path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128032771/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
128032771/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
128032771/cell_3 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import random
import os
import gc
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
import mat... | code |
128032771/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
128032771/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128032771/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age'], ax = axes[0][0])
sns.boxplot(y = train['Height'], ax = axes[0][1])
sns.boxplot(y = train['Weight'], ax = axes[0][2])
sns.boxplot(y = train['Duration'], ax = axes[1][0])
sns.boxplot(y ... | code |
128032771/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import random
import seaborn as sns
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
seed_everything(42)
fig, axes = plt.subplots(2,3, figsize = (10,10))
sns.boxplot(y = train['Age']... | code |
128032771/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(2, 3, figsize=(10, 10))
sns.boxplot(y=train['Age'], ax=axes[0][0])
sns.boxplot(y=train['Height'], ax=axes[0][1])
sns.boxplot(y=train['Weight'], ax=axes[0][2])
sns.boxplot(y=train['Duration'], ax=axes[1][0])
sns.boxplot(y=train['Heart_Rate'... | code |
72118116/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
df.fillna(df.mean(), inplace=True)
df['type'].unique() | code |
72118116/cell_29 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pa... | code |
72118116/cell_19 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum(... | code |
72118116/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 |
72118116/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape | code |
72118116/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pa... | code |
72118116/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
df.describe() | code |
72118116/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
df.fillna(df.mean(), inplace=True)
plt.figure(... | code |
72118116/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
df.fillna(df.mean(), inplace=True)
plt.figure(... | code |
72118116/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
df.fillna(df.mean(), inplace=True)
df['type'] ... | code |
72118116/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
df.fillna(df.mean(), inplace=True)
plt.figure(... | code |
72118116/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.shape
missing_val_count_by_column = df.isnull().sum()
print(missing_val_count_by_column[missing_val_count_by_column > 0]) | code |
72118116/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality/winequalityN.csv')
df.head() | code |
18115505/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
train_set[['Pclass', 'Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascendin... | code |
18115505/cell_9 | [
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | 342 * 100 / 891 | code |
18115505/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any() | code |
18115505/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.head() | code |
18115505/cell_11 | [
"text_plain_output_1.png"
] | 549 * 100 / 891 | code |
18115505/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_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts() | code |
18115505/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
train_set[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survi... | code |
18115505/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
pd.DataFrame(train_set['Survived'], index=train_set.Age).plot(kind='hist') | code |
18115505/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.info() | code |
18115505/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
train_set[['Sex', 'Survived']].groupby(['Sex']).mean().plot(kin... | code |
18115505/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
train_set[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survi... | code |
18115505/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
train_set.Survived.value_counts()
train_set[['Sex', 'Survived']].groupby(['Sex']).mean() | code |
18115505/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.isnull().any()
print('Total null entries:\n')
print('Age :%d\nCabin:%d\nEmbarked:%d' % (train_set.Age.isnull().sum(), train_set.Cabin.isnull().sum... | code |
327240/cell_21 | [
"image_output_1.png"
] | import pandas as ps
import pylab
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 193... | code |
327240/cell_25 | [
"text_html_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950,... | code |
327240/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
print(fileR.head()) | code |
327240/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib
import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
matplotlib.rcParams['figure.figsize'] = (10, 5)
ops = fileR['Operator'].value_counts()[:20]
ops.plot(kind='bar', legend='Operator', color='g', fontsize=10, title='Operators with Highest Crashes') | code |
327240/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950,... | code |
327240/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
types = fileR['Type'].value_counts()[:20]
types.plot(kind='bar', legend='Types', color='g', fontsize=10, title='Types with Highest Crashes') | code |
327240/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
fileR['day'] = fileR['Date'].dt.day
sub_years = [1900, 1910, 1920, 1930, 1940, 1950,... | code |
327240/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib
import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
matplotlib.rcParams['figure.figsize'] = (10, 5)
ops = fileR['Operator'].value_counts()[:20]
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month... | code |
327240/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as ps
import string
fileR = ps.read_csv('../input/3-Airplane_Crashes_Since_1908.txt', sep=',')
fileR['Date'] = ps.to_datetime(fileR['Date'])
fileR['year'] = fileR['Date'].dt.year
fileR['month'] = fileR['Date'].dt.month
file... | code |
2037064/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/all_energy_statistics.csv')
df.columns = ['country', 'commodity', 'year', 'unit', 'quantity', 'footnotes', 'category']
df_solar = df[df.commodity.str.contains('Electricity - total net installed capacity of electric pow... | code |
2037064/cell_7 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
df = pd.read_csv('../input/all_energy_statistics.csv')
df.columns = ['country', 'commodity', 'year', 'unit', 'quantity', 'footnotes', 'category']
df_solar = df[df.commodity.str.contains('Electricity - total net installed capacity of electric power plants, solar')]
df_max = df_s... | code |
2037064/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/all_energy_statistics.csv')
df.columns = ['country', 'commodity', 'year', 'unit