_gene = result['Gene'].value_counts()
s = sum(unique_gene.values)
h = unique_gene / s
h
f, ax = plt.subplots(figsize=(10,6))
plt.plot(h, label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.show()
c = np.cumsum(h)
plt.plot(c, label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show() | 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-cancer-treatment/training_text.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
train_class_distribution = y_train['Class'].value_counts()
unique_gene = result['Gene'].value_counts()
s = sum(unique_gene.values)
h = unique_gene / s
h
f, ax = plt.subplots(figsize=(10, 6))
plt.plot(h, label='Histrogram of Genes')
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.show() | 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_feature_onehotCoding = gene_vectorizer.transform(x_cv['Gene'])
print(train_gene_feature_onehotCoding.shape)
print(test_gene_feature_onehotCoding.shape)
print(cv_gene_feature_onehotCoding.shape) | 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_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_12.png"
] | 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_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 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
train_class_distribution = y_train['Class'].value_counts()
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_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
from sklearn.metrics.classification import accuracy_score, log_loss
test_size = y_test.shape[0]
random_test_data = np.zeros((test_size, 9))
for i in range(test_size):
random_prob = np.random.rand(1, 9)
random_test_data[i] = (random_prob / sum(sum(random_prob)))[0]
unique_gene = result['Gene'].value_counts()
s = sum(unique_gene.values)
h = unique_gene / s
h
f, ax = plt.subplots(figsize=(10,6))
plt.plot(h, label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.show()
c = np.cumsum(h)
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_feature_onehotCoding = gene_vectorizer.transform(x_cv['Gene'])
from sklearn.linear_model import SGDClassifier
from sklearn.calibration import CalibratedClassifierCV
alpha = [10 ** i for i in range(-5, 1)]
cv_log_error_array = []
for i in alpha:
clf = SGDClassifier(loss='log', alpha=i, random_state=42)
clf.fit(train_gene_feature_onehotCoding, y_train)
calibration = CalibratedClassifierCV(clf, method='sigmoid')
calibration.fit(train_gene_feature_onehotCoding, y_train)
y_cv_predict = calibration.predict_proba(cv_gene_feature_onehotCoding)
cv_log_error_array.append(log_loss(y_cv, y_cv_predict))
min_log_loss_position = np.argmin(cv_log_error_array)
clf = SGDClassifier(loss='log', alpha=alpha[min_log_loss_position], random_state=42)
clf.fit(train_gene_feature_onehotCoding, y_train)
calibration = CalibratedClassifierCV(clf, method='sigmoid')
calibration.fit(train_gene_feature_onehotCoding, y_train)
y_train_predict = calibration.predict_proba(train_gene_feature_onehotCoding)
y_cv_predict = calibration.predict_proba(cv_gene_feature_onehotCoding)
y_test_predict = calibration.predict_proba(test_gene_feature_onehotCoding)
print('For values of alpha as', alpha[min_log_loss_position], 'The log loss for train data is:', log_loss(y_train, y_train_predict))
print('For values of alpha as', alpha[min_log_loss_position], 'The log loss for cv data is:', log_loss(y_cv, y_cv_predict))
print('For values of alpha as', alpha[min_log_loss_position], 'The log loss for test data is:', log_loss(y_test, y_test_predict)) | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
else:
print('there is no text description for id:', index) | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
result.head() | 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_feature_onehotCoding = gene_vectorizer.transform(x_cv['Gene'])
