path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
17116059/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
df = pd.read_csv('../input/raw_lemonade_data.csv')
print(df.head())
print(df.tail()) | code |
17116059/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
quartet = pd.read_csv('../input/quartet.csv', index_col='id')
quartet.groupby('dataset').agg(['mean', 'std']) | code |
72092898/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from scipy.spatial.distance import squareform, pdist
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
def get_dissimilarity(data):
from scipy.spatial.distance import squareform, pdist
similarity_matrix = pd.DataFrame(squareform(pdist(data, 'euclidean')))
return similarity_matrix
similarity_matrix = get_dissimilarity(wine_data_normalized)
similarity_matrix
def get_avg_dissimilarity(data):
avg_dissimilarity = np.zeros((data.shape[0], 1))
for i in range(data.shape[0]):
avg_dissimilarity[i] = data[i].mean()
return avg_dissimilarity
avg_dissimilarity = get_avg_dissimilarity(similarity_matrix)
(avg_dissimilarity[:10], avg_dissimilarity.shape)
def form_m_clusters(data, avg_data):
cluster_objects = []
cluster = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if data[i][j] < avg_data[i]:
cluster.append(j)
cluster_objects.append(cluster)
cluster = []
return cluster_objects
cluster_objects = form_m_clusters(similarity_matrix, avg_dissimilarity)
len(cluster_objects)
def remove_subset_clusters(cluster_objects):
for i in range(len(cluster_objects)):
for j in range(i + 1, len(cluster_objects)):
if j < len(cluster_objects) and set(cluster_objects[j]).issubset(set(cluster_objects[i])):
cluster_objects = np.delete(cluster_objects, j, axis=0)
print('cluster', j, 'subset of cluster', i, 'deleted!')
return cluster_objects | code |
72092898/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
print(col, 'max:', data[col].max(), 'min:', data[col].min())
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
wine_data_normalized.head() | code |
72092898/cell_6 | [
"text_plain_output_1.png"
] | from scipy.spatial.distance import squareform, pdist
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
def get_dissimilarity(data):
from scipy.spatial.distance import squareform, pdist
similarity_matrix = pd.DataFrame(squareform(pdist(data, 'euclidean')))
return similarity_matrix
similarity_matrix = get_dissimilarity(wine_data_normalized)
similarity_matrix
def get_avg_dissimilarity(data):
avg_dissimilarity = np.zeros((data.shape[0], 1))
for i in range(data.shape[0]):
avg_dissimilarity[i] = data[i].mean()
return avg_dissimilarity
avg_dissimilarity = get_avg_dissimilarity(similarity_matrix)
(avg_dissimilarity[:10], avg_dissimilarity.shape) | code |
72092898/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
wine_data.head() | code |
72092898/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 |
72092898/cell_7 | [
"text_plain_output_1.png"
] | from scipy.spatial.distance import squareform, pdist
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
def get_dissimilarity(data):
from scipy.spatial.distance import squareform, pdist
similarity_matrix = pd.DataFrame(squareform(pdist(data, 'euclidean')))
return similarity_matrix
similarity_matrix = get_dissimilarity(wine_data_normalized)
similarity_matrix
def get_avg_dissimilarity(data):
avg_dissimilarity = np.zeros((data.shape[0], 1))
for i in range(data.shape[0]):
avg_dissimilarity[i] = data[i].mean()
return avg_dissimilarity
avg_dissimilarity = get_avg_dissimilarity(similarity_matrix)
(avg_dissimilarity[:10], avg_dissimilarity.shape)
def form_m_clusters(data, avg_data):
cluster_objects = []
cluster = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if data[i][j] < avg_data[i]:
cluster.append(j)
cluster_objects.append(cluster)
cluster = []
return cluster_objects
cluster_objects = form_m_clusters(similarity_matrix, avg_dissimilarity)
len(cluster_objects) | code |
72092898/cell_8 | [
"text_plain_output_1.png"
] | from scipy.spatial.distance import squareform, pdist
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
def get_dissimilarity(data):
from scipy.spatial.distance import squareform, pdist
similarity_matrix = pd.DataFrame(squareform(pdist(data, 'euclidean')))
return similarity_matrix
similarity_matrix = get_dissimilarity(wine_data_normalized)
