path
stringlengths 13
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|---|---|---|---|
130013718/cell_12 | [
"text_html_output_1.png"
] | from PIL import Image
import numpy as np
import numpy as np # linear algebra
import numpy as np # linear algebra
import os
import os
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 tensorflow as tf
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]
sol_train = pd.DataFrame()
sol_train = sol_train.assign(filename=Id)
sol_train['label'] = sol_train['filename']
sol_train['label'] = sol_train['label'].str.replace('/kaggle/input/cassava-disease-classification/train/', '')
sol_train['label'] = sol_train['label'].str.split('/').str[0]
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/test'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]
sol_test = pd.DataFrame()
sol_test = sol_test.assign(filename=Id)
sol_test['label'] = sol_test['filename']
sol_test['label'] = sol_test['label'].str.replace('/kaggle/input/cassava-disease-classification/test/', '')
sol_test['label'] = sol_test['label'].str.split('/').str[0]
import tensorflow as tf
import numpy as np
from PIL import Image
model = tf.saved_model.load('/kaggle/input/efficientnet-cassava-disease-classification/EfficientNet')
classes = ['Cassava Bacterial Blight (CBB)', 'Cassava Brown Streak Disease (CBSD)', 'Cassava Green Mottle (CGM)', 'Cassava Mosaic Disease (CMD)', 'Healthy']
result = []
for i in sol_test.filename:
img = Image.open(i).convert('RGB')
img = img.resize((300, 300 * img.size[1] // img.size[0]), Image.ANTIALIAS)
inp_numpy = np.array(img)[None]
inp = tf.constant(inp_numpy, dtype='float32')
class_scores = model(inp)[0].numpy()
result.append(classes[class_scores.argmax()])
result[:5]
result = []
for i in sol_train.filename:
img = Image.open(i).convert('RGB')
img = img.resize((300, 300 * img.size[1] // img.size[0]), Image.ANTIALIAS)
inp_numpy = np.array(img)[None]
inp = tf.constant(inp_numpy, dtype='float32')
class_scores = model(inp)[0].numpy()
result.append(classes[class_scores.argmax()])
result[:5] | code |
130013718/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import os
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/train'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5]
Id = []
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input/cassava-disease-classification/test'):
for filename in filenames:
Id.append(os.path.join(dirname, filename))
Id[:5] | code |
2001102/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train_df = train_df.rename(columns={'train_id': 'id'})
train_df.head() | code |
2001102/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train = pd.read_table('../input/train.tsv')
train_df = train_df.rename(columns={'train_id': 'id'})
test_df = test_df.rename(columns={'test_id': 'id'})
train_test_combine = pd.concat([train_df.drop(['price'], axis=1), test_df], axis=0)
train_test_combine.category_name = train_test_combine.category_name.astype('category')
train_test_combine.item_description = train_test_combine.item_description.astype('category')
train_test_combine.name = train_test_combine.name.astype('category')
train_test_combine.brand_name = train_test_combine.brand_name.astype('category')
train_test_combine.name = train_test_combine.name.cat.codes
train_test_combine.brand_name = train_test_combine.brand_name.cat.codes
train_test_combine.item_description = train_test_combine.item_description.cat.codes
train_test_combine.category_name = train_test_combine.category_name.cat.codes
train_test_combine = train_test_combine.drop(['brand_name'], axis=1)
train_df = train_test_combine.loc[train_test_combine['is_train'] == 1]
test_df = train_test_combine.loc[train_test_combine['is_train'] == 0]
train_df = train_df.drop(['is_train'], axis=1)
test_df = test_df.drop(['is_train'], axis=1)
train_df.head() | code |
2001102/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train_df = train_df.rename(columns={'train_id': 'id'})
plt.figure(figsize=(20, 15))
plt.hist(train_df['price'], bins=50, range=[0, 300], label='price')
plt.xlabel('Price')
plt.ylabel('Sample')
plt.title('Sale Price Distribution')
plt.show() | code |
2001102/cell_18 | [
"image_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train = pd.read_table('../input/train.tsv')
train_df = train_df.rename(columns={'train_id': 'id'})
test_df = test_df.rename(columns={'test_id': 'id'})
train_test_combine = pd.concat([train_df.drop(['price'], axis=1), test_df], axis=0)
train_test_combine.category_name = train_test_combine.category_name.astype('category')
train_test_combine.item_description = train_test_combine.item_description.astype('category')
train_test_combine.name = train_test_combine.name.astype('category')
train_test_combine.brand_name = train_test_combine.brand_name.astype('category')
train_test_combine.name = train_test_combine.name.cat.codes
train_test_combine.brand_name = train_test_combine.brand_name.cat.codes
train_test_combine.item_description = train_test_combine.item_description.cat.codes
train_test_combine.category_name = train_test_combine.category_name.cat.codes
train_test_combine.head() | code |
2001102/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train_df = train_df.rename(columns={'train_id': 'id'})
train_df['price'].describe() | code |
2001102/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
random_forest_model = RandomForestRegressor(n_jobs=-1, min_samples_leaf=3, n_estimators=200)
random_forest_model.fit(features_rdf, target_rdf)
random_forest_model.score(features_rdf, target_rdf) | code |
2001102/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train = pd.read_table('../input/train.tsv')
train_df = train_df.rename(columns={'train_id': 'id'})
test_df = test_df.rename(columns={'test_id': 'id'})