print(train_gene_feature_onehotCoding.toarray()) | 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_validation = np.zeros((cross_validation_size, 9))
for i in range(cross_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
from sklearn.metrics.classification import accuracy_score, log_loss
test_size = y_test.shape[0]
random_test_data = np.zeros((test_size, 9))
for i in range(test_size):
random_prob = np.random.rand(1, 9)
random_test_data[i] = (random_prob / sum(sum(random_prob)))[0]
print('log loss for random test data is', log_loss(y_test, random_test_data)) | 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 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_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
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_feature_onehotCoding = gene_vectorizer.transform(x_cv['Gene'])
from sklearn.linear_model import SGDClassifier
from sklearn.calibration import CalibratedClassifierCV
alpha = [10 ** i for i in range(-5, 1)]
cv_log_error_array = []
for i in alpha:
clf = SGDClassifier(loss='log', alpha=i, random_state=42)
clf.fit(train_gene_feature_onehotCoding, y_train)
calibration = CalibratedClassifierCV(clf, method='sigmoid')
calibration.fit(train_gene_feature_onehotCoding, y_train)
y_cv_predict = calibration.predict_proba(cv_gene_feature_onehotCoding)
cv_log_error_array.append(log_loss(y_cv, y_cv_predict))
print('For values of alpha = ', i, 'The log loss is:', log_loss(y_cv, y_cv_predict)) | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
unique_variant = result['Variation'].value_counts()
unique_variant | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
result[result['ID'] == 1109] | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
unique_gene = result['Gene'].value_counts()
s = sum(unique_gene.values)
h = unique_gene / s
h | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
result | 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_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
print('log loss for random cross validation is', log_loss(y_cv, random_cross_validation)) | 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 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_validation_size):
random_prob = np.random.rand(1, 9)
random_cross_validation[i] = (random_prob / sum(sum(random_prob)))[0]
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_feature_onehotCoding = gene_vectorizer.transform(x_cv['Gene'])
from sklearn.linear_model import SGDClassifier
from sklearn.calibration import CalibratedClassifierCV
alpha = [10 ** i for i in range(-5, 1)]
cv_log_error_array = []
for i in alpha:
clf = SGDClassifier(loss='log', alpha=i, random_state=42)
clf.fit(train_gene_feature_onehotCoding, y_train)
calibration = CalibratedClassifierCV(clf, method='sigmoid')
calibration.fit(train_gene_feature_onehotCoding, y_train)
y_cv_predict = calibration.predict_proba(cv_gene_feature_onehotCoding)
cv_log_error_array.append(log_loss(y_cv, y_cv_predict))
print(cv_log_error_array) | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result[result.isnull().any(axis=1)]
result.loc[result['TEXT'].isnull(), 'TEXT'] = result['Gene'] + '' + result['Variation']
y_true = result['Class'].values
result.Gene = result.Gene.str.replace('\\s+', '_')
result.Variation = result.Variation.str.replace('\\s+', '_')
x = result.drop('Class', axis=1)
y = result.iloc[:, 3:4]
unique_gene = result['Gene'].value_counts()
unique_gene.head(20) | 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.zip', sep='\\|\\|', engine='python', names=['ID', 'TEXT'], skiprows=1)
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
import re
def cleaning(text, index, column):
if type(text) is not int:
string = ''
text = re.sub('[^a-zA-Z0-9\n]', ' ', text)
text = re.sub('\\s+', ' ', text)
text = text.lower()
for word in text.split():
if word not in stop_words:
string += word + ' '
text_data[column][index] = string
for index, row in text_data.iterrows():