similarity_matrix
def get_avg_dissimilarity(data):
avg_dissimilarity = np.zeros((data.shape[0], 1))
for i in range(data.shape[0]):
avg_dissimilarity[i] = data[i].mean()
return avg_dissimilarity
avg_dissimilarity = get_avg_dissimilarity(similarity_matrix)
(avg_dissimilarity[:10], avg_dissimilarity.shape)
def form_m_clusters(data, avg_data):
cluster_objects = []
cluster = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
if data[i][j] < avg_data[i]:
cluster.append(j)
cluster_objects.append(cluster)
cluster = []
return cluster_objects
cluster_objects = form_m_clusters(similarity_matrix, avg_dissimilarity)
len(cluster_objects)
for i in range(5):
print('cluster', i, '(', max(cluster_objects[i]), ')', ': ', cluster_objects[i])
print('') | code |
72092898/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
wine_data.info() | code |
72092898/cell_5 | [
"text_plain_output_1.png"
] | from scipy.spatial.distance import squareform, pdist
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
wine_data = pd.read_csv('../input/uci-wine-data/wine-clustering.csv')
def normalize_data(data):
data_normalized = data.copy()
for col in data.columns:
data_normalized[col] = (data_normalized[col] - data_normalized[col].min()) / (data_normalized[col].max() - data_normalized[col].min())
return data_normalized
wine_data_normalized = normalize_data(wine_data)
def get_dissimilarity(data):
from scipy.spatial.distance import squareform, pdist
similarity_matrix = pd.DataFrame(squareform(pdist(data, 'euclidean')))
return similarity_matrix
similarity_matrix = get_dissimilarity(wine_data_normalized)
similarity_matrix | code |
324878/cell_13 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
test_df.describe() | code |
324878/cell_9 | [
"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
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
sns.countplot(x='Embarked', data=train_df) | code |
324878/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.svm import SVC, LinearSVC
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
svc = SVC()
svc.fit(X_train, Y_train)
Y_pred_svm = svc.predict(X_test)
svc.score(X_train, Y_train) | code |
324878/cell_4 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
train_df.info() | code |
324878/cell_23 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, Y_train)
Y_pred_knn = knn.predict(X_test)
knn.score(X_train, Y_train) | code |
324878/cell_20 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df['Family'] | code |
324878/cell_6 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
median_age = train_df['Age'].median()
train_df['Age'] = train_df['Age'].fillna(median_age)
train_df['Embarked'] = train_df['Embarked'].fillna('S')
test_df['Age'] = test_df['Age'].fillna(median_age)
test_df['Fare'] = test_df['Fare'].fillna(0)
test_df['Cabin'] = test_df['Cabin'].fillna('Missing')
train_df['Cabin'] = train_df['Cabin'].fillna('Missing') | code |
324878/cell_26 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LogisticRegression
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred_log = logreg.predict(X_test)
logreg.score(X_train, Y_train) | code |
324878/cell_11 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"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
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
sns.countplot(x='Survived', hue='Pclass', data=train_df, order=[0, 1]) | code |
324878/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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df.info() | code |
324878/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 |
324878/cell_7 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df.info()
test_df.info() | code |
324878/cell_18 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df['Family'] = train_df['Parch'] + train_df['SibSp']
train_df['Family'].loc[train_df['Family'] > 0] = 1
train_df['Family'].loc[train_df['Family'] == 0] = 0
test_df['Family'] = test_df['Parch'] + test_df['SibSp']
test_df['Family'].loc[test_df['Family'] > 0] = 1
test_df['Family'].loc[test_df['Family'] == 0] = 0 | code |
324878/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
pid = np.array(test_df['PassengerId']).astype(int)
my_solution = pd.DataFrame(Y_pred, pid, columns=['Survived'])
print(my_solution)
print(my_solution.shape)