train_test_combine = pd.concat([train_df.drop(['price'], axis=1), test_df], axis=0)
train_test_combine.head() | code |
2001102/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train_df = train_df.rename(columns={'train_id': 'id'})
print(train_df.isnull().sum(), train_df.isnull().sum() / train_df.shape[0] * 100) | code |
2001102/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
train = pd.read_table('../input/train.tsv')
train_df = train_df.rename(columns={'train_id': 'id'})
test_df = test_df.rename(columns={'test_id': 'id'})
train_test_combine = pd.concat([train_df.drop(['price'], axis=1), test_df], axis=0)
train_test_combine.category_name = train_test_combine.category_name.astype('category')
train_test_combine.item_description = train_test_combine.item_description.astype('category')
train_test_combine.name = train_test_combine.name.astype('category')
train_test_combine.brand_name = train_test_combine.brand_name.astype('category')
train_test_combine.name = train_test_combine.name.cat.codes
train_test_combine.brand_name = train_test_combine.brand_name.cat.codes
train_test_combine.item_description = train_test_combine.item_description.cat.codes
train_test_combine.category_name = train_test_combine.category_name.cat.codes
train_test_combine = train_test_combine.drop(['brand_name'], axis=1)
train_df = train_test_combine.loc[train_test_combine['is_train'] == 1]
test_df = train_test_combine.loc[train_test_combine['is_train'] == 0]
train_df = train_df.drop(['is_train'], axis=1)
test_df = test_df.drop(['is_train'], axis=1)
test_df.head() | code |
2001102/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
types_dict_train = {'train_id': 'int64', 'item_condition_id': 'int8', 'price': 'float64', 'shipping': 'int8'}
train_df = pd.read_csv('../input/train.tsv', delimiter='\t', low_memory=True, dtype=types_dict_train)
types_dict_test = {'test_id': 'int64', 'item_condition_id': 'int8', 'shipping': 'int8'}
test_df = pd.read_csv('../input/test.tsv', delimiter='\t', low_memory=True, dtype=types_dict_test)
test_df = test_df.rename(columns={'test_id': 'id'})
test_df.head() | code |
1006233/cell_4 | [
"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')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
processed_features.head(n=20) | code |
1006233/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
classifiers = [('Linear SVC', LinearSVC()), ('Decision Tree', DecisionTreeClassifier()), ('Multinomial NB', MultinomialNB())]
random_score = float(max(n_survived, n_died)) / float(n_samples)
for title, clf in classifiers:
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
scores = []
for max_depth in range(1, 10):
clf = DecisionTreeClassifier(max_depth=max_depth)
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
print('Max depth of {}: {:.4f}'.format(max_depth, score))
scores.append((max_depth, score)) | code |
1006233/cell_2 | [
"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')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
print('Number of training samples: {}'.format(n_samples))
print('Number of features: {}'.format(n_features))
print('Number of survivors: {}'.format(n_survived))
print('Number of deaths: {}'.format(n_died)) | code |
1006233/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006233/cell_3 | [
"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')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
features.head(n=20) | code |
1006233/cell_5 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train_df = train_df[pd.isnull(train_df['Age']) == False]
features = train_df.drop(['PassengerId', 'Survived', 'Name', 'Ticket'], axis=1)
labels = train_df['Survived']
n_samples = len(train_df)
n_features = len(features.columns)
n_survived = labels.value_counts()[1]
n_died = labels.value_counts()[0]
processed_features = pd.DataFrame(index=features.index)
for col, col_data in features.iteritems():
if col == 'Sex':
col_data = col_data.replace(['male', 'female'], [1, 0])
if col == 'Embarked' or col == 'Cabin':
col_data = pd.get_dummies(col_data, prefix=col)
processed_features = processed_features.join(col_data)
from sklearn.linear_model import LogisticRegressionCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import LinearSVC
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import cross_val_score
classifiers = [('Linear SVC', LinearSVC()), ('Decision Tree', DecisionTreeClassifier()), ('Multinomial NB', MultinomialNB())]
random_score = float(max(n_survived, n_died)) / float(n_samples)
print('Random score: {:.4f}'.format(random_score))
for title, clf in classifiers:
score = np.mean(cross_val_score(clf, processed_features, labels, cv=5))
print('{} score: {:.4f}'.format(title, score)) | code |
1008540/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Activation, Flatten, Dense
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from subprocess import check_output
print('Files in Input Directory:')
print(check_output(['ls', '../input']).decode('utf8')) | code |
18104686/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survived'].sum().reset_index()
Survials_By_Age_Segment = []
age_difference = 5
max_age = 70
for i in range(max_age // age_difference):
s = 0
for j in range(age_difference):
s = s + Survials_By_Age.loc[[i * age_difference + j, 'Age'], 'Survived'][0]
Survials_By_Age_Segment.append(s)
Survials_By_Age_Segment = pd.Series(Survials_By_Age_Segment, index=list(range(0, max_age, age_difference)))
sns.barplot(y=Survials_By_Age_Segment, x=Survials_By_Age_Segment.index)
print(Survials_By_Age_Segment) | code |
18104686/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
18104686/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survived'].sum().reset_index()
Survials_By_Age_Segment = []
age_difference = 5
max_age = 70