if type(row['TEXT']) is str:
cleaning(row['TEXT'], index, 'TEXT')
result = pd.merge(variant, text_data, on='ID', how='left')
result.head() | 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, x='arrival_date_month')
plt.xlabel('Months') | 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 load_dict:
file_name = load_dict[item][0]
entry = load_dict[item][1]
index = load_dict[item][2]
temp_dict = pd.read_csv(file_name, index_col=entry).to_dict()
temp_dict = temp_dict[index]
if entry == 'hs4%0':
entry = 'hs4'
df[item] = df[entry].map(temp_dict)
return df
load_map = {'Country_name': ['../input/country_eng.csv', 'Country', 'Country_name'], 'hs2_name': ['../input/hs2_eng.csv', 'hs2', 'hs2_name'], 'hs4_name': ['../input/hs4_eng.csv', 'hs4%0', 'hs4_name'], 'hs6_name': ['../input/hs6_eng.csv', 'hs6', 'hs6_name'], 'hs9_name': ['../input/hs9_eng.csv', 'hs9', 'hs9_name']}
trade_hist = pd.read_csv('../input/year_1988_2015.csv')
trade_hist = load_colls(load_map, trade_hist)
trade_jpbg_hist = trade_hist[trade_hist['Country_name'] == 'Bulgaria']
trade_2016 = pd.read_csv('../input/ym_custom_2016.csv')
trade_2016 = load_colls(load_map, trade_2016)
trade_jpbg_2016 = trade_2016[trade_2016['Country_name'] == 'Bulgaria'] | 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_name = load_dict[item][0]
entry = load_dict[item][1]
index = load_dict[item][2]
temp_dict = pd.read_csv(file_name, index_col=entry).to_dict()
temp_dict = temp_dict[index]
if entry == 'hs4%0':
entry = 'hs4'
df[item] = df[entry].map(temp_dict)
return df
load_map = {'Country_name': ['../input/country_eng.csv', 'Country', 'Country_name'], 'hs2_name': ['../input/hs2_eng.csv', 'hs2', 'hs2_name'], 'hs4_name': ['../input/hs4_eng.csv', 'hs4%0', 'hs4_name'], 'hs6_name': ['../input/hs6_eng.csv', 'hs6', 'hs6_name'], 'hs9_name': ['../input/hs9_eng.csv', 'hs9', 'hs9_name']}
trade_hist = pd.read_csv('../input/year_1988_2015.csv')
trade_hist = load_colls(load_map, trade_hist)
trade_jpbg_hist = trade_hist[trade_hist['Country_name'] == 'Bulgaria']
trade_2016 = pd.read_csv('../input/ym_custom_2016.csv')
trade_2016 = load_colls(load_map, trade_2016)
trade_jpbg_2016 = trade_2016[trade_2016['Country_name'] == 'Bulgaria']
trjpbg_gb_year = trade_jpbg_hist.groupby(by=['Year'], as_index=False)['VY'].sum()
plt.plot(trjpbg_gb_year['Year'], trjpbg_gb_year['VY'])
plt.xlabel('Year')
plt.ylabel('VY')
plt.title('Japan-Bulgaria Trade over the years, grouped by year') | 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_name = load_dict[item][0]
entry = load_dict[item][1]
index = load_dict[item][2]
temp_dict = pd.read_csv(file_name, index_col=entry).to_dict()
temp_dict = temp_dict[index]
if entry == 'hs4%0':
entry = 'hs4'
df[item] = df[entry].map(temp_dict)
return df
load_map = {'Country_name': ['../input/country_eng.csv', 'Country', 'Country_name'], 'hs2_name': ['../input/hs2_eng.csv', 'hs2', 'hs2_name'], 'hs4_name': ['../input/hs4_eng.csv', 'hs4%0', 'hs4_name'], 'hs6_name': ['../input/hs6_eng.csv', 'hs6', 'hs6_name'], 'hs9_name': ['../input/hs9_eng.csv', 'hs9', 'hs9_name']}
trade_hist = pd.read_csv('../input/year_1988_2015.csv')
trade_hist = load_colls(load_map, trade_hist)
trade_jpbg_hist = trade_hist[trade_hist['Country_name'] == 'Bulgaria']
trade_2016 = pd.read_csv('../input/ym_custom_2016.csv')
trade_2016 = load_colls(load_map, trade_2016)
trade_jpbg_2016 = trade_2016[trade_2016['Country_name'] == 'Bulgaria']
trjpbg_gb_year = trade_jpbg_hist.groupby(by=['Year'], as_index=False)['VY'].sum()
print('Minimum trade:', trjpbg_gb_year['VY'].min())
print('Maximum trade:', trjpbg_gb_year['VY'].max())
print('Mean trade:', trjpbg_gb_year['VY'].mean()) | 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=True)
train.nunique() | 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', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.nunique()
plt.figure(figsize=(15, 5))