my_solution.to_csv('titanic_solution_rf.csv', index_label=['PassengerId']) | code |
324878/cell_8 | [
"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
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
sns.factorplot('Embarked', 'Survived', data=train_df, size=4) | code |
324878/cell_15 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df['Child'] = float(0)
train_df['Child'][train_df['Age'] < 18] = 1
train_df['Child'][train_df['Age'] >= 18] = 0
test_df['Child'] = float(0)
test_df['Child'][test_df['Age'] < 18] = 1
test_df['Child'][test_df['Age'] >= 18] = 0 | code |
324878/cell_16 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
letters = 'ABCDEFGHIJKLM'
train_df['CabinStartsWith'] = float(-1)
train_df['CabinStartsWithLetter'] = ''
train_df['CabinStartsWithLetter'][train_df['Cabin'] != 'Missing'] = train_df['Cabin'].str[0]
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'A'] = 0
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'B'] = 1
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'C'] = 2
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'D'] = 3
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'E'] = 4
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'F'] = 5
train_df['CabinStartsWith'][train_df['CabinStartsWithLetter'] == 'G'] = 6
test_df['CabinStartsWith'] = -1
test_df['CabinStartsWithLetter'] = ''
test_df['CabinStartsWithLetter'][test_df['Cabin'] != 'Missing'] = test_df['Cabin'].str[0]
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'A'] = 0
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'B'] = 1
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'C'] = 2
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'D'] = 3
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'E'] = 4
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'F'] = 5
test_df['CabinStartsWith'][test_df['CabinStartsWithLetter'] == 'G'] = 6 | code |
324878/cell_3 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
train_df.head() | code |
324878/cell_17 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df['CabinStartsWith'] | code |
324878/cell_24 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_35.png",
"application_vnd.jupyter.stderr_output_24.png",
"application_vnd.jupyter.stderr_output_16.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_52.png",
"application_vnd.jupyter.stderr_output_53.png",
"application_vnd.jupyter.stderr_output_32.png",
"application_vnd.jupyter.stderr_output_48.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"application_vnd.jupyter.stderr_output_18.png",
"application_vnd.jupyter.stderr_output_38.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_26.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_31.png",
"application_vnd.jupyter.stderr_output_33.png",
"application_vnd.jupyter.stderr_output_25.png",
"application_vnd.jupyter.stderr_output_12.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"application_vnd.jupyter.stderr_output_23.png",
"application_vnd.jupyter.stderr_output_34.png",
"application_vnd.jupyter.stderr_output_19.png",
"application_vnd.jupyter.stderr_output_44.png",
"application_vnd.jupyter.stderr_output_13.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_42.png",
"application_vnd.jupyter.stderr_output_5.png",
"application_vnd.jupyter.stderr_output_30.png",
"application_vnd.jupyter.stderr_output_15.png",
"application_vnd.jupyter.stderr_output_17.png",
"application_vnd.jupyter.stderr_output_28.png",
"application_vnd.jupyter.stderr_output_46.png",
"application_vnd.jupyter.stderr_output_41.png",
"application_vnd.jupyter.stderr_output_20.png",
"application_vnd.jupyter.stderr_output_49.png",
"application_vnd.jupyter.stderr_output_47.png",
"application_vnd.jupyter.stderr_output_36.png",
"application_vnd.jupyter.stderr_output_57.png",
"application_vnd.jupyter.stderr_output_22.png",
"application_vnd.jupyter.stderr_output_56.png",
"application_vnd.jupyter.stderr_output_50.png",
"application_vnd.jupyter.stderr_output_29.png",
"application_vnd.jupyter.stderr_output_1.png",
"application_vnd.jupyter.stderr_output_51.png",
"application_vnd.jupyter.stderr_output_45.png",
"application_vnd.jupyter.stderr_output_14.png",
"application_vnd.jupyter.stderr_output_39.png",
"application_vnd.jupyter.stderr_output_21.png",
"application_vnd.jupyter.stderr_output_43.png",
