path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
2040512/cell_10 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(daily_Data['Gender'])
daily_Data['Gender'] = le.transform(daily_Data['Gender'])
le.fit(daily_Data['No-show'])
daily_Data['No-show... | code |
2040512/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_Data = pd.read_csv('../input/KaggleV2-May-2016.csv')
print('Age:', sorted(daily_Data.Age.unique()))
print('Gender:', daily_Data.Gender.unique())
print('Neighbourhood', daily_Data.Neighbourhood.unique())
print('Scholarship:', daily_Data.Scholarship.unique())
print('Hipertension:', daily_Data.... | code |
89132381/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
discrete_feature = [feature for feature in feature_list if len(df[feature].unique()) < 25]
print('Discrete Variables Count: {}'.format(len(discrete_feature)))
print('Dis... | code |
89132381/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.describe() | code |
89132381/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input/'):
for filename in filenames:
print(os.path.join(dirname, filename))
print('Setup complete, packages loaded') | code |
89132381/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes | code |
89132381/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
print(df.isnull().any()) | code |
89132381/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(df.corr(),annot=True,ax=ax,cmap="Greys")
fig = plt.figure(fig... | code |
89132381/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10,8))
sns.heatmap(df.corr(),annot=True,ax=ax,cmap="Greys")
fig = plt.figure(fig... | code |
89132381/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
fig, ax = plt.subplots(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, ax=ax, cmap='Greys') | code |
89132381/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
df.dtypes
feature_list = [feature for feature in df.columns]
print('There are', len(feature_list), 'features found in the data') | code |
89132381/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/heart-disease-dataset/heart.csv')
print('Dataset has', df.shape[0], 'entries and', df.shape[1], 'variables') | code |
326886/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model impo... | code |
326886/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326886/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model impo... | code |
326886/cell_16 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.en... | code |
326886/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326886/cell_14 | [
"image_output_1.png"
] | from collections import Counter
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Coun... | code |
326886/cell_22 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from col... | code |
326886/cell_12 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.en... | code |
2017953/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_9 | [
"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)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted.he... | code |
2017953/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
print(medals.info())
medals.head() | code |
2017953/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
medal_counts = medals['NOC'].value_counts()
print('The total medals: %d' % medal_counts.sum())
print('\nTop 15 countries:\n', medal_counts.head(15)) | code |
2017953/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_28 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('tot... | code |
2017953/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_3 | [
"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 |
2017953/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
2017953/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('tot... | code |
2017953/cell_12 | [
"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)
medals = pd.read_csv('../input/.csv')
counted = medals.pivot_table(index='NOC', values='Athlete', columns='Medal', aggfunc='count')
counted['totals'] = counted.sum(axis='columns')
counted = counted.sort_values('totals', ascending=False)
counted =... | code |
73087956/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
av = pd.read_csv('../input/seti-adversarial-validation/oof.csv')
tmp = pd.read_csv('../input/seti-breakthrough-listen/train_labels.csv')
tmp['label'] = tmp['target']
av = pd.merge(av, tmp[['id', 'label']], on='id', how='left')
av.query('target == 0 and pred > 1e-4') | code |
73087956/cell_1 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/rwightman/pytorch-image-models
import timm | code |
73087956/cell_3 | [
"text_html_output_1.png"
] | import numpy as np
import os
import random
import torch
class CFG:
seed = 46
debug = False
n_fold = 4
n_epoch = 11
height = 480
width = 480
model_name = 'efficientnet_b0'
lr = 0.0001
min_lr = 1e-06
weight_decay = 0.0001
T_max = 10
device = torch.device('cuda' if torch... | code |
73087956/cell_5 | [
"text_plain_output_35.png",
"text_plain_output_43.png",
"text_plain_output_37.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_44.png",
"text_plain_output_40.png",
... | from sklearn.model_selection import StratifiedKFold
import numpy as np
import os
import pandas as pd
import random
import torch
class CFG:
seed = 46
debug = False
n_fold = 4
n_epoch = 11
height = 480