sns.kdeplot(train['target'], shade=True, alpha=0.9, linewidth=1.5, facecolor=(1, 1, 1, 0), edgecolor='.2')
plt.title('Target', fontdict={'fontsize': 20})
plt.show() | 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', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.nunique()
fig, axes =plt.subplots(5,2, figsize=(15,15), sharex=True)
axes = axes.flatten()
object_bol = train.dtypes == 'object'
for ax, catplot in zip(axes, train.dtypes[object_bol].index):
sns.countplot(y=catplot, data=train, ax=ax)
plt.tight_layout()
plt.show()
fig = plt.figure(figsize=(15, 60))
for i in range(len(train.columns.tolist()[10:24])):
plt.subplot(20, 5, i + 1)
sns.set_style('white')
plt.title(train.columns.tolist()[10:24][i], size=12, fontname='monospace')
a = sns.kdeplot(train[train.columns.tolist()[10:24][i]], shade=True, alpha=0.9, linewidth=1.5, facecolor=(1, 1, 1, 0), edgecolor='.2')
plt.ylabel('')
plt.xlabel('')
plt.xticks(fontname='monospace')
plt.yticks([])
for j in ['right', 'left', 'top']:
a.spines[j].set_visible(False)
a.spines['bottom'].set_linewidth(1.2)
fig.tight_layout(h_pad=3)
plt.show() | 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', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.nunique()
fig, axes =plt.subplots(5,2, figsize=(15,15), sharex=True)
axes = axes.flatten()
object_bol = train.dtypes == 'object'
for ax, catplot in zip(axes, train.dtypes[object_bol].index):
sns.countplot(y=catplot, data=train, ax=ax)
plt.tight_layout()
plt.show()
fig = plt.figure(figsize = (15, 60))
for i in range(len(train.columns.tolist()[10:24])):
plt.subplot(20,5,i+1)
sns.set_style("white")
plt.title(train.columns.tolist()[10:24][i], size = 12, fontname = 'monospace')
a = sns.kdeplot(train[train.columns.tolist()[10:24][i]], shade = True, alpha = 0.9, linewidth = 1.5, facecolor=(1, 1, 1, 0), edgecolor=".2")
plt.ylabel('')
plt.xlabel('')
plt.xticks(fontname = 'monospace')
plt.yticks([])
for j in ['right', 'left', 'top']:
a.spines[j].set_visible(False)
a.spines['bottom'].set_linewidth(1.2)
fig.tight_layout(h_pad = 3)
plt.show()
fig = plt.figure(figsize=(15, 60))
for i in range(len(train.columns.tolist()[10:24])):
plt.subplot(20, 5, i + 1)
sns.set_style('white')
plt.title(train.columns.tolist()[10:24][i], size=12, fontname='monospace')
a = sns.boxplot(train[train.columns.tolist()[10:24][i]], linewidth=2.5, color='white')
plt.ylabel('')
plt.xlabel('')
plt.xticks(fontname='monospace')
plt.yticks([])
for j in ['right', 'left', 'top']:
a.spines[j].set_visible(False)
a.spines['bottom'].set_linewidth(1.2)
fig.tight_layout(h_pad=3)
plt.show() | 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', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.nunique()
fig, axes = plt.subplots(5, 2, figsize=(15, 15), sharex=True)
axes = axes.flatten()
object_bol = train.dtypes == 'object'
for ax, catplot in zip(axes, train.dtypes[object_bol].index):
sns.countplot(y=catplot, data=train, ax=ax)
plt.tight_layout()
plt.show() | 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()
corr | 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()
corr
labels = ['B', 'M']
df['diagnosis'].value_counts().plot(kind='bar', figsize=(4, 3)) | 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()
corr
df[170:180] | 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_prediction)
training_data_accuracy
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(y_test, X_test_prediction)
test_data_accuracy | 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_prediction)
training_data_accuracy | 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()
corr
labels = ['B', 'M']
label = [labels.index(i) for i in df['diagnosis']]
(label[:5], df['diagnosis'][:5])
X = df.drop(columns='diagnosis', axis=1)
y = label
X | 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, 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()
corr