"application_vnd.jupyter.stderr_output_54.png",
"application_vnd.jupyter.stderr_output_55.png",
"application_vnd.jupyter.stderr_output_40.png",
"application_vnd.jupyter.stderr_output_37.png"
] | from sklearn.naive_bayes import GaussianNB
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred_nb = gaussian.predict(X_test)
gaussian.score(X_train, Y_train) | code |
324878/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
test_df['Sex'][test_df['Sex'] == 'male'] = 0
test_df['Sex'][test_df['Sex'] == 'female'] = 1
test_df['Embarked'][test_df['Embarked'] == 'S'] = 0
test_df['Embarked'][test_df['Embarked'] == 'C'] = 1
test_df['Embarked'][test_df['Embarked'] == 'Q'] = 2
train_df['Sex'][train_df['Sex'] == 'male'] = 0
train_df['Sex'][train_df['Sex'] == 'female'] = 1
train_df['Embarked'][train_df['Embarked'] == 'S'] = 0
train_df['Embarked'][train_df['Embarked'] == 'C'] = 1
train_df['Embarked'][train_df['Embarked'] == 'Q'] = 2 | code |
324878/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train) | code |
324878/cell_10 | [
"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
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
sns.countplot(x='Survived', hue='Embarked', data=train_df, order=[0, 1]) | code |
324878/cell_27 | [
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.svm import SVC, LinearSVC
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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
X_train = train_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
Y_train = train_df[['Survived']].values
X_test = test_df[['Pclass', 'Sex', 'Age', 'Parch', 'Fare', 'Embarked', 'Child', 'Family', 'CabinStartsWith']].values
linreg = LinearSVC()
linreg.fit(X_train, Y_train)
Y_pred_lin = linreg.predict(X_test)
linreg.score(X_train, Y_train) | code |
324878/cell_12 | [
"text_plain_output_2.png",
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Age is null for some rows
Cabin is almost always null
Embarked is null for only 2 rows
"""
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
train_df.describe() | code |
324878/cell_5 | [
"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)
train_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
test_df = pd.read_csv('../input/test.csv', dtype={'Age': np.float64})
"""
Takeaways
Fare is null for one row
Cabin null for most
Age null for some
"""
test_df.info() | code |
2000632/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"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'))
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Input, Dropout
from keras.utils import np_utils
from sklearn.model_selection import train_test_split | code |
2000632/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
model = Sequential()
model.add(Dense(X_train.shape[1], input_shape=(784,), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(y_train.shape[1], activation='softmax'))
model.compile(optimizer='adam', metrics=['accuracy'], loss='categorical_crossentropy')
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=100) | code |
89123618/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns | code |
89123618/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.isnull().sum() | code |
89123618/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['BMI', 'PatientID']
given_data.drop(columns=drop_cols, inplace=True)
test = test_data.drop(columns=drop_cols)
sns.countplot(given_data.PhysHlth) | code |
89123618/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 |
89123618/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
test_data.isnull().sum() | code |
89123618/cell_16 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train) | code |
89123618/cell_17 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
89123618/cell_14 | [
"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)
import seaborn as sns
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['BMI', 'PatientID']
given_data.drop(columns=drop_cols, inplace=True)
test = test_data.drop(columns=drop_cols)
y = given_data['target']
X = given_data.drop(columns=['target'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
print(len(X_train), len(X_test)) | code |
89123618/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['BMI', 'PatientID']
given_data.drop(columns=drop_cols, inplace=True)
test = test_data.drop(columns=drop_cols)