for i in range(max_age // age_difference):
s = 0
for j in range(age_difference):
s = s + Survials_By_Age.loc[[i * age_difference + j, 'Age'], 'Survived'][0]
Survials_By_Age_Segment.append(s)
Survials_By_Age_Segment = pd.Series(Survials_By_Age_Segment, index=list(range(0, max_age, age_difference)))
boolean_Survivals = train_data['Survived'] == 1
Survivals = train_data[boolean_Survivals]
sns.barplot(y='title', x='average_rating', data=ayu) | code |
130008236/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True)
df.rate = df.rate.astype('float')
type(df.rate[0])
X = df.groupby(['location']).count()
X.loc[X['name'] == X['name'].max()].index.tolist()
X = df.groupby(['location']).mean()
A = X.sort_values(by=['rate'], ascending=False)
B = X.sort_values(by=['rate'])
a = A.iloc[0:5]
b = B.iloc[0:5]
b['rate'].plot(kind='bar') | code |
130008236/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True) | code |
130008236/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique() | code |
130008236/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].unique() | code |
130008236/cell_2 | [
"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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df.head() | code |
130008236/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique() | code |
130008236/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True)
df.rate = df.rate.astype('float')
type(df.rate[0])
X = df.groupby(['location']).count()
X.loc[X['name'] == X['name'].max()].index.tolist()
X = df.groupby(['location']).mean()
A = X.sort_values(by=['rate'], ascending=False)
B = X.sort_values(by=['rate'])
a = A.iloc[0:5]
b = B.iloc[0:5]
a['rate'].plot(kind='bar') | code |
130008236/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 |
130008236/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique() | code |
130008236/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True)
df.rate = df.rate.astype('float')
type(df.rate[0])
X = df.groupby(['location']).count()
X.loc[X['name'] == X['name'].max()].index.tolist() | code |
130008236/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df['rate'] = [re.sub('\\/\\d', '', i) for i in df['rate']]
df | code |
130008236/cell_16 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True)
df.rate = df.rate.astype('float')
type(df.rate[0])
sns.barplot(x='book_table', y='rate', data=df) | code |
130008236/cell_3 | [
"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 pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df.head() | code |
130008236/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test
df['cuisines'] = df['cuisines'].str.split(',')
df = df.explode('cuisines')
df
df['rest_type'] = df['rest_type'].str.split(',')
df = df.explode('rest_type')
df.reset_index(drop=True)
sns.countplot(x='online_order', data=df) | code |
130008236/cell_10 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import re
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df['rate'] = [re.sub('\\/\\d', '', i) for i in df['rate']]
df
df.cost.unique()
df['cost'] = [re.sub(',', '', i) for i in df['cost']]
df | code |
130008236/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum()
df['rate'].replace('NEW', np.nan, inplace=True)
df['rate'].replace('-', np.nan, inplace=True)
df = df.dropna()
df['rate'].unique()
df.cost.unique()
df.cost.unique()
test = df.iloc[[2]].copy()
test['cuisines'] = test['cuisines'].astype(str)
test | code |
130008236/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
import numpy as np
import re
import seaborn as sns
df = pd.read_csv('/kaggle/input/zomato-eda/zomato.csv').copy()
df = df.rename(columns={'listed_in(city)': 'city', 'listed_in(type)': 'type', 'approx_cost(for two people)': 'cost'})
df = df.drop(columns=['url', 'address', 'phone', 'dish_liked', 'reviews_list', 'menu_item'])
df = df.dropna()
df.isnull().sum() | code |
88079729/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 |
90146618/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
def extract_face(filename, required_size=(160, 160)):
image = Image.open(filename)
image = image.convert('RGB')
pixels = np.asarray(image)
image = Image.fromarray(pixels)
image = image.resize(required_size)
face_array = np.asarray(image)
return face_array
def load_face(dir):
faces = list()
for filename in os.listdir(dir):
path = dir + filename
face = extract_face(path)
faces.append(face)
return faces
def load_dataset(dir):
X, y = (list(), list())
for subdir in os.listdir(dir):
path = dir + subdir + '/'
faces = load_face(path)
labels = [subdir for i in range(len(faces))]
X.extend(faces)
y.extend(labels)
return (np.asarray(X), np.asarray(y))
def get_embedding(face_pixels):
face_pixels = face_pixels.astype('float32')
mean, std = (face_pixels.mean(), face_pixels.std())
face_pixels = (face_pixels - mean) / std
samples = np.expand_dims(face_pixels, axis=0)
yhat = model.predict(samples)
return yhat[0]
data = np.load('../input/new-masked-face/extracted_masked_unmasked.npz')
trainX, trainy, testX, testy = (data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3'])
print(trainX.shape) | code |
90146618/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
print(os.listdir('/')) | code |
90146618/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
def extract_face(filename, required_size=(160, 160)):
image = Image.open(filename)
image = image.convert('RGB')
pixels = np.asarray(image)
image = Image.fromarray(pixels)
image = image.resize(required_size)
face_array = np.asarray(image)
return face_array
def load_face(dir):
faces = list()
for filename in os.listdir(dir):
path = dir + filename
face = extract_face(path)
faces.append(face)
return faces
def load_dataset(dir):
X, y = (list(), list())
for subdir in os.listdir(dir):
path = dir + subdir + '/'