sns.set(rc={'figure.figsize': (7, 7)})
fig, ax = plt.subplots(figsize=(7, 3))
sns.barplot(x="radius_mean", y="texture_mean", ax=ax, data=df[170:180])
plt.title("Radius Mean vs Texture Mean",fontsize=15)
plt.xlabel("Radius Mean")
plt.ylabel("Texture Mean")
plt.show()
plt.style.use("ggplot")
model = LogisticRegression()
model.fit(X_train, y_train)
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(y_train, X_train_prediction)
training_data_accuracy
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(y_test, X_test_prediction)
test_data_accuracy
fig, ax = plt.subplots(figsize=(4, 4))
sns.heatmap(confusion_matrix(y_test, X_test_prediction), annot=True, ax=ax) | 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 = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
corr
sns.set(rc={'figure.figsize': (7, 7)})
fig, ax = plt.subplots(figsize=(7, 3))
sns.barplot(x='radius_mean', y='texture_mean', ax=ax, data=df[170:180])
plt.title('Radius Mean vs Texture Mean', fontsize=15)
plt.xlabel('Radius Mean')
plt.ylabel('Texture Mean')
plt.show()
plt.style.use('ggplot') | 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 = df.loc[df.duplicated(), :]
len(dup)
corr = df.corr()
corr
sns.set(rc={'figure.figsize': (7, 7)})
sns.heatmap(corr, cmap='RdBu')
plt.show() | 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()
corr
labels = ['B', 'M']
label = [labels.index(i) for i in df['diagnosis']]
(label[:5], df['diagnosis'][:5]) | 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_prediction)
training_data_accuracy
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(y_test, X_test_prediction)
test_data_accuracy
f1s = f1_score(y_test, X_test_prediction)
ps = precision_score(y_test, X_test_prediction)
rs = recall_score(y_test, X_test_prediction)
print('The evaluation metrics are given below: \nf1_score:', f1s, '\nprecision_score:', ps, '\nrecall_score:', rs) | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
labels = ['18-21', '22-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-69', '70+']
o_values = data['AGE GROUP'].value_counts(sort=True).values
o_pie = go.Pie(labels=labels, values=o_values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
i_values = idata['AGE GROUP'].value_counts(sort=True).values
i_pie = go.Pie(labels=labels, values=i_values, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='AGE GROUPWISE DISTRIBUTION', font=dict(size=10), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
py.iplot(fig) | 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': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig.show() | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
labels = ['18-21', '22-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-69', '70+']
o_values = data['AGE GROUP'].value_counts(sort=True).values
o_pie = go.Pie(labels=labels, values=o_values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
i_values = idata['AGE GROUP'].value_counts(sort=True).values
i_pie = go.Pie(labels=labels, values=i_values, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='AGE GROUPWISE DISTRIBUTION', font=dict(size=10), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
counts = data['EDUCATION'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['EDUCATION'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='HIGHER EDUCATION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
fig = px.histogram(idata, y="EDUCATION", color="AGE GROUP")
fig.update_layout(title = "AGE GROUPWISE HIGHER EDUCATION DISTRIBUTION - INDIA")
fig.show()
count_male = imale['EDUCATION'].value_counts().reset_index()
count_female = ifemale['EDUCATION'].value_counts().reset_index()
pie_men = go.Pie(labels=count_male['index'], values=count_male['EDUCATION'], name='Men', hole=0.4, domain={'x': [0, 0.46]})
pie_women = go.Pie(labels=count_female['index'], values=count_female['EDUCATION'], name='Women', hole=0.5, domain={'x': [0.52, 1]})