given_data.hist(figsize=(20, 18)) | code |
89123618/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.isnull().sum()
test_data.isnull().sum()
drop_cols = ['BMI', 'PatientID']
given_data.drop(columns=drop_cols, inplace=True)
test = test_data.drop(columns=drop_cols)
sns.heatmap(given_data.corr(), annot=True) | code |
89123618/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
given_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/train.csv')
test_data = pd.read_csv('/kaggle/input/ml-olympiad-good-health-and-well-being/test.csv')
given_data.columns
given_data.head() | code |
1006492/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, axis1 = plt.subplots(1, 1, figsize=(15, 4))
sns.countplot(x='Open', hue='DayOfWeek', data=df_train) | code |
1006492/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x ='Month', y ='Sales', data = df_train, ax=axis1)
sns.barplot(x ='Month', y ='Customers', data = df_train, ax=axis2)
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='DayOfWeek', y='Sales', data = df_train, order = [1,2,3,4,5,6,7], ax = axis1)
sns.barplot(x='DayOfWeek', y='Customers', data = df_train, order = [1,2,3,4,5,6,7], ax = axis2)
df_train.StateHoliday.unique()
df_train['StateHoliday'] = df_train['StateHoliday'].replace(0, '0')
df_train.StateHoliday.unique()
sns.factorplot(x='Year', y='Sales', hue='StateHoliday', data=df_train, size=6, kind='bar', palette='muted') | code |
1006492/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x ='Month', y ='Sales', data = df_train, ax=axis1)
sns.barplot(x ='Month', y ='Customers', data = df_train, ax=axis2)
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='DayOfWeek', y='Sales', data = df_train, order = [1,2,3,4,5,6,7], ax = axis1)
sns.barplot(x='DayOfWeek', y='Customers', data = df_train, order = [1,2,3,4,5,6,7], ax = axis2)
df_train.StateHoliday.unique()
df_train['StateHoliday'] = df_train['StateHoliday'].replace(0, '0')
df_train.StateHoliday.unique()
df_train['HolidayBin'] = df_train['StateHoliday'].map({'0': 0, 'a': 1, 'b': 1, 'c': 1})
df_train.HolidayBin.unique()
sns.factorplot(x='Month', y='Sales', hue='HolidayBin', data=df_train, size=6, kind='bar', palette='muted') | code |
1006492/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1, axis2) = plt.subplots(2, 1, sharex=True, figsize=(15, 8))
average_monthly_sales = df_train.groupby('Month')['Sales'].mean()
ax1 = average_monthly_sales.plot(legend=False, ax=axis1, marker='o', title='Avg. Monthly Sales')
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')['Sales'].sum().pct_change()
ax2 = average_monthly_sales_change.plot(legend=False, ax=axis2, marker='o', colormap='summer', title='% Change Monthly Sales') | code |
1006492/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x ='Month', y ='Sales', data = df_train, ax=axis1)
sns.barplot(x ='Month', y ='Customers', data = df_train, ax=axis2)
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='DayOfWeek', y='Sales', data = df_train, order = [1,2,3,4,5,6,7], ax = axis1)
sns.barplot(x='DayOfWeek', y='Customers', data = df_train, order = [1,2,3,4,5,6,7], ax = axis2)
sns.factorplot(x='Year', y='Sales', hue='Promo', data=df_train, size=6, kind='box', palette='muted') | code |
1006492/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train.Year.head() | code |
1006492/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_store.head() | code |
1006492/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')['Customers'].mean()
fig = plt.subplots(1, 1, sharex=True, figsize=(25, 8))
average_daily_visits.plot(title='Average Daily Visits') | code |
1006492/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
df_train.StateHoliday.unique()
df_train['StateHoliday'] = df_train['StateHoliday'].replace(0, '0')
df_train.StateHoliday.unique() | code |
1006492/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')['Sales'].mean()
fig = plt.subplots(1, 1, sharex=True, figsize=(25, 8))
average_daily_sales.plot(title='Average Daily Sales') | code |
1006492/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv') | code |
1006492/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
df_train.StateHoliday.unique()
df_train['StateHoliday'] = df_train['StateHoliday'].replace(0, '0')
df_train.StateHoliday.unique()
df_train['HolidayBin'] = df_train['StateHoliday'].map({'0': 0, 'a': 1, 'b': 1, 'c': 1})