faces = load_face(path)
labels = [subdir for i in range(len(faces))]
X.extend(faces)
y.extend(labels)
return (np.asarray(X), np.asarray(y))
def get_embedding(face_pixels):
face_pixels = face_pixels.astype('float32')
mean, std = (face_pixels.mean(), face_pixels.std())
face_pixels = (face_pixels - mean) / std
samples = np.expand_dims(face_pixels, axis=0)
yhat = model.predict(samples)
return yhat[0]
data = np.load('../input/new-masked-face/extracted_masked_unmasked.npz')
trainX, trainy, testX, testy = (data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3'])
print(trainy.shape) | code |
90146618/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
def extract_face(filename, required_size=(160, 160)):
image = Image.open(filename)
image = image.convert('RGB')
pixels = np.asarray(image)
image = Image.fromarray(pixels)
image = image.resize(required_size)
face_array = np.asarray(image)
return face_array
def load_face(dir):
faces = list()
for filename in os.listdir(dir):
path = dir + filename
face = extract_face(path)
faces.append(face)
return faces
def load_dataset(dir):
X, y = (list(), list())
for subdir in os.listdir(dir):
path = dir + subdir + '/'
faces = load_face(path)
labels = [subdir for i in range(len(faces))]
X.extend(faces)
y.extend(labels)
return (np.asarray(X), np.asarray(y))
def get_embedding(face_pixels):
face_pixels = face_pixels.astype('float32')
mean, std = (face_pixels.mean(), face_pixels.std())
face_pixels = (face_pixels - mean) / std
samples = np.expand_dims(face_pixels, axis=0)
yhat = model.predict(samples)
return yhat[0]
data = np.load('../input/new-masked-face/extracted_masked_unmasked.npz')
trainX, trainy, testX, testy = (data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3'])
print('Loaded: ', trainX.shape, trainy.shape, testX.shape, testy.shape) | code |
90146618/cell_3 | [
"text_plain_output_1.png"
] | from keras.models import load_model
from keras.models import load_model
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
print('Loaded Model') | code |
90146618/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.models import load_model
from keras.models import load_model
import numpy as np
import numpy as np # linear algebra
import os
import os
import numpy as np
import pandas as pd
import cv2
from matplotlib import pyplot as plt
from keras.models import load_model
from PIL import Image
from sklearn.model_selection import train_test_split
import os
model = load_model('../input/facenet/keras-facenet/model/facenet_keras.h5')
def extract_face(filename, required_size=(160, 160)):
image = Image.open(filename)
image = image.convert('RGB')
pixels = np.asarray(image)
image = Image.fromarray(pixels)
image = image.resize(required_size)
face_array = np.asarray(image)
return face_array
def load_face(dir):
faces = list()
for filename in os.listdir(dir):
path = dir + filename
face = extract_face(path)
faces.append(face)
return faces
def load_dataset(dir):
X, y = (list(), list())
for subdir in os.listdir(dir):
path = dir + subdir + '/'
faces = load_face(path)
labels = [subdir for i in range(len(faces))]
X.extend(faces)
y.extend(labels)
return (np.asarray(X), np.asarray(y))
def get_embedding(face_pixels):
face_pixels = face_pixels.astype('float32')
mean, std = (face_pixels.mean(), face_pixels.std())
face_pixels = (face_pixels - mean) / std
samples = np.expand_dims(face_pixels, axis=0)
yhat = model.predict(samples)
return yhat[0]
data = np.load('../input/new-masked-face/extracted_masked_unmasked.npz')
trainX, trainy, testX, testy = (data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3'])
type(trainX) | code |
32068320/cell_13 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False)
predicted_std = model.predict(X_test)
result_std = pd.DataFrame(predicted_std)
result_std.columns = ['predict']
result_std['actual'] = Y_test
loss = hist.history['loss']
epochs = len(loss)
fig = plt.figure()
plt.plot(range(epochs), loss, marker='.', label='loss(training data)')
plt.show()
predicted = scaler.inverse_transform(predicted_std)
Y_test2 = scaler.inverse_transform(Y_test)
result = pd.DataFrame(predicted)
result.columns = ['predict']
result['actual'] = Y_test2
result.plot(figsize=(25, 6))
plt.show() | code |
32068320/cell_9 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False)
predicted_std = model.predict(X_test)
result_std = pd.DataFrame(predicted_std)
result_std.columns = ['predict']
result_std['actual'] = Y_test
result_std.plot(figsize=(25, 6))
plt.show() | code |
32068320/cell_2 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font) | code |
32068320/cell_19 | [
"image_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.metrics import mean_squared_log_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False)
predicted_std = model.predict(X_test)
result_std = pd.DataFrame(predicted_std)
result_std.columns = ['predict']
result_std['actual'] = Y_test
loss = hist.history['loss']
epochs = len(loss)
fig = plt.figure()
plt.plot(range(epochs), loss, marker='.', label='loss(training data)')
plt.show()
predicted = scaler.inverse_transform(predicted_std)
Y_test2 = scaler.inverse_transform(Y_test)
np.sqrt(mean_squared_log_error(predicted, Y_test2))
result = pd.DataFrame(predicted)
result.columns = ['predict']
result['actual'] = Y_test2
test_df = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
submission = pd.read_csv('../input/covid19-global-forecasting-week-4/submission.csv')
temp = (datetime.datetime.strptime('2020-04-01', '%Y-%m-%d') - datetime.timedelta(days=time_series_len)).strftime('%Y-%m-%d')
test_df = train_df[train_df['Date'] > temp]
check_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv').query("Date>'2020-04-01'and Date<='2020-04-14'")