layout = dict(title='GENDERWISE HIGHER EDUCATION - INDIA', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.19, y=0.5, text='Men', showarrow=False, font=dict(size=20)), dict(x=0.81, y=0.5, text='Women', showarrow=False, font=dict(size=20))])
fig = dict(data=[pie_men, pie_women], layout=layout)
py.iplot(fig) | 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': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y='COUNTRY', histnorm='percent')
fig.update_layout(xaxis={'ticksuffix': '%'})
fig.update_layout(xaxis_title='COUNT PERCENTAGE')
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)', marker_line_width=1.5, opacity=1)
fig.show() | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
labels = ['18-21', '22-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-69', '70+']
o_values = data['AGE GROUP'].value_counts(sort=True).values
o_pie = go.Pie(labels=labels, values=o_values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
i_values = idata['AGE GROUP'].value_counts(sort=True).values
i_pie = go.Pie(labels=labels, values=i_values, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='AGE GROUPWISE DISTRIBUTION', font=dict(size=10), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
counts = data['EDUCATION'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['EDUCATION'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='HIGHER EDUCATION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
py.iplot(fig) | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
labels = ['18-21', '22-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-69', '70+']
o_values = data['AGE GROUP'].value_counts(sort=True).values
o_pie = go.Pie(labels=labels, values=o_values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
i_values = idata['AGE GROUP'].value_counts(sort=True).values
i_pie = go.Pie(labels=labels, values=i_values, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='AGE GROUPWISE DISTRIBUTION', font=dict(size=10), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
counts = data['EDUCATION'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['EDUCATION'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='HIGHER EDUCATION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
fig = px.histogram(idata, y='EDUCATION', color='AGE GROUP')
fig.update_layout(title='AGE GROUPWISE HIGHER EDUCATION DISTRIBUTION - INDIA')
fig.show() | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
labels = ['18-21', '22-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-69', '70+']
o_values = data['AGE GROUP'].value_counts(sort=True).values
o_pie = go.Pie(labels=labels, values=o_values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
i_values = idata['AGE GROUP'].value_counts(sort=True).values
i_pie = go.Pie(labels=labels, values=i_values, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='AGE GROUPWISE DISTRIBUTION', font=dict(size=10), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
counts = data['EDUCATION'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['EDUCATION'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='HIGHER EDUCATION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
fig = px.histogram(idata, y="EDUCATION", color="AGE GROUP")
fig.update_layout(title = "AGE GROUPWISE HIGHER EDUCATION DISTRIBUTION - INDIA")
fig.show()
count_male = imale['EDUCATION'].value_counts().reset_index()
count_female = ifemale['EDUCATION'].value_counts().reset_index()
pie_men = go.Pie(labels=count_male['index'], values=count_male['EDUCATION'], name='Men', hole=0.4, domain={'x': [0, 0.46]})
pie_women = go.Pie(labels=count_female['index'], values=count_female['EDUCATION'], name='Women', hole=0.5, domain={'x': [0.52, 1]})
layout = dict(title='GENDERWISE HIGHER EDUCATION - INDIA', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.19, y=0.5, text='Men', showarrow=False, font=dict(size=20)), dict(x=0.81, y=0.5, text='Women', showarrow=False, font=dict(size=20))])