df_train.HolidayBin.unique() | code |
1006492/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x ='Month', y ='Sales', data = df_train, ax=axis1)
sns.barplot(x ='Month', y ='Customers', data = df_train, ax=axis2)
fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(15, 4))
sns.barplot(x='DayOfWeek', y='Sales', data=df_train, order=[1, 2, 3, 4, 5, 6, 7], ax=axis1)
sns.barplot(x='DayOfWeek', y='Customers', data=df_train, order=[1, 2, 3, 4, 5, 6, 7], ax=axis2) | code |
1006492/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')['Sales'].mean()
fig = plt.subplots(1, 1, sharex=True, figsize=(10, 5))
average_monthly_sales.plot(legend=True, marker='o', title='Average Sales') | code |
1006492/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
fig, (axis1, axis2) = plt.subplots(1, 2, figsize=(15, 4))
sns.barplot(x='Month', y='Sales', data=df_train, ax=axis1)
sns.barplot(x='Month', y='Customers', data=df_train, ax=axis2) | code |
1006492/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x = 'Open', hue = 'DayOfWeek', data = df_train,)
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(10,5))
average_monthly_sales.plot(legend=True,marker='o',title="Average Sales")
average_daily_sales = df_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_sales.plot(title="Average Daily Sales")
average_daily_visits = df_train.groupby('Date')["Customers"].mean()
fig = plt.subplots(1,1,sharex=True,figsize=(25,8))
average_daily_visits.plot(title="Average Daily Visits")
fig, (axis1,axis2) = plt.subplots(2,1,sharex=True,figsize=(15,8))
average_monthly_sales = df_train.groupby('Month')["Sales"].mean()
# plot average sales over time (year-month)
ax1 = average_monthly_sales.plot(legend = False, ax = axis1, marker = 'o',
title = "Avg. Monthly Sales")
ax1.set_xticks(range(len(average_monthly_sales)))
ax1.set_xticklabels(average_monthly_sales.index.tolist(), rotation=90)
average_monthly_sales_change = df_train.groupby('Month')["Sales"].sum().pct_change()
# plot precent change for sales over time(year-month)
ax2 = average_monthly_sales_change.plot(legend = False, ax = axis2, marker = 'o',
colormap = "summer", title = "% Change Monthly Sales")
df_train.StateHoliday.unique() | code |
1006492/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
df_train.Month.head() | code |
1006492/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_store = pd.read_csv('../input/store.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
332880/cell_4 | [
"text_plain_output_1.png"
] | from Bio import pairwise2
alignments = pairwise2.align.globalxx('ACCGT', 'ACG')
for a in pairwise2.align.globalxx('ACCGT', 'ACG'):
print(pairwise2.format_alignment(*a)) | code |
332880/cell_6 | [
"text_plain_output_1.png"
] | from Bio import pairwise2
alignments = pairwise2.align.globalxx('ACCGT', 'ACG')
for a in pairwise2.align.globalmx('ACCGT', 'ACG', 2, -1):
print(pairwise2.format_alignment(*a)) | code |
332880/cell_11 | [
"text_plain_output_1.png"
] | from Bio import SeqIO
from Bio import SeqIO
count = 0
sequences = []
for seq_record in SeqIO.parse('../input/genome.fa', 'fasta'):
if count < 6:
sequences.append(seq_record)
count = count + 1
chr2L = sequences[0].seq
chr2R = sequences[1].seq
chr3L = sequences[2].seq
chr3R = sequences[3].seq
chr4 = sequences[4].seq
chrM = sequences[5].seq
count = 0
mrna_sequences = []
for seq_record in SeqIO.parse('../input/mrna-genbank.fa', 'fasta'):
if count < 6:
mrna_sequences.append(seq_record)
print('Id: ' + seq_record.id + ' \t ' + 'Length: ' + str('{:,d}'.format(len(seq_record))))
print(repr(seq_record.seq) + '\n')
count = count + 1
mRNA1 = mrna_sequences[0].seq
mRNA2 = mrna_sequences[1].seq
mRNA3 = mrna_sequences[2].seq
mRNA4 = mrna_sequences[3].seq
mRNA5 = mrna_sequences[4].seq
mRNA1 = sequences[5].seq | code |
332880/cell_7 | [
"text_plain_output_1.png"
] | from Bio import pairwise2
alignments = pairwise2.align.globalxx('ACCGT', 'ACG')
for a in pairwise2.align.globalms('ACCGT', 'ACG', 2, -1, -0.5, -0.1):
print(pairwise2.format_alignment(*a)) | code |
332880/cell_8 | [
"text_plain_output_1.png"
] | from Bio import pairwise2
alignments = pairwise2.align.globalxx('ACCGT', 'ACG')
from Bio.SubsMat import MatrixInfo as matlist
matrix = matlist.blosum62
for a in pairwise2.align.globaldx('KEVLA', 'EVL', matrix):