check_df['ConfirmedCases_std'] = scaler.transform(check_df['ConfirmedCases'].values.reshape(len(check_df['ConfirmedCases'].values), 1))
confirmedCases_pred = []
for i in range(0, 313 * time_series_len, time_series_len):
temp_array = np.array(test_df['ConfirmedCases_std'][i:i + time_series_len])
for j in range(43):
if j < 13:
temp_array = np.append(temp_array, np.array(check_df['ConfirmedCases_std'])[int(i * 13 / time_series_len) + j])
elif np.array(test_df['ConfirmedCases'][i:i + time_series_len]).sum() == 0:
temp_array = np.append(temp_array, temp_array[-1])
else:
temp_array = np.append(temp_array, model.predict(temp_array[-time_series_len:].reshape(1, time_series_len, 1)))
confirmedCases_pred.append(temp_array[-43:])
submission['ConfirmedCases'] = np.abs(scaler.inverse_transform(np.array(confirmedCases_pred).reshape(313 * 43)))
submission['ConfirmedCases_std'] = np.array(confirmedCases_pred).reshape(313 * 43)
submission | code |
32068320/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 |
32068320/cell_8 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False) | code |
32068320/cell_3 | [
"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/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df | code |
32068320/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False)
predicted_std = model.predict(X_test)
result_std = pd.DataFrame(predicted_std)
result_std.columns = ['predict']
result_std['actual'] = Y_test
loss = hist.history['loss']
epochs = len(loss)
fig = plt.figure()
plt.plot(range(epochs), loss, marker='.', label='loss(training data)')
plt.show() | code |
32068320/cell_12 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras.initializers import random_uniform
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.models import Sequential
from keras.optimizers import Adagrad
from sklearn.metrics import mean_squared_log_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import GRU
from keras.initializers import random_uniform
from keras.optimizers import Adagrad
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from keras import backend as K
import tensorflow as tf
import datetime
import matplotlib.pyplot as plt
plt.style.use('ggplot')
font = {'family': 'meiryo'}
plt.rc('font', **font)
train_df = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
train_df = train_df.fillna('No State')
train_df
test_rate = 0.1
time_series_len = 18
train_date_count = len(set(train_df['Date']))
X, Y = ([], [])
scaler = StandardScaler()
train_df['ConfirmedCases_std'] = scaler.fit_transform(train_df['ConfirmedCases'].values.reshape(len(train_df['ConfirmedCases'].values), 1))
for state, country in train_df.groupby(['Province_State', 'Country_Region']).sum().index:
df = train_df[(train_df['Country_Region'] == country) & (train_df['Province_State'] == state)]
if df['ConfirmedCases'].sum() != 0:
for i in range(len(df) - time_series_len):
X.append(df[['ConfirmedCases_std']].iloc[i:i + time_series_len].values)
Y.append(df[['ConfirmedCases_std']].iloc[i + time_series_len].values)
X = np.array(X)
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_rate, shuffle=True, random_state=0)
def huber_loss(y_true, y_pred, clip_delta=1.0):
error = y_true - y_pred
cond = tf.keras.backend.abs(error) < clip_delta
squared_loss = 0.5 * tf.keras.backend.square(error)
linear_loss = clip_delta * (tf.keras.backend.abs(error) - 0.5 * clip_delta)
return tf.where(cond, squared_loss, linear_loss)
def huber_loss_mean(y_true, y_pred, clip_delta=1.0):
return tf.keras.backend.mean(huber_loss(y_true, y_pred, clip_delta))
epochs_num = 20
n_in = 1
model = Sequential()
model.add(GRU(100, batch_input_shape=(None, time_series_len, n_in), kernel_initializer=random_uniform(seed=0), return_sequences=False))
model.add(Dense(50))
model.add(Dropout(0.15, seed=0))
model.add(Dense(n_in, kernel_initializer=random_uniform(seed=0)))
model.add(Activation('linear'))
opt = Adagrad(lr=0.01, epsilon=1e-08, decay=0.0001)
model.compile(loss=huber_loss_mean, optimizer=opt)
callbacks = [ReduceLROnPlateau(monitor='loss', patience=4, verbose=1, factor=0.6), EarlyStopping(monitor='loss', patience=10)]
hist = model.fit(X_train, Y_train, batch_size=20, epochs=epochs_num, callbacks=callbacks, shuffle=False)
predicted_std = model.predict(X_test)
result_std = pd.DataFrame(predicted_std)
result_std.columns = ['predict']
result_std['actual'] = Y_test
predicted = scaler.inverse_transform(predicted_std)
Y_test2 = scaler.inverse_transform(Y_test)
np.sqrt(mean_squared_log_error(predicted, Y_test2)) | code |
122258225/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.isna().sum() | code |
122258225/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.head() | code |
122258225/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.info() | code |
122258225/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_train.isna().sum()
df_titanic_train['Sex'].value_counts() | code |
122258225/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
df_gender_submission.head() | code |
122258225/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_train.isna().sum() | code |
122258225/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_train.isna().sum()
df_titanic_train['Embarked'].value_counts() | code |
122258225/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.describe() | code |
122258225/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 |
122258225/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.head() | code |
122258225/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)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_train['Embarked'].mode() | code |
122258225/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_train.isna().sum()
df_titanic_test.isna().sum()
model = LogisticRegression()
x = df_titanic_train.loc[:, df_titanic_train.columns != 'Survived']
y = df_titanic_train['Survived']
model.fit(x, y)
y_pred = model.predict(df_titanic_test)
df_gender_submission = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
df_true_answers = df_gender_submission['Survived']
print(accuracy_score(df_true_answers, y_pred) * 100) | code |
122258225/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.drop(labels=['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)
df_titanic_test.isna().sum()
df_titanic_test['Sex'].value_counts() | code |
122258225/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)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_train.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_test.drop(labels=['Cabin'], axis=1, inplace=True)
df_titanic_train.describe() | code |
122258225/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_titanic_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_titanic_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_titanic_test.info() | code |
2035583/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import AdaBoostClassifier, AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsOneClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC
import numpy as np # linear algebra
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, Y)
x_train = np.array(x_train).reshape(len(x_train), 10)
x_test = np.array(x_test).reshape(len(x_test), 10)
y_train = y_train.values.ravel()
y_test = y_test.values.ravel()
def fit_and_score(x_train, x_validation, y_train, y_validation):
names = ['OneVsrest', 'OneVsOne', 'MultinomialNB', 'AdaBoost', 'LinearRegression', 'DecisionTreeRegressor', 'AdaBoostRegressor', 'GradientBoostingRegressor']
models = [OneVsRestClassifier(LinearSVC(random_state=0)), OneVsOneClassifier(LinearSVC(random_state=0)), MultinomialNB(), AdaBoostClassifier(), LinearRegression(), tree.DecisionTreeRegressor(), AdaBoostRegressor(), GradientBoostingRegressor()]
scores_train = []
scores_validation = []
for model in models:
model.fit(x_train, y_train)
scores_train.append(model.score(x_train, y_train))
scores_validation.append(model.score(x_validation, y_validation))
return (names, scores_train, scores_validation)
nome, resultado_treino, resultado_validacao = fit_and_score(x_train, x_test, y_train, y_test)
print(' Results ')
for n, r_train, r_validation in zip(nome, resultado_treino, resultado_validacao):
print('_' * 30)
print('Model: {}'.format(n))
print('Score train: {:0.3}'.format(r_train))
print('Score validation: {:0.3}'.format(r_validation))
print('\n') | code |
2035583/cell_4 | [
"text_html_output_1.png"
] | X.head() | code |
2035583/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2035583/cell_5 | [
"text_plain_output_1.png"
] | print(X.isnull().any()) | code |
17135521/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
df_test = pd.read_csv('../input/test.csv')
df_test[['Id', 'SalePrice']].head() | code |
17135521/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
X = df_tidy[['OverallQual']]
y = df_tidy['SalePrice']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
print(X_train.count())
print(X_test.count()) | code |
17135521/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17135521/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
17135521/cell_26 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
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)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
# ヒートマップに表示させるカラムの数
k = 10
# SalesPriceとの相関が大きい上位10個のカラム名を取得
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
# SalesPriceとの相関が大きい上位10個のカラムを対象に相関を算出
# .T(Trancepose[転置行列])を行う理由は、corrcoefで相関を算出する際に、各カラムの値を行毎にまとめなければならない為
cm = np.corrcoef(df[cols].values.T)
# ヒートマップのフォントサイズを指定
sns.set(font_scale=1.25)
# 算出した相関データをヒートマップで表示
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
X = df_tidy[['OverallQual']]
y = df_tidy['SalePrice']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
slr = LinearRegression()
slr.fit(X_train, y_train)
plt.scatter(X_train, y_train)
plt.plot(X_train, slr.predict(X_train), color='red')
plt.show() | code |
17135521/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
k = 10
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show() | code |
17135521/cell_19 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns | code |
17135521/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
len(df) | code |
17135521/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.describe() | code |
17135521/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15) | code |
17135521/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt | code |
17135521/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy.describe() | code |
17135521/cell_31 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_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)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
# ヒートマップに表示させるカラムの数
k = 10
# SalesPriceとの相関が大きい上位10個のカラム名を取得
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
# SalesPriceとの相関が大きい上位10個のカラムを対象に相関を算出
# .T(Trancepose[転置行列])を行う理由は、corrcoefで相関を算出する際に、各カラムの値を行毎にまとめなければならない為
cm = np.corrcoef(df[cols].values.T)
# ヒートマップのフォントサイズを指定
sns.set(font_scale=1.25)
# 算出した相関データをヒートマップで表示
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
X = df_tidy[['OverallQual']]
y = df_tidy['SalePrice']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
slr = LinearRegression()
slr.fit(X_train, y_train)
from sklearn.metrics import r2_score
y_train_pred = slr.predict(X_train)
y_test_pred = slr.predict(X_test)
df_test = pd.read_csv('../input/test.csv')
X_test = df_test[['OverallQual']].values
y_test_pred = slr.predict(X_test)
y_test_pred | code |
17135521/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
X = df_tidy[['OverallQual']]
y = df_tidy['SalePrice']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
slr = LinearRegression()
slr.fit(X_train, y_train) | code |
17135521/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
df.isnull().sum().sort_values().tail(15) | code |
17135521/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
corrmat.head() | code |
17135521/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_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)
import seaborn as sns
df = pd.read_csv('../input/train.csv')
corrmat = df.corr()
# ヒートマップに表示させるカラムの数
k = 10
# SalesPriceとの相関が大きい上位10個のカラム名を取得
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
# SalesPriceとの相関が大きい上位10個のカラムを対象に相関を算出
# .T(Trancepose[転置行列])を行う理由は、corrcoefで相関を算出する際に、各カラムの値を行毎にまとめなければならない為
cm = np.corrcoef(df[cols].values.T)
# ヒートマップのフォントサイズを指定
sns.set(font_scale=1.25)
# 算出した相関データをヒートマップで表示
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
df.isnull().sum().sort_values().tail(15)
df_tidy = df.fillna({'PoolQC': 'Nothing', 'MiscFeature': 'Nothing', 'Alley': 'Nothing', 'Fence': 'Nothing', 'FireplaceQu': 'Nothing', 'LotFrontage': 0}).dropna()
df_tidy.isnull().sum().sort_values().tail(15)
df_tidy = pd.get_dummies(df_tidy, columns=['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'BsmtFullBath', 'BsmtHalfBath', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition'], drop_first=True)
df_tidy.columns
X = df_tidy[['OverallQual']]
y = df_tidy['SalePrice']
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
slr = LinearRegression()
slr.fit(X_train, y_train)
from sklearn.metrics import r2_score
y_train_pred = slr.predict(X_train)
y_test_pred = slr.predict(X_test)
print('Accuracy on Training Set: {:.3f}'.format(r2_score(y_train, y_train_pred)))
print('Accuracy on Validation Set: {:.3f}'.format(r2_score(y_test, y_test_pred))) | code |
17135521/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.head() | code |
104131002/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
num_cols | code |
104131002/cell_25 | [
"text_plain_output_1.png"
] | from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
def high_correlated_cols(dataframe, plot=False, corr_th=0.9):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize': (15, 15)})
return drop_list
high_correlated_cols(df, plot=True)
df['last_order_date'].max()
analysis_date = dt.datetime(2021, 6, 1)
df['recency'] = (analysis_date - df['last_order_date']).astype('timedelta64[D]')
df['tenure'] = (df['last_order_date'] - df['first_order_date']).astype('timedelta64[D]')
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
def check_skew(df_skew, column):
skew = stats.skew(df_skew[column])
skewtest = stats.skewtest(df_skew[column])
plt.title('Distribution of ' + column)
sns.histplot(df_skew[column], color='g')
print("{}'s: Skew: {}, : {}".format(column, skew, skewtest))
return
plt.figure(figsize=(9, 9))
plt.subplot(6, 1, 1)
check_skew(model_df, 'order_num_total_ever_online')
plt.subplot(6, 1, 2)
check_skew(model_df, 'order_num_total_ever_offline')
plt.subplot(6, 1, 3)
check_skew(model_df, 'customer_value_total_ever_offline')
plt.subplot(6, 1, 4)
check_skew(model_df, 'customer_value_total_ever_online')
plt.subplot(6, 1, 5)
check_skew(model_df, 'recency')
plt.subplot(6, 1, 6)
check_skew(model_df, 'tenure')
plt.tight_layout()
plt.savefig('before_transform.png', format='png', dpi=1000)
plt.show() | code |
104131002/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
def high_correlated_cols(dataframe, plot=False, corr_th=0.9):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize': (15, 15)})
sns.heatmap(corr, cmap='RdBu')
plt.show()
return drop_list
high_correlated_cols(df, plot=True) | code |
104131002/cell_6 | [
"text_html_output_1.png"
] | !pip install xgboost
!pip install lightgbm
!pip install catboost
import numpy as np
import datetime as dt
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn.cluster import AgglomerativeClustering
from sklearn.linear_model import Ridge, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error,r2_score
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn import model_selection
import matplotlib.pyplot as plt
from sklearn.linear_model import RidgeCV, LassoCV, ElasticNetCV
from warnings import filterwarnings
filterwarnings('ignore')
!pip install missingno
import missingno as msno
from sklearn.preprocessing import RobustScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, plot_roc_curve
from sklearn.model_selection import train_test_split, cross_validate
import warnings
warnings.simplefilter(action='ignore')
import xgboost
from sklearn.impute import KNNImputer
from sklearn import preprocessing
from sklearn.neighbors import LocalOutlierFactor
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, Lasso, LassoCV
from sklearn.metrics import mean_squared_error
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVR
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
import random
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from yellowbrick.cluster import KElbowVisualizer
from scipy.cluster.hierarchy import linkage
from scipy.cluster.hierarchy import dendrogram
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.preprocessing import LabelEncoder
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 500) | code |
104131002/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
def high_correlated_cols(dataframe, plot=False, corr_th=0.9):
corr = dataframe.corr()
cor_matrix = corr.abs()
upper_triangle_matrix = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(bool))
drop_list = [col for col in upper_triangle_matrix.columns if any(upper_triangle_matrix[col] > corr_th)]
if plot:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(rc={'figure.figsize': (15, 15)})
return drop_list
high_correlated_cols(df, plot=True)
df['last_order_date'].max()
analysis_date = dt.datetime(2021, 6, 1)
df['recency'] = (analysis_date - df['last_order_date']).astype('timedelta64[D]')
df['tenure'] = (df['last_order_date'] - df['first_order_date']).astype('timedelta64[D]')
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
model_df.head() | code |
104131002/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
df.info() | code |
104131002/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
df.head() | code |
104131002/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
# Functions
def check_df(dataframe, head=5):
print("##################### Shape #####################")
print(dataframe.shape)
print("##################### Types #####################")
print(dataframe.dtypes)
print("##################### Head #####################")
print(dataframe.head(head))
print("##################### Tail #####################")
print(dataframe.tail(head))
print("##################### NA #####################")
print(dataframe.isnull().sum())
print("##################### Quantiles #####################")
print(dataframe.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T)
def cat_summary(dataframe, col_name, plot=False):
print(pd.DataFrame({col_name: dataframe[col_name].value_counts(),
"Ratio": 100 * dataframe[col_name].value_counts() / len(dataframe)}))
print("##########################################")
if plot:
sns.countplot(x=dataframe[col_name], data=dataframe)
plt.show(block=True)
def num_summary(dataframe, numerical_col, plot=False):
quantiles = [0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99]
print(dataframe[numerical_col].describe(quantiles).T)
if plot:
dataframe[numerical_col].hist(bins=20)
plt.xlabel(numerical_col)
plt.title(numerical_col)
plt.show(block=True)
def target_summary_with_num(dataframe, target, numerical_col):
print(dataframe.groupby(target).agg({numerical_col: "mean"}), end="\n\n\n")
def target_summary_with_cat(dataframe, target, categorical_col):
print(pd.DataFrame({"TARGET_MEAN": dataframe.groupby(categorical_col)[target].mean()}), end="\n\n\n")
def correlation_matrix(df, cols):
fig = plt.gcf()
fig.set_size_inches(10, 8)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
fig = sns.heatmap(df[cols].corr(), annot=True, linewidths=0.5, annot_kws={'size': 12}, linecolor='w', cmap='RdBu')
plt.show(block=True)
def grab_col_names(dataframe, cat_th=10, car_th=20):
"""
Veri setindeki kategorik, numerik ve kategorik fakat kardinal değişkenlerin isimlerini verir.
Not: Kategorik değişkenlerin içerisine numerik görünümlü kategorik değişkenler de dahildir.
Parameters
------
dataframe: dataframe
Değişken isimleri alınmak istenilen dataframe
cat_th: int, optional
numerik fakat kategorik olan değişkenler için sınıf eşik değeri
car_th: int, optinal
kategorik fakat kardinal değişkenler için sınıf eşik değeri
Returns
------
cat_cols: list
Kategorik değişken listesi
num_cols: list
Numerik değişken listesi
cat_but_car: list
Kategorik görünümlü kardinal değişken listesi
Examples
------
import seaborn as sns
df = sns.load_dataset("iris")
print(grab_col_names(df))
Notes
------
cat_cols + num_cols + cat_but_car = toplam değişken sayısı
num_but_cat cat_cols'un içerisinde.
Return olan 3 liste toplamı toplam değişken sayısına eşittir: cat_cols + num_cols + cat_but_car = değişken sayısı
"""
# cat_cols, cat_but_car
cat_cols = [col for col in dataframe.columns if dataframe[col].dtypes == "O"]
num_but_cat = [col for col in dataframe.columns if dataframe[col].nunique() < cat_th and
dataframe[col].dtypes != "O"]
cat_but_car = [col for col in dataframe.columns if dataframe[col].nunique() > car_th and
dataframe[col].dtypes == "O"]
cat_cols = cat_cols + num_but_cat
cat_cols = [col for col in cat_cols if col not in cat_but_car]
# num_cols
num_cols = [col for col in dataframe.columns if dataframe[col].dtypes != "O"]
num_cols = [col for col in num_cols if col not in num_but_cat]
# print(f"Observations: {dataframe.shape[0]}")
# print(f"Variables: {dataframe.shape[1]}")
# print(f'cat_cols: {len(cat_cols)}')
# print(f'num_cols: {len(num_cols)}')
# print(f'cat_but_car: {len(cat_but_car)}')
# print(f'num_but_cat: {len(num_but_cat)}')
return cat_cols, num_cols, cat_but_car
def missing_values_table(dataframe, na_name=False):
na_columns = [col for col in dataframe.columns if dataframe[col].isnull().sum() > 0]
n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False)
ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False)
missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio'])
print(missing_df, end="\n")
if na_name:
return na_columns
def outlier_thresholds(dataframe, col_name, q1=0.25, q3=0.75):
quartile1 = dataframe[col_name].quantile(q1)
quartile3 = dataframe[col_name].quantile(q3)
interquantile_range = quartile3 - quartile1
up_limit = quartile3 + 1.5 * interquantile_range
low_limit = quartile1 - 1.5 * interquantile_range
return low_limit, up_limit
def replace_with_thresholds(dataframe, variable):
low_limit, up_limit = outlier_thresholds(dataframe, variable)
dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit
dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit
def check_outlier(dataframe, col_name, q1=0.25, q3=0.75):
low_limit, up_limit = outlier_thresholds(dataframe, col_name, q1, q3)
if dataframe[(dataframe[col_name] > up_limit) | (dataframe[col_name] < low_limit)].any(axis=None):
return True
else:
return False
def one_hot_encoder(dataframe, categorical_cols, drop_first=False):
dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first)
return dataframe
df_ = pd.read_csv('../input/customer-segmentation-with-unsupervised-learning/flo_data_20k.csv')
df = df_.copy()
for col in cat_cols:
cat_summary(df, col) | code |
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