width = 480
model_name = 'efficientnet_b0'
lr = 0.0001
min_lr = 1e-06
weig... | code |
33097852/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-price... | code |
33097852/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
33097852/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/... | code |
33097852/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', header=0)
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', header=0)
sample_submission = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv',... | code |
2031298/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1['Survived'].value_counts().plot.bar(color='r')
plt.show()
df1['Pclass'].value_counts().plot.bar()
plt.show()
df1['Sex'].value_counts().plot.bar(color='g')
plt.show()
df1['SibSp'].val... | code |
2031298/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
corr = df1.corr()
freq_1 = pd.crosstab(index=df1['Survive... | code |
2031298/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
corr = df1.corr()
sns.heatmap(corr[['Age', 'Fare']], annot... | code |
2031298/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.figure()
df1.plot.scatter('Age', 'Fare')
plt.show() | code |
2031298/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.describe() | code |
2031298/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.head() | code |
2031298/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df2.head() | code |
2031298/cell_16 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.hist(df12.Age, alpha=0.3)
sns.rugplot(df12.Age)
plt.show() | code |
2031298/cell_17 | [
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
plt.hist(df12.Fare)
sns.rugplot(df12.Fare, alpha=0.3)
plt.show() | code |
2031298/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1[['Age', 'Fare']].describe()
df1['Age'].value_counts().plot.hist(grid=True, color='b', alpha=0.7)
plt.show()
df1['Fare'].value_counts().plot.hist(grid=True, color='r')
plt.show() | code |
2031298/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df12 = df1.fillna(df1.mean())
i = df1[['Age', 'Fare', 'Survived']]
plt.style.use('seaborn-colorblind')
pd.tools.plotting.scatter_matrix(i)
plt.show() | code |
2031298/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/train.csv')
df2 = pd.read_csv('../input/test.csv')
df1.describe(include=['O']) | code |
1006119/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
vec.get_feature_names() | code |
1006119/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_t... | code |
1006119/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_t... | code |
1006119/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 |
1006119/cell_7 | [
"application_vnd.jupyter.stderr_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)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = ... | code |
1006119/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_t... | code |
1006119/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_t... | code |
1006119/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
d... | code |
1006119/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv') | code |
1006119/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_train = pd.read_csv('train.csv')
data_test = pd.read_csv('test.csv')
data_x = data_train[['Pclass', 'Age', 'Sex']]
data_y = data_train['Survived']
age_mean = data_x.mean()['Age']
data_x = data_x.fillna(age_mean)
for i in range(1, 4):
data... | code |
33101666/cell_13 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_9 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_4 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5) | code |
33101666/cell_2 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch() | code |
33101666/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))
break | code |
33101666/cell_7 | [
"text_plain_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_15 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
data.show_batch() | code |
33101666/cell_17 | [
"text_plain_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_14 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
33101666/cell_12 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
learner2 = cnn_learner(data, models.resnet1... | code |
33101666/cell_5 | [
"image_output_1.png"
] | from fastai.vision import *
data = ImageList.from_folder('/kaggle/input/turkish-lira-banknote-dataset/').random_split_by_pct().label_from_folder().transform(get_transforms(), size=122).databunch()
learner = cnn_learner(data, models.resnet18, metrics=accuracy)
learner.fit(5)
interp = ClassificationInterpretation.from_... | code |
320604/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL... | code |
320604/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
320604/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL... | code |
320604/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.connect('../input/database.sqlite')
... | code |
320604/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import sqlite3
import matplotlib.pyplot as plt
import numpy as np
import sqlite3
from sklearn import tree
def sql_query(s):
"""Return results for a SQL query.
Arguments:
s (str) -- SQL query string
Returns:
(list) -- SQL query results
"""
conn = sqlite3.conn... | code |
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