print(pairwise2.format_alignment(*a)) | code |
332880/cell_10 | [
"text_plain_output_1.png"
] | from Bio import SeqIO
from Bio import SeqIO
count = 0
sequences = []
for seq_record in SeqIO.parse('../input/genome.fa', 'fasta'):
if count < 6:
sequences.append(seq_record)
print('Id: ' + seq_record.id + ' \t ' + 'Length: ' + str('{:,d}'.format(len(seq_record))))
print(repr(seq_record.seq) + '\n')
count = count + 1
chr2L = sequences[0].seq
chr2R = sequences[1].seq
chr3L = sequences[2].seq
chr3R = sequences[3].seq
chr4 = sequences[4].seq
chrM = sequences[5].seq | code |
332880/cell_5 | [
"text_plain_output_1.png"
] | from Bio import pairwise2
alignments = pairwise2.align.globalxx('ACCGT', 'ACG')
for a in pairwise2.align.localxx('ACCGT', 'ACG'):
print(pairwise2.format_alignment(*a)) | code |
104115847/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd #Data manipulation
path = '../input/'
df = pd.read_csv(path + 'insurance.csv')
print('\nNumber of rows and columns in the data set: ', df.shape)
print('')
df.head() | code |
18112276/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
train = train_df.drop('label', axis=1)
target = train_df['label']
sns.countplot(target) | code |
18112276/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
train = train_df.drop('label', axis=1)
target = train_df['label']
train.head() | code |
18112276/cell_6 | [
"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.head() | code |
18112276/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
18112276/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.head() | code |
18112276/cell_3 | [
"text_html_output_1.png"
] | import keras
import keras
from keras import utils
from keras import models
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras import optimizers
print('Keras version: {}'.format(keras.__version__)) | code |
18112276/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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 = train_df.drop('label', axis=1)
target = train_df['label']
target.head() | code |
18112276/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('../input/train.csv')
train = train_df.drop('label', axis=1)
target = train_df['label']
sns.distplot(target) | code |
1008978/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.0
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')
plt.tight_layout()
def show_data(cm, title='Confusion matrix', print_res=0):
tp = cm[1, 1]
fn = cm[1, 0]
fp = cm[0, 1]
tn = cm[0, 0]
return (tp / (tp + fp), tp / (tp + fn), fp / (fp + tn))
df = pd.read_csv('..input/creditcard.csv')
print(df.head(3))
y = np.array(df.Class.tolist())
df = df.drop('Class', 1)
df = df.drop('Time', 1)
df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))
X = np.array(df.as_matrix()) | code |
2017559/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file = pd.read_csv('../input/train.csv')
test_file = pd.read_csv('../input/test.csv')
train_features = train_file[['Pclass', 'Age', 'Sex']].values
target = train_file['Survived'].values
model = DecisionTreeClassifier()
x_train, x_test, y_train, y_test = train_test_split(train_features, target)
model.fit(x_train, y_train)
y_predict = model.predict(x_test)
test_features = test_file[['Pclass', 'Age', 'Sex']].values
test_answer = model.predict(test_features)
PassengerId = np.array(test_file['PassengerId']).astype(int)
solution = pd.DataFrame(test_answer, PassengerId, columns=['Survived'])
solution.shape | code |
2017559/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file = pd.read_csv('../input/train.csv')
test_file = pd.read_csv('../input/test.csv')
train_features = train_file[['Pclass', 'Age', 'Sex']].values
target = train_file['Survived'].values
model = DecisionTreeClassifier()
x_train, x_test, y_train, y_test = train_test_split(train_features, target)
model.fit(x_train, y_train)
y_predict = model.predict(x_test)
accuracy_score(y_test, y_predict) | code |
2017559/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score | code |
2017559/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_file = pd.read_csv('../input/train.csv')
test_file = pd.read_csv('../input/test.csv')
print(train_file.columns)
print(test_file.columns) | code |
129021770/cell_9 | [
"text_html_output_1.png"
] | from catboost import CatBoostClassifier
import pickle
import torch
test_X = pickle.load(open('/kaggle/input/embedder/test_embedding', 'rb'))
multilayerperceptron = torch.load('/kaggle/input/nlptrain/mlp.pt', map_location=torch.device('cpu'))
test_X_tensor = torch.tensor(test_X)
multilayerperceptronprediction_proba = multilayerperceptron(test_X_tensor).detach().numpy()
catboostclassifier = CatBoostClassifier().load_model('/kaggle/input/nlptrain/catboost_model.bin')
catboostclassifierprediction_proba = catboostclassifier.predict_proba(test_X)
prediction_proba = multilayerperceptronprediction_proba + catboostclassifierprediction_proba
prediction = prediction_proba.argmax(1)
((multilayerperceptronprediction_proba.argmax(1) == prediction).mean(), (catboostclassifierprediction_proba.argmax(1) == prediction).mean()) | code |
129021770/cell_4 | [
"text_plain_output_1.png"
] | import pickle
import torch
test_X = pickle.load(open('/kaggle/input/embedder/test_embedding', 'rb'))
multilayerperceptron = torch.load('/kaggle/input/nlptrain/mlp.pt', map_location=torch.device('cpu'))
test_X_tensor = torch.tensor(test_X)
multilayerperceptronprediction_proba = multilayerperceptron(test_X_tensor).detach().numpy() | code |
129021770/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import torch
import catboost
import pickle
from catboost import CatBoostClassifier | code |
129021770/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from catboost import CatBoostClassifier
import pickle
import torch
test_X = pickle.load(open('/kaggle/input/embedder/test_embedding', 'rb'))
multilayerperceptron = torch.load('/kaggle/input/nlptrain/mlp.pt', map_location=torch.device('cpu'))
test_X_tensor = torch.tensor(test_X)
multilayerperceptronprediction_proba = multilayerperceptron(test_X_tensor).detach().numpy()
catboostclassifier = CatBoostClassifier().load_model('/kaggle/input/nlptrain/catboost_model.bin')
catboostclassifierprediction_proba = catboostclassifier.predict_proba(test_X)
prediction_proba = multilayerperceptronprediction_proba + catboostclassifierprediction_proba
prediction = prediction_proba.argmax(1)
prediction.mean() | code |
129021770/cell_12 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
import torch
test_X = pickle.load(open('/kaggle/input/embedder/test_embedding', 'rb'))
multilayerperceptron = torch.load('/kaggle/input/nlptrain/mlp.pt', map_location=torch.device('cpu'))
test_X_tensor = torch.tensor(test_X)
multilayerperceptronprediction_proba = multilayerperceptron(test_X_tensor).detach().numpy()
catboostclassifier = CatBoostClassifier().load_model('/kaggle/input/nlptrain/catboost_model.bin')
catboostclassifierprediction_proba = catboostclassifier.predict_proba(test_X)
prediction_proba = multilayerperceptronprediction_proba + catboostclassifierprediction_proba
prediction = prediction_proba.argmax(1)
prediction.mean()
submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
submission.target = prediction
submission.to_csv('submission.csv', index=False)
submission | code |
105192343/cell_6 | [
"text_plain_output_1.png"
] | sale_of_store1 = 123
sale_of_store2 = 456
differece_of_sale = sale_of_store1 + sale_of_store2
print('differece of sale', differece_of_sale) | code |
105192343/cell_8 | [
"text_plain_output_1.png"
] | average_coffee_sold = 128
no_of_branches = 56
total_coffee_sold = average_coffee_sold * no_of_branches
print('total coffee sold', total_coffee_sold) | code |
105192343/cell_16 | [
"text_plain_output_1.png"
] | r = 19
s = 45
t = r ** 45
print(t) | code |
105192343/cell_3 | [
"text_plain_output_1.png"
] | No_of_books_store1 = 100
No_of_books_store2 = 200
total_count_of_books = No_of_books_store1 + No_of_books_store2
print('total count of books is', total_count_of_books) | code |
105192343/cell_14 | [
"text_plain_output_1.png"
] | total_apple = 5890
no_of_people = 70
no_of_apple_to_each = total_apple / no_of_people
total_apple = 5890
no_of_people = 70
no_of_apple_reminded = total_apple % no_of_people
total_apple = 5890
no_of_people = 70
no_of_apple_to_each = total_apple // no_of_people
print('no of apple to each is', no_of_apple_to_each) | code |
105192343/cell_10 | [
"text_plain_output_1.png"
] | total_apple = 5890
no_of_people = 70
no_of_apple_to_each = total_apple / no_of_people
print('no of apple to each is', no_of_apple_to_each) | code |
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