fig = dict(data=[pie_men, pie_women], layout=layout)
fig = px.histogram(idata, y='TITLE', color='AGE GROUP')
fig.update_layout(title='AGE GROUPWISE CURRENT ROLE/ TITLE - INDIA')
fig.show() | 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({'Q2': 'GENDER'}, axis=1, inplace=True)
data.rename({'Q3': 'COUNTRY'}, axis=1, inplace=True)
data.rename({'Q4': 'EDUCATION'}, axis=1, inplace=True)
data.rename({'Q5': 'TITLE'}, axis=1, inplace=True)
data.rename({'Q6': 'CODING EXP'}, axis=1, inplace=True)
data.rename({'Q8': '1ST LANG'}, axis=1, inplace=True)
idata = data[data['COUNTRY'] == 'India']
imale = idata[idata['GENDER'] == 'Man']
ifemale = idata[idata['GENDER'] == 'Woman']
country_wise_distribution = data['COUNTRY'].value_counts()
fig = px.choropleth(country_wise_distribution.values, locations=country_wise_distribution.index, locationmode='country names', color=country_wise_distribution.values, labels={'color': 'Count', 'locations': 'Location'}, color_continuous_scale=px.colors.sequential.OrRd)
fig.update_layout(title='COUNTRYWISE DISTRIBUTION')
fig = px.histogram(data, y="COUNTRY", histnorm='percent')
fig.update_layout(xaxis={"ticksuffix":"%"})
fig.update_layout(xaxis_title="COUNT PERCENTAGE")
fig.update_traces(marker_color='rgb(255,128,128)', marker_line_color='rgb(255,128,128)',
marker_line_width=1.5, opacity=1)
fig.show()
counts = data['GENDER'].value_counts(sort=True)
labels = counts.index
values = counts.values
o_pie = go.Pie(labels=labels, values=values, name='Overall', hole=0.3, domain={'x': [0, 0.46]})
icounts = idata['GENDER'].value_counts(sort=True)
ilabels = icounts.index
ivalues = icounts.values
i_pie = go.Pie(labels=ilabels, values=ivalues, name='India', hole=0.3, domain={'x': [0.52, 1]})
layout = dict(title='GENDER DISTRIBUTION', font=dict(size=12), legend=dict(orientation='h'), annotations=[dict(x=0.17, y=0.5, text='Overall', showarrow=False, font=dict(size=20)), dict(x=0.8, y=0.5, text='India', showarrow=False, font=dict(size=20))])
fig = dict(data=[o_pie, i_pie], layout=layout)
py.iplot(fig) | 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'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
corrMatrix.tail(10) | 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'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
scatter(y=tr['mean'], color=tr['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.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
def mul_lab_logreg(test, train_X, train_y):
sub = pd.DataFrame(test['sig_id'])
col = train_y.columns.drop('sig_id')
train_X.set_index('sig_id', inplace=True)
df = pd.concat([train_X.iloc[:, 0], train_y.set_index('sig_id')], axis=1)
for c in tqdm(col):
y = df.loc[:, c]
clf = LogisticRegression(random_state=0, class_weight=y.mean(), n_jobs=6).fit(train_X, y)
clf.fit(train_X, y)
sub[c] = clf.predict_proba(test.drop('sig_id', axis=1)).T[1]
return sub
clf = RandomForestClassifier(n_estimators=15, criterion='entropy', max_depth=15, max_samples=150, max_features=0.3, verbose=1, n_jobs=-1, random_state=1998, ccp_alpha=0.0)
clf.fit(train_features.set_index('sig_id'), train_targets_scored.set_index('sig_id'))
preds = clf.predict_proba(test_features.drop('sig_id', axis=1)) | 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.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
def mul_lab_logreg(test, train_X, train_y):
sub = pd.DataFrame(test['sig_id'])
col = train_y.columns.drop('sig_id')
train_X.set_index('sig_id', inplace=True)
df = pd.concat([train_X.iloc[:, 0], train_y.set_index('sig_id')], axis=1)
for c in tqdm(col):
y = df.loc[:, c]
clf = LogisticRegression(random_state=0, class_weight=y.mean(), n_jobs=6).fit(train_X, y)
clf.fit(train_X, y)
sub[c] = clf.predict_proba(test.drop('sig_id', axis=1)).T[1]
return sub
clf = RandomForestClassifier(n_estimators=15, criterion='entropy', max_depth=15, max_samples=150, max_features=0.3, verbose=1, n_jobs=-1, random_state=1998, ccp_alpha=0.0)
clf.fit(train_features.set_index('sig_id'), train_targets_scored.set_index('sig_id'))
preds = clf.predict_proba(test_features.drop('sig_id', axis=1))
sub = pd.DataFrame(test_features['sig_id'])
col = train_targets_scored.columns.drop('sig_id')
col = train_targets_scored.columns.drop('sig_id')
plt.bar(train_targets_scored.mean().index, train_targets_scored.mean()) | 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.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
def mul_lab_logreg(test, train_X, train_y):
sub = pd.DataFrame(test['sig_id'])
col = train_y.columns.drop('sig_id')
train_X.set_index('sig_id', inplace=True)
df = pd.concat([train_X.iloc[:, 0], train_y.set_index('sig_id')], axis=1)
for c in tqdm(col):
y = df.loc[:, c]
clf = LogisticRegression(random_state=0, class_weight=y.mean(), n_jobs=6).fit(train_X, y)
clf.fit(train_X, y)
sub[c] = clf.predict_proba(test.drop('sig_id', axis=1)).T[1]
return sub
clf = RandomForestClassifier(n_estimators=15, criterion='entropy', max_depth=15, max_samples=150, max_features=0.3, verbose=1, n_jobs=-1, random_state=1998, ccp_alpha=0.0)
clf.fit(train_features.set_index('sig_id'), train_targets_scored.set_index('sig_id'))
preds = clf.predict_proba(test_features.drop('sig_id', axis=1))
sub = pd.DataFrame(test_features['sig_id'])
col = train_targets_scored.columns.drop('sig_id')
plt.bar(sub.mean().index, sub.mean()) | 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'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
scatter(y=tr['std'], x=tr['mean'], color=tr['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 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'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
def mul_lab_logreg(test, train_X, train_y):
sub = pd.DataFrame(test['sig_id'])
col = train_y.columns.drop('sig_id')
train_X.set_index('sig_id', inplace=True)
df = pd.concat([train_X.iloc[:, 0], train_y.set_index('sig_id')], axis=1)
for c in tqdm(col):
y = df.loc[:, c]
clf = LogisticRegression(random_state=0, class_weight=y.mean(), n_jobs=6).fit(train_X, y)
clf.fit(train_X, y)
sub[c] = clf.predict_proba(test.drop('sig_id', axis=1)).T[1]
return sub
def score(preds, true_y):
log_preds = np.log(preds + 1e-08)
log_inv_preds = np.log(1 - preds)
total = true_y * log_preds + (1 - true_y) * log_inv_preds
return total.sum().sum() / (preds.shape[0] * preds.shape[1])
clf = RandomForestClassifier(n_estimators=15, criterion='entropy', max_depth=15, max_samples=150, max_features=0.3, verbose=1, n_jobs=-1, random_state=1998, ccp_alpha=0.0)
clf.fit(train_features.set_index('sig_id'), train_targets_scored.set_index('sig_id'))
preds = clf.predict_proba(test_features.drop('sig_id', axis=1))
sub = pd.DataFrame(test_features['sig_id'])
col = train_targets_scored.columns.drop('sig_id')
col = train_targets_scored.columns.drop('sig_id')
for p, c in tqdm(zip(preds, col)):
print(c)
sub[c] = p.T[1] | 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'] = ret_df['index'].apply(lambda x: x.split('-')[0])
ret_df['post'] = ret_df['index'].apply(lambda x: x.split('-')[1])
return ret_df
def scatter(y, x=None, color= None):
fig, ax = plt.subplots()
un_c = color.unique()
for i, col in enumerate(un_c):
y_i = y[color == col]
if type(x) != pd.core.series.Series:
x_i = range(0 + i * (y.shape[0] - y_i.shape[0]),
(1 - i)*y_i.shape[0] + i * y.shape[0])
else:
x_i = x[color == col]
ax.scatter(x_i, y_i, c=col, label=col,
alpha=0.3)
ax.set_ylabel(y.name)
ax.legend()
ax.grid(True)
plt.show()
tr = create_mean_std_df(train_features.iloc[:, 4:])
corrMatrix = train_features.corr()
plt.imshow(corrMatrix.sort_values(by=list(corrMatrix.columns)))
plt.show() | code |
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