path
stringlengths 13
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sequencelengths 1
873
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stringlengths 0
40.4k
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stringclasses 1
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|---|---|---|---|
34130031/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 |
34130031/cell_7 | [
"text_plain_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
train_annotations.keys() | code |
34130031/cell_8 | [
"text_html_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_test_information.json', 'r', errors='ignore') as f:
test_information = json.load(f)
test_information.keys() | code |
34130031/cell_15 | [
"text_plain_output_1.png"
] | import json
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
samp_sub = pd.read_csv('/kaggle/input/iwildcam-2020-fgvc7/sample_submission.csv')
train_annotations.keys()
train_ann = pd.DataFrame(train_annotations['annotations'])
train_cat = pd.DataFrame(train_annotations['categories'])
train_imgs = pd.DataFrame(train_annotations['images'])
train_imgs | code |
34130031/cell_16 | [
"text_plain_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
train_annotations.keys()
train_annotations['info'] | code |
34130031/cell_17 | [
"text_html_output_1.png"
] | import json
import json
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_train_annotations.json', 'r', errors='ignore') as f:
train_annotations = json.load(f)
with open('/kaggle/input/iwildcam-2020-fgvc7/iwildcam2020_test_information.json', 'r', errors='ignore') as f:
test_information = json.load(f)
test_information.keys()
test_information['info'] | code |
316827/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
data.head() | code |
316827/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('comments')
print(compare_means('comments')) | code |
316827/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('msg_len')
print(compare_means('msg_len')) | code |
316827/cell_20 | [
"text_plain_output_1.png"
] | from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
conf_interval('likes') | code |
316827/cell_40 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
shared = data[data.shares > data.shares.quantile(0.98)][data.shares > data.likes * 10][['msg', 'shares']]
top = 10
sorted_data = shared.sort_values(by='shares', ascending=False)[:top]
likes = data[data.likes > data.likes.quantile(0.98)][data.likes > data.shares * 100][['msg', 'likes']]
print('top %d out of %d' % (top, likes.shape[0]))
sorted_data = likes.sort_values(by='likes', ascending=False)[:top]
for i in sorted_data.index.values:
print('likes:', sorted_data.likes[i], '\n', 'message:', sorted_data.msg[i][:300], '\n') | code |
316827/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
sns.pairplot(data, hue='gid') | code |
316827/cell_43 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
shared = data[data.shares > data.shares.quantile(0.98)][data.shares > data.likes * 10][['msg', 'shares']]
top = 10
sorted_data = shared.sort_values(by='shares', ascending=False)[:top]
likes = data[data.likes > data.likes.quantile(0.98)][data.likes > data.shares * 100][['msg', 'likes']]
sorted_data = likes.sort_values(by='likes', ascending=False)[:top]
discussed = data[data.comments > data.comments.quantile(0.98)][['msg', 'comments']]
print('top %d out of %d\n' % (top, discussed.shape[0]))
sorted_data = discussed.sort_values(by='comments', ascending=False)[:top]
for i in sorted_data.index.values:
print('comments:', sorted_data.comments[i], '\n', 'message:', sorted_data.msg[i][:300], '\n') | code |
316827/cell_24 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('likes')
print(compare_means('likes')) | code |
316827/cell_27 | [
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
conf_interval('shares')
print(compare_means('shares')) | code |
316827/cell_37 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import mannwhitneyu
from statsmodels.sandbox.stats.multicomp import multipletests
from statsmodels.stats.weightstats import zconfint
import pandas as pd
posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])
comments = pd.read_csv('../input/comment.csv')
com_count = comments.groupby('pid').count()['cid']
data = posts.join(com_count, on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]
data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']
data['msg_len'] = data.msg.apply(len)
data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})
data.fillna(0, inplace=True)
park = data[data.gid == 1]
town = data[data.gid == 2]
free = data[data.gid == 3]
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
def compare_means(field):
"""
Mann–Whitney test to compare mean values level
"""
mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}
comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])
for i in range(1, 4):
for j in range(1, 4):
if i >= j:
continue
p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]
comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p}, ignore_index=True)
rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, alpha=0.05, method='holm')
comparison['p_value_corrected'] = p_corrected
comparison['rejected'] = rejected
return comparison
shared = data[data.shares > data.shares.quantile(0.98)][data.shares > data.likes * 10][['msg', 'shares']]
top = 10
print('top %d out of %d' % (top, shared.shape[0]))
sorted_data = shared.sort_values(by='shares', ascending=False)[:top]
for i in sorted_data.index.values:
print('shares:', sorted_data.shares[i], '\n', 'message:', sorted_data.msg[i][:200], '\n') | code |
16161648/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass','Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
#
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i,name in enumerate(q.index)]
vals = [name for i,name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend()
for col in ['Pclass', 'SibSp', 'Parch', 'TicketSplitLen', 'Sex', 'CabinPrefix']:
unique_vals = np.array(train[col].unique())
unique_vals.sort()
for unique_value in unique_vals:
for df in [train, test]:
df.loc[df[col] == unique_value, f'{col} {unique_value}'] = 1
df.loc[df[col] != unique_value, f'{col} {unique_value}'] = 0
feature_cols = ['Age', 'Fare', 'Pclass 1', 'Pclass 2', 'Pclass 3', 'SibSp', 'Parch', 'TicketSplitLen 1', 'TicketSplitLen 2', 'TicketSplitLen 3', 'Sex female', 'Sex male', 'CabinPrefix A', 'CabinPrefix B', 'CabinPrefix C', 'CabinPrefix D', 'CabinPrefix E', 'CabinPrefix F', 'CabinPrefix G', 'CabinPrefix None']
from sklearn.model_selection import train_test_split
X = np.array(train[feature_cols])
y = np.array(train.Survived)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train.shape
X_submission = np.array(test[feature_cols])
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)
predictions = lr_model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)
recall = recall_score(y_test, predictions)
f1 = f1_score(y_test, predictions)
parameters = lr_model.coef_
comparison = pd.DataFrame([['LR', accuracy, precision, recall, f1]], columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1'])
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
nn_model = Sequential()
nn_model.add(Dense(20, activation='relu', input_shape=(20,)))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(64, activation='relu'))
nn_model.add(Dense(32, activation='relu'))
nn_model.add(Dropout(0.2, noise_shape=None, seed=None))
nn_model.add(Dense(16, activation='relu'))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(1, activation='sigmoid'))
nn_model.summary()
nn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
results = nn_model.fit(
X_train, y_train,
epochs= 200,
batch_size = 48,
validation_data = (X_test, y_test)
)
f, axes = plt.subplots(1,2, figsize=(10,5))
axes[0].plot(results.history['loss'])
axes[0].plot(results.history['val_loss'])
axes[0].set_title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
axes[0].grid(color='grey')
axes[1].plot(results.history['acc'])
axes[1].plot(results.history['val_acc'])
axes[1].set_title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
axes[1].grid(color='grey')
plt.show()
predictions = nn_model.predict(X_test)
accuracy = accuracy_score(y_test,predictions.round())
precision = precision_score(y_test,predictions.round())
recall = recall_score(y_test,predictions.round())
f1 = f1_score(y_test,predictions.round())
comparison = comparison.append({'Model':'NN', 'Accuracy':accuracy, 'Precision':precision, 'Recall':recall, 'F1':f1}, ignore_index=True)
print(f'Accuracy with NN: {accuracy}')
print(f'Precision with NN: {precision}')
print(f'Recall with NN: {recall}')
print(f'F1 with NN: {f1}')
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
n_estimators = [int(x) for x in np.linspace(start=200, stop=2000, num=10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num=11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap}
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
rf_random = RandomizedSearchCV(estimator=rf_model, param_distributions=random_grid, n_iter=100, cv=3, verbose=2, random_state=42, n_jobs=-1)
rf_random.fit(X_train, y_train)
best_random = rf_random.best_estimator_
predictions = best_random.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)
recall = recall_score(y_test, predictions)
f1 = f1_score(y_test, predictions)
comparison = comparison.append({'Model': 'RF', 'Accuracy': accuracy, 'Precision': precision, 'Recall': recall, 'F1': f1}, ignore_index=True)
print(f'Accuracy with RF: {accuracy}')
print(f'Precision with RF: {precision}')
print(f'Recall with RF: {recall}')
print(f'F1 with RF: {f1}') | code |
16161648/cell_2 | [
"text_plain_output_1.png"
] | import os
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16161648/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass','Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
#
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i,name in enumerate(q.index)]
vals = [name for i,name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend()
print(train.columns)
feature_cols = ['Age', 'Fare', 'Pclass 1', 'Pclass 2', 'Pclass 3', 'SibSp', 'Parch', 'TicketSplitLen 1', 'TicketSplitLen 2', 'TicketSplitLen 3', 'Sex female', 'Sex male', 'CabinPrefix A', 'CabinPrefix B', 'CabinPrefix C', 'CabinPrefix D', 'CabinPrefix E', 'CabinPrefix F', 'CabinPrefix G', 'CabinPrefix None'] | code |
16161648/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass','Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
#
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i,name in enumerate(q.index)]
vals = [name for i,name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend()
for col in ['Pclass', 'SibSp', 'Parch', 'TicketSplitLen', 'Sex', 'CabinPrefix']:
unique_vals = np.array(train[col].unique())
unique_vals.sort()
for unique_value in unique_vals:
for df in [train, test]:
df.loc[df[col] == unique_value, f'{col} {unique_value}'] = 1
df.loc[df[col] != unique_value, f'{col} {unique_value}'] = 0
feature_cols = ['Age', 'Fare', 'Pclass 1', 'Pclass 2', 'Pclass 3', 'SibSp', 'Parch', 'TicketSplitLen 1', 'TicketSplitLen 2', 'TicketSplitLen 3', 'Sex female', 'Sex male', 'CabinPrefix A', 'CabinPrefix B', 'CabinPrefix C', 'CabinPrefix D', 'CabinPrefix E', 'CabinPrefix F', 'CabinPrefix G', 'CabinPrefix None']
from sklearn.model_selection import train_test_split
X = np.array(train[feature_cols])
y = np.array(train.Survived)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train.shape
X_submission = np.array(test[feature_cols])
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
n_estimators = [int(x) for x in np.linspace(start=200, stop=2000, num=10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num=11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, 'min_samples_split': min_samples_split, 'min_samples_leaf': min_samples_leaf, 'bootstrap': bootstrap}
rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
rf_random = RandomizedSearchCV(estimator=rf_model, param_distributions=random_grid, n_iter=100, cv=3, verbose=2, random_state=42, n_jobs=-1)
rf_random.fit(X_train, y_train)
best_random = rf_random.best_estimator_ | code |
16161648/cell_7 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i, name in enumerate(q.index)]
vals = [name for i, name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend() | code |
16161648/cell_16 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
nn_model = Sequential()
nn_model.add(Dense(20, activation='relu', input_shape=(20,)))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(64, activation='relu'))
nn_model.add(Dense(32, activation='relu'))
nn_model.add(Dropout(0.2, noise_shape=None, seed=None))
nn_model.add(Dense(16, activation='relu'))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(1, activation='sigmoid'))
nn_model.summary()
nn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) | code |
16161648/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
16161648/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass','Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
#
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i,name in enumerate(q.index)]
vals = [name for i,name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend()
for col in ['Pclass', 'SibSp', 'Parch', 'TicketSplitLen', 'Sex', 'CabinPrefix']:
unique_vals = np.array(train[col].unique())
unique_vals.sort()
for unique_value in unique_vals:
for df in [train, test]:
df.loc[df[col] == unique_value, f'{col} {unique_value}'] = 1
df.loc[df[col] != unique_value, f'{col} {unique_value}'] = 0
feature_cols = ['Age', 'Fare', 'Pclass 1', 'Pclass 2', 'Pclass 3', 'SibSp', 'Parch', 'TicketSplitLen 1', 'TicketSplitLen 2', 'TicketSplitLen 3', 'Sex female', 'Sex male', 'CabinPrefix A', 'CabinPrefix B', 'CabinPrefix C', 'CabinPrefix D', 'CabinPrefix E', 'CabinPrefix F', 'CabinPrefix G', 'CabinPrefix None']
from sklearn.model_selection import train_test_split
X = np.array(train[feature_cols])
y = np.array(train.Survived)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train.shape
X_submission = np.array(test[feature_cols])
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)
predictions = lr_model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)
recall = recall_score(y_test, predictions)
f1 = f1_score(y_test, predictions)
parameters = lr_model.coef_
comparison = pd.DataFrame([['LR', accuracy, precision, recall, f1]], columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1'])
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
nn_model = Sequential()
nn_model.add(Dense(20, activation='relu', input_shape=(20,)))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(64, activation='relu'))
nn_model.add(Dense(32, activation='relu'))
nn_model.add(Dropout(0.2, noise_shape=None, seed=None))
nn_model.add(Dense(16, activation='relu'))
nn_model.add(Dropout(0.3, noise_shape=None, seed=None))
nn_model.add(Dense(1, activation='sigmoid'))
nn_model.summary()
nn_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
results = nn_model.fit(X_train, y_train, epochs=200, batch_size=48, validation_data=(X_test, y_test))
f, axes = plt.subplots(1, 2, figsize=(10, 5))
axes[0].plot(results.history['loss'])
axes[0].plot(results.history['val_loss'])
axes[0].set_title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
axes[0].grid(color='grey')
axes[1].plot(results.history['acc'])
axes[1].plot(results.history['val_acc'])
axes[1].set_title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
axes[1].grid(color='grey')
plt.show()
predictions = nn_model.predict(X_test)
accuracy = accuracy_score(y_test, predictions.round())
precision = precision_score(y_test, predictions.round())
recall = recall_score(y_test, predictions.round())
f1 = f1_score(y_test, predictions.round())
comparison = comparison.append({'Model': 'NN', 'Accuracy': accuracy, 'Precision': precision, 'Recall': recall, 'F1': f1}, ignore_index=True)
print(f'Accuracy with NN: {accuracy}')
print(f'Precision with NN: {precision}')
print(f'Recall with NN: {recall}')
print(f'F1 with NN: {f1}') | code |
16161648/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
def prepareFeatuers(df):
df['CabinPrefix'] = [str(cabinname)[0] for cabinname in df.Cabin]
df.loc[df.Cabin.isnull(), 'CabinPrefix'] = 'None'
df['CabinKnown'] = [value for value in df.Cabin.isnull()]
df['TicketSplitLen'] = [len(t.split()) for t in df.Ticket]
df['Sex_Ind'] = -1
df.loc[df.Sex == 'female', 'Sex_Ind'] = 1
df.loc[df.Sex == 'male', 'Sex_Ind'] = 2
df['Age'] = df.Age.fillna(0)
df['Fare'] = df.Fare.fillna(0)
return df
train = prepareFeatuers(train)
test = prepareFeatuers(test)
cols = ['Pclass','Sex', 'SibSp', 'Parch', 'Embarked', 'CabinPrefix', 'TicketSplitLen', 'CabinKnown']
for col in cols:
q = train.groupby(col).Survived.sum()
#
t = train.groupby(col).Survived.sum() + train.groupby(col).Survived.count()
fig, ax = plt.subplots()
pos = [i for i,name in enumerate(q.index)]
vals = [name for i,name in enumerate(q.index)]
ax.barh(pos, t, color='r', label='died')
ax.barh(pos, q, label='survived')
ax.set_yticks(pos)
ax.set_yticklabels(vals)
ax.set_ylabel(col)
ax.legend()
for col in ['Pclass', 'SibSp', 'Parch', 'TicketSplitLen', 'Sex', 'CabinPrefix']:
unique_vals = np.array(train[col].unique())
unique_vals.sort()
for unique_value in unique_vals:
for df in [train, test]:
df.loc[df[col] == unique_value, f'{col} {unique_value}'] = 1
df.loc[df[col] != unique_value, f'{col} {unique_value}'] = 0
feature_cols = ['Age', 'Fare', 'Pclass 1', 'Pclass 2', 'Pclass 3', 'SibSp', 'Parch', 'TicketSplitLen 1', 'TicketSplitLen 2', 'TicketSplitLen 3', 'Sex female', 'Sex male', 'CabinPrefix A', 'CabinPrefix B', 'CabinPrefix C', 'CabinPrefix D', 'CabinPrefix E', 'CabinPrefix F', 'CabinPrefix G', 'CabinPrefix None']
from sklearn.model_selection import train_test_split
X = np.array(train[feature_cols])
y = np.array(train.Survived)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train.shape
X_submission = np.array(test[feature_cols])
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)
predictions = lr_model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)
recall = recall_score(y_test, predictions)
f1 = f1_score(y_test, predictions)
parameters = lr_model.coef_
comparison = pd.DataFrame([['LR', accuracy, precision, recall, f1]], columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1'])
print(f'Accuracy with LR: {accuracy}')
print(f'Precision with LR: {precision}')
print(f'Recall with LR: {recall}')
print(f'F1 with LR: {f1}') | code |
122249481/cell_13 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns:
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize=(20, 10))
fig.add_subplot(1, 2, 1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1, 2, 2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1, 0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1, 0])
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1, 0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1, 0]) | code |
122249481/cell_4 | [
"image_output_1.png"
] | df_X = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/training_set_rna.csv', index_col=0).T
df = df_X
df_Y = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/training_set_adt.csv', index_col=0).T
df_X_submission = pd.read_csv('/kaggle/input/machine-learning-challenge-2-prediction/test_set_rna.csv', index_col=0).T
print(df_X.shape, df_Y.shape, df_X_submission.shape)
display(df_X.head(2))
display(df_Y.head(2)) | code |
122249481/cell_20 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns: # 'PTPRC' is name for CD45 gene
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (20,10))
fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1,2,2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1,0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1,0], )
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
for col in ['CD45RA', 'CD45RO']:
n_bins = 50
fig = plt.figure(figsize = (20,4))
fig.add_subplot(1,2,1)
mask = df['PTPRC'] == 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('ZERO RNA condition ' + col)
fig.add_subplot(1,2,2)
mask = df['PTPRC'] != 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('NON-ZERO RNA condition ' + col)
plt.show()
# print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
d2 = pd.DataFrame()
for c in ['CD45RA', 'CD45RO', 'MALAT1', 'NEAT1', 'CD45', 'SNRPD2', 'SNRPE', 'SRSF1', 'SRSF5', 'PTPRC']:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
d2.corr()
sns.clustermap(d2.corr().round(2), annot=True)
l = ['CD45RA', 'CD45RO', 'CD45', 'PTPRC', 'CD53', 'CD4', 'CD28', 'CD37', 'CD81', 'APC', 'SP1', 'CD151', 'MUC1', 'SDS', 'CD69', 'PTPRC', 'CD9', 'LCK', 'CD44', 'UCHL1', 'CD5', 'CD55', 'BCR', 'NHS', 'STAR', 'ZAP70']
d2 = pd.DataFrame()
for c in l:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
d2.corr()
sns.clustermap(d2.corr().round(2), annot=True)
plt.show() | code |
122249481/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
t0start = time.time()
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 |
122249481/cell_19 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns: # 'PTPRC' is name for CD45 gene
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (20,10))
fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1,2,2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1,0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1,0], )
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
for col in ['CD45RA', 'CD45RO']:
n_bins = 50
fig = plt.figure(figsize = (20,4))
fig.add_subplot(1,2,1)
mask = df['PTPRC'] == 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('ZERO RNA condition ' + col)
fig.add_subplot(1,2,2)
mask = df['PTPRC'] != 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('NON-ZERO RNA condition ' + col)
plt.show()
# print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
d2 = pd.DataFrame()
for c in ['CD45RA', 'CD45RO', 'MALAT1', 'NEAT1', 'CD45', 'SNRPD2', 'SNRPE', 'SRSF1', 'SRSF5', 'PTPRC']:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
d2.corr()
sns.clustermap(d2.corr().round(2), annot=True)
l = ['CD45RA', 'CD45RO', 'CD45', 'PTPRC', 'CD53', 'CD4', 'CD28', 'CD37', 'CD81', 'APC', 'SP1', 'CD151', 'MUC1', 'SDS', 'CD69', 'PTPRC', 'CD9', 'LCK', 'CD44', 'UCHL1', 'CD5', 'CD55', 'BCR', 'NHS', 'STAR', 'ZAP70']
d2 = pd.DataFrame()
for c in l:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
else:
print(c, 'absent')
d2.corr() | code |
122249481/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize=(20, 8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA', 'CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c += 1
fig.add_subplot(1, 3, c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print(np.corrcoef(x_loc, y_loc)[0, 1], 'correlation ', col_x, col)
m = (x_loc != 0) & (y_loc != 0)
cc = np.corrcoef(x_loc[m], y_loc[m])[0, 1]
print(cc, 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5:
print('WOW it is big ! ')
plt.show() | code |
122249481/cell_8 | [
"text_plain_output_1.png"
] | df_corr = df.corr()
N = 20
d = df_corr['CD53'].sort_values(ascending=False, key=abs).head(N).to_frame()
for t in df_corr['CD53'].sort_values(ascending=False, key=abs).index[:N]:
m = (df[t] != 0) & (df['CD53'] != 0)
c = np.corrcoef(df[t][m], df['CD53'][m])[0, 1]
d.loc[t, 'Corr non zeros'] = c
d | code |
122249481/cell_16 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns: # 'PTPRC' is name for CD45 gene
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (20,10))
fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1,2,2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1,0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1,0], )
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
d2 = pd.DataFrame()
for c in ['CD45RA', 'CD45RO', 'MALAT1', 'NEAT1', 'CD45', 'SNRPD2', 'SNRPE', 'SRSF1', 'SRSF5', 'PTPRC']:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
else:
print(c, 'absent')
d2.corr() | code |
122249481/cell_17 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns: # 'PTPRC' is name for CD45 gene
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (20,10))
fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1,2,2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1,0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1,0], )
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
for col in ['CD45RA', 'CD45RO']:
n_bins = 50
fig = plt.figure(figsize = (20,4))
fig.add_subplot(1,2,1)
mask = df['PTPRC'] == 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('ZERO RNA condition ' + col)
fig.add_subplot(1,2,2)
mask = df['PTPRC'] != 0
plt.hist(np.clip(df_Y[col][mask],0,2.5), bins = n_bins)
plt.title('NON-ZERO RNA condition ' + col)
plt.show()
# print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
d2 = pd.DataFrame()
for c in ['CD45RA', 'CD45RO', 'MALAT1', 'NEAT1', 'CD45', 'SNRPD2', 'SNRPE', 'SRSF1', 'SRSF5', 'PTPRC']:
if c in df.columns:
d2[c] = df[c]
elif c in df_Y.columns:
d2[c] = df_Y[c]
d2.corr()
sns.clustermap(d2.corr().round(2), annot=True)
plt.show() | code |
122249481/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display( df_Y[['CD45RA','CD45RO', ]].corr() )
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (10,10))
# fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
# fig.add_subplot(1,2,2)
# x_loc = df_Y['CD45RO']
# sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
if 'PTPRC' in df.columns: # 'PTPRC' is name for CD45 gene
y_loc = df['PTPRC']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize = (20,10))
fig.add_subplot(1,2,1)
sns.scatterplot(x=x_loc, y=y_loc)
fig.add_subplot(1,2,2)
x_loc = df_Y['CD45RO']
sns.scatterplot(x=x_loc, y=y_loc)
plt.show()
print('Pearson correlations CD45RA, CD45RO:')
print(np.corrcoef(df['PTPRC'], df_Y['CD45RA'])[1,0], np.corrcoef(df['PTPRC'], df_Y['CD45RO'])[1,0], )
print('Pearson correlations excluding zero rna values CD45RA, CD45RO:')
mask = df['PTPRC'] != 0
print(np.corrcoef(df['PTPRC'][mask], df_Y['CD45RA'][mask])[1,0], np.corrcoef(df['PTPRC'][mask], df_Y['CD45RO'][mask])[1,0], )
for col in ['CD45RA', 'CD45RO']:
n_bins = 50
fig = plt.figure(figsize=(20, 4))
fig.add_subplot(1, 2, 1)
mask = df['PTPRC'] == 0
plt.hist(np.clip(df_Y[col][mask], 0, 2.5), bins=n_bins)
plt.title('ZERO RNA condition ' + col)
fig.add_subplot(1, 2, 2)
mask = df['PTPRC'] != 0
plt.hist(np.clip(df_Y[col][mask], 0, 2.5), bins=n_bins)
plt.title('NON-ZERO RNA condition ' + col)
plt.show() | code |
122249481/cell_10 | [
"text_plain_output_1.png"
] | import umap
reducer = umap.UMAP(random_state=42)
r = reducer.fit_transform(df)
dict_reds = {}
dict_reds['umap'] = r
n_x_subplots = 2
c = 0
str_data_inf = 'CITEseq2302'
l = ['CD45RA', 'CD45RO', 'PTPRC', 'CD53', 'MALAT1', 'NEAT1', 'CD3', 'CD4', 'CD69']
for gene in l[:40]:
if gene in df.columns:
v4color = df[gene]
elif gene in df_Y.columns:
v4color = df_Y[gene]
else:
continue
v4color = np.clip(v4color, np.percentile(v4color, 5), np.percentile(v4color, 95))
if c % n_x_subplots == 0:
if c > 0:
plt.show()
fig = plt.figure(figsize=(20, 4))
c = 0
plt.suptitle(' UMAP ' + str_data_inf + ' n_samples=' + str(len(df)), fontsize=12)
c += 1
fig.add_subplot(1, n_x_subplots, c)
sns.scatterplot(x=r[:, 0], y=r[:, 1], hue=v4color, palette='rainbow') | code |
122249481/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import numpy as np # linear algebra
import seaborn as sns
fig = plt.figure(figsize = (20,8))
col_x = 'CD53'
x_loc = df[col_x]
c = 0
for col in ['CD45RA','CD45RO', 'PTPRC']:
if col in df_Y.columns:
y_loc = df_Y[col]
else:
y_loc = df[col]
c+=1; fig.add_subplot(1,3,c)
sns.scatterplot(x=x_loc, y=y_loc)
plt.xlabel(col_x)
plt.ylabel(col)
print( np.corrcoef(x_loc, y_loc)[0,1], 'correlation ', col_x, col)
m = (x_loc !=0 ) &( y_loc != 0 )
cc = np.corrcoef(x_loc[m], y_loc[m])[0,1]
print(cc , 'correlation excluding zeros ', col_x, col)
if np.abs(cc) > 0.5: print('WOW it is big ! ')
plt.show()
display(df_Y[['CD45RA', 'CD45RO']].corr())
y_loc = df_Y['CD45RO']
x_loc = df_Y['CD45RA']
fig = plt.figure(figsize=(10, 10))
sns.scatterplot(x=x_loc, y=y_loc)
plt.show() | code |
122249481/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from scipy import stats
d1_corr = pd.DataFrame(index=df.columns)
res = stats.pearsonr([1, 2, 3, 4, 5], [10, 9, 2.5, 6, 4])
col1 = 'CD45RA'
for col in df.columns:
v0 = df[col]
v1 = df_Y[col1]
res = stats.pearsonr(v0, v1)
d1_corr.loc[col, 'Corr ' + col1] = res[0]
d1_corr.loc[col, 'pvalue ' + col1] = res[1]
d1_corr.to_csv()
m = d1_corr[d1_corr.columns[1]] < 0.05 / len(d1_corr)
print(m.sum())
display(d1_corr[m].sort_values(d1_corr.columns[0], ascending=False, key=abs))
d1_corr.sort_values(d1_corr.columns[0], ascending=False, key=abs) | code |
16125229/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
df.groupby(by='state')['user_id'].count().sort_values(ascending=False) | code |
16125229/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
state.columns | code |
16125229/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
df.groupby(by='state')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
corrmat = df.corr()
sns.set(font_scale=1)
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(corrmat, vmax=1, vmin=-1, square=True, annot=True) | code |
16125229/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
df.groupby(by='state')['user_id'].count().sort_values(ascending=False)
plt.figure(figsize=(15, 10))
df.groupby(by='state')['user_id'].count().sort_values(ascending=False).plot.bar()
plt.xticks(rotation=50)
plt.xlabel('Estados')
plt.ylabel('Número de plays')
plt.show() | code |
16125229/cell_6 | [
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns | code |
16125229/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
df.groupby(by='state')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
corrmat = df.corr()
sns.set(font_scale=1)
fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(corrmat, vmax=1, vmin=-1, square=True, annot=True);
fig, ax = plt.subplots(figsize=(10,10))
ax.scatter(df['duration'], df['value'])
ax.set_title('Music Dataset')
ax.set_xlabel('Duration')
ax.set_ylabel('Value')
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(df['duration'], df['plays'])
ax.set_title('Music Dataset')
ax.set_xlabel('Duration')
ax.set_ylabel('Plays') | code |
16125229/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
hits.head() | code |
16125229/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.figure(figsize=(15, 10))
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False).plot.bar()
plt.xticks(rotation=50)
plt.xlabel('Gênero')
plt.ylabel('Número de plays')
plt.show() | code |
16125229/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
musics.columns | code |
16125229/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
genre.columns | code |
16125229/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.head() | code |
16125229/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878') | code |
16125229/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False) | code |
16125229/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns | code |
16125229/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
musics.columns
musics.head() | code |
16125229/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
hits = pd.read_csv('../input/hits.csv')
musics = pd.read_csv('../input/music_data.csv')
genre = pd.read_csv('../input/genre.csv')
state = pd.read_csv('../input/state.csv')
hits.columns
musics.columns
genre.columns
state.columns
df = hits.merge(genre, on='genre_id').merge(state, on='state_id').merge(musics, on='music_id')
df.columns
df.query('user_id==878')
df.groupby(by='genre')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
df.groupby(by='state')['user_id'].count().sort_values(ascending=False)
plt.xticks(rotation=50)
corrmat = df.corr()
sns.set(font_scale=1)
fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(corrmat, vmax=1, vmin=-1, square=True, annot=True);
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(df['duration'], df['value'])
ax.set_title('Music Dataset')
ax.set_xlabel('Duration')
ax.set_ylabel('Value') | code |
90118469/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
import random
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda.predict(X_attack)
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, Y_attack)
X_attack1 = X_attack.reshape(X_attack.shape[0], 28, 28)
import random
for i in range(0, 10):
s = random.randint(0, X_attack.shape[0])
print(s)
plt.imshow(X_attack1[s])
plt.show() | code |
90118469/cell_13 | [
"text_plain_output_1.png"
] | X_test_final = X_test.to_numpy(dtype='uint8')
print(X_test_final) | code |
90118469/cell_9 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape | code |
90118469/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
disp.figure_.suptitle('Confusion Matrix')
print(f'Confusion matrix:\n{disp.confusion_matrix}')
plt.show() | code |
90118469/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda.predict(X_attack)
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, Y_attack)
disp.figure_.suptitle('Confusion Matrix')
print(f'Confusion matrix:\n{disp.confusion_matrix}')
plt.show() | code |
90118469/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
print(accuracy_score(y_test, y_pred))
print(y_pred.shape) | code |
90118469/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_pred)
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda.predict(X_attack)
print(f'Classification report for classifier {lda}:\n{metrics.classification_report(y_test, Y_attack)}\n') | code |
90118469/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape | code |
90118469/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
print(X_attack.shape) | code |
90118469/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda.predict(X_attack) | code |
90118469/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
mnist_train = pd.read_csv('../input/mnist-in-csv/mnist_train.csv')
mnist_train.head() | code |
90118469/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
X_test_final = X_test.to_numpy(dtype='uint8')
X_attack = X_test_final - (X_test_final @ np.transpose(w) + w0) @ w / np.linalg.norm(w)
Y_attack = lda.predict(X_attack)
print(accuracy_score(y_test, Y_attack))
print(Y_attack.shape) | code |
90118469/cell_10 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import accuracy_score
import numpy as np
lda = LDA(n_components=1)
X_train_r2 = lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)
w = lda.coef_
w.shape
w0 = lda.intercept_
np.transpose(w0).shape
print(f'Classification report for classifier {lda}:\n{metrics.classification_report(y_test, y_pred)}\n') | code |
17121374/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
print(test_df.shape)
test_df.describe() | code |
17121374/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.head(10) | code |
17121374/cell_20 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df.isnull().sum() / 1460 * 100).iloc[0:50]
(test_df.isnull().sum() / 1460 * 100).iloc[50:82]
test_df.drop(['Id', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu', 'Alley'], axis=1).head()
train_df.drop(['Id', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu', 'Alley'], axis=1).head()
num_attributes = train_df[['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'SalePrice']]
corr = num_attributes.corr()
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(corr, mask=mask, cmap=cmap, vmin=-0.4, vmax=0.4, center=0, square=True, linewidths=0.5, cbar_kws={'shrink': 0.5}) | code |
17121374/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns | code |
17121374/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df.isnull().sum() / 1460 * 100).iloc[0:50]
(test_df.isnull().sum() / 1460 * 100).iloc[50:82]
test_df.drop(['Id', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu', 'Alley'], axis=1).head()
train_df.drop(['Id', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu', 'Alley'], axis=1).head() | code |
17121374/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input')) | code |
17121374/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes | code |
17121374/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
(train_df.isnull().sum() / 1460 * 100).iloc[0:50] | code |
17121374/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
(test_df.isnull().sum() / 1460 * 100).iloc[50:82] | code |
17121374/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
train_df.columns
train_df.dtypes
print(train_df.shape)
train_df.describe() | code |
17121374/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sub_df = pd.read_csv('../input/sample_submission.csv')
test_df.head(10) | code |
48163942/cell_42 | [
"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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
fig = plt.figure(figsize=(15, 8))
most_freq_category = train.groupby('Product_Category')['Selling_Price'].sum().reset_index()
sns.barplot(x='Product_Category', y='Selling_Price', data=most_freq_category, palette='muted') | code |
48163942/cell_13 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
print(train.columns) | code |
48163942/cell_34 | [
"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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
sns.distplot(train['Selling_Price']) | code |
48163942/cell_30 | [
"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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
sns.distplot(train['charges_2 (%)']) | code |
48163942/cell_44 | [
"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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
#Function to analye how Purchase amount is dependent upon Product categories.
fig = plt.figure(figsize=(15,8))
most_freq_category = train.groupby('Product_Category')['Selling_Price'].sum().reset_index()
sns.barplot(x='Product_Category',y='Selling_Price',data = most_freq_category,palette="muted")
train['Loyalty_customer'].value_counts() | code |
48163942/cell_39 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
train.info() | code |
48163942/cell_26 | [
"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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
sns.distplot(train['charges_1']) | code |
48163942/cell_41 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
train['Product_Category'].nunique() | code |
48163942/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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
test.head() | code |
48163942/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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train.describe() | code |
48163942/cell_17 | [
"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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
def showMissingValues(dataset):
for col in dataset.columns.tolist():
print(f' {col} column missing values: {dataset[col].isnull().sum()}')
print('\n')
print('Train data-------------------------------------')
showMissingValues(train)
print('Validation dataset--------------------------------------')
showMissingValues(test) | code |
48163942/cell_35 | [
"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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape | code |
48163942/cell_43 | [
"text_plain_output_1.png",
"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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
#Function to analye how Purchase amount is dependent upon Product categories.
fig = plt.figure(figsize=(15,8))
most_freq_category = train.groupby('Product_Category')['Selling_Price'].sum().reset_index()
sns.barplot(x='Product_Category',y='Selling_Price',data = most_freq_category,palette="muted")
train['Loyalty_customer'].nunique() | code |
48163942/cell_46 | [
"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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
#Function to analye how Purchase amount is dependent upon Product categories.
fig = plt.figure(figsize=(15,8))
most_freq_category = train.groupby('Product_Category')['Selling_Price'].sum().reset_index()
sns.barplot(x='Product_Category',y='Selling_Price',data = most_freq_category,palette="muted")
train['Customer_name'].nunique() | code |
48163942/cell_14 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train.info() | code |
48163942/cell_22 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train[train['Customer_name'] == 'Missing'].head() | code |
48163942/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)
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
train.head() | code |
48163942/cell_37 | [
"text_plain_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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
def showMissingValues(dataset):
pass
train = train.rename(columns={'charges_2 (%)': 'charges_2'})
test = test.rename(columns={'charges_2 (%)': 'charges_2'})
train['charges_2'] = train['charges_2'].fillna(train['charges_1'].median())
test['charges_2'] = test['charges_2'].fillna(test['charges_1'].median())
train = train[~train['Selling_Price'].isna()]
train.shape
def showMissingValues(dataset):
for col in dataset.columns.tolist():
print(f' {col} column missing values: {dataset[col].isnull().sum()}')
print('\n')
print('Train data-------------------------------------')
showMissingValues(train)
print('Test dataset--------------------------------------')
showMissingValues(test) | code |
48163942/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
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
sns.set()
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.feature_selection import VarianceThreshold
from scipy.stats import norm
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeRegressor
from xgboost.sklearn import XGBRegressor
from sklearn.model_selection import GridSearchCV, cross_val_score, learning_curve, KFold, StratifiedKFold
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import warnings
warnings.filterwarnings('ignore')
train_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/train.csv')
test_original = pd.read_csv('../input/hackerearth-carnival-wars-challenge/test.csv')
sample_submission = pd.read_csv('../input/hackerearth-carnival-wars-challenge/sample_submission.csv')
train = train_original.copy()
test = test_original.copy()
print(f'Train Datset shape : {train.shape}')
print(f'Test Datset shape : {test.shape}') | code |
48163942/cell_5 | [
"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 |
33118743/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_shape = []
for ex in tqdm(os.listdir(evaluation_path)):
with open(evaluation_path + ex, 'r') as train_file:
all_im = json.load(train_file)
im_in = np.array(all_im['train'][0]['input'])
im_out = np.array(all_im['train'][0]['output'])
if im_in.shape == im_out.shape:
same_shape.append(ex)
def get_im_with_same_ioshape(file_path, name, show=False, mode='train'):
train = []
test = []
with open(file_path + name, 'r') as train_file:
all_im = json.load(train_file)
im_in = np.array(all_im['train'][0]['input'])
im_out = np.array(all_im['train'][0]['output'])
if im_in.shape != im_out.shape:
return None
for im in all_im['train']:
im_in = np.array(im['input'])
im_out = np.array(im['output'])
mask = np.asarray(np.nan_to_num((im_in - im_out) / (im_in - im_out), 0), 'int8')
train.append((im_in, im_out, mask))
if mode == 'train':
for im in all_im['test']:
im_in = np.array(im['input'])
im_out = np.array(im['output'])
test.append((im_in, im_out))
if mode == 'test':
for im in all_im['test']:
im_in = np.array(im['input'])
test.append(im_in)
return (train, test)
train, test = get_im_with_same_ioshape(evaluation_path, same_shape[1], False)
def get_features(input_):
im_in, im_out, mask = input_
features = np.zeros((sum(sum(mask)), 8))
colors = np.zeros(sum(sum(mask)))
f = 0
for y in range(mask.shape[0]):
for x in range(mask.shape[1]):
if mask[y, x] == 1:
pix_exp = np.zeros(8)
n_p = 0
for dy in range(-1, 2):
for dx in range(-1, 2):
if dy != 0 or dx != 0:
if dx + x >= 0 and dy + y >= 0 and (dx + x < mask.shape[1]) and (dy + y < mask.shape[0]):
pix_exp[n_p] = im_in[y + dy, x + dx]
else:
pix_exp[n_p] = -1
n_p += 1
features[f] = pix_exp
colors[f] = im_out[y, x]
f += 1
return (features, colors)
def get_cf(train):
features_set = []
colors_set = []
for in_out_mask in train:
features, colors = get_features(in_out_mask)
features_set += list(features)
colors_set += list(colors)
features_set_min = np.unique(np.array(features_set), axis=0)
colors_min = np.zeros(len(features_set_min))
for n, feature in enumerate(features_set):
if feature in features_set_min:
for i, feature_uniq in enumerate(features_set_min):
if str(feature_uniq) == str(feature):
break
colors_min[i] = colors_set[n]
return (colors_min, features_set_min)
colors_min, features_set_min = get_cf(train)
def make_pred(im_in, features, colors):
im_out = im_in.copy()
f = 0
for y in range(im_in.shape[0]):
for x in range(im_in.shape[1]):
pix_exp = np.zeros(8)
n_p = 0
for dy in range(-1, 2):
for dx in range(-1, 2):
if dy != 0 or dx != 0:
if dx + x >= 0 and dy + y >= 0 and (dx + x < im_in.shape[1]) and (dy + y < im_in.shape[0]):
pix_exp[n_p] = im_in[y + dy, x + dx]
else:
pix_exp[n_p] = -1
n_p += 1
for n, f in enumerate(features):
if str(f) == str(pix_exp):
im_out[y, x] = colors[n]
return im_out
pred = make_pred(test[0][0], features_set_min, colors_min)
print(test[0][0])
print(pred)
print(test[0][1])
print(np.sum(np.sum(np.where(np.nan_to_num((pred - test[0][1]) / (pred - test[0][1]), 0) != 0, 1, 0)))) | code |
33118743/cell_4 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import json
import numpy as np # linear algebra
import os
train_path = '/kaggle/input/abstraction-and-reasoning-challenge/training/'
evaluation_path = '/kaggle/input/abstraction-and-reasoning-challenge/evaluation/'
test_path = '/kaggle/input/abstraction-and-reasoning-challenge/test/'
same_shape = []
for ex in tqdm(os.listdir(evaluation_path)):
with open(evaluation_path + ex, 'r') as train_file:
all_im = json.load(train_file)
im_in = np.array(all_im['train'][0]['input'])
im_out = np.array(all_im['train'][0]['output'])
if im_in.shape == im_out.shape:
same_shape.append(ex)
def get_im_with_same_ioshape(file_path, name, show=False, mode='train'):
train = []
test = []
with open(file_path + name, 'r') as train_file:
all_im = json.load(train_file)
im_in = np.array(all_im['train'][0]['input'])
im_out = np.array(all_im['train'][0]['output'])
if im_in.shape != im_out.shape:
return None
for im in all_im['train']:
im_in = np.array(im['input'])
im_out = np.array(im['output'])
mask = np.asarray(np.nan_to_num((im_in - im_out) / (im_in - im_out), 0), 'int8')
train.append((im_in, im_out, mask))
if show:
print('NAME:\n', same_shape[N])
print('IN:\n', im_in)
print('OUT:\n', im_out)
print('MASK:\n', mask)
if mode == 'train':
for im in all_im['test']:
im_in = np.array(im['input'])
im_out = np.array(im['output'])
test.append((im_in, im_out))
if mode == 'test':
for im in all_im['test']:
im_in = np.array(im['input'])
test.append(im_in)
return (train, test)
train, test = get_im_with_same_ioshape(evaluation_path, same_shape[1], False) | code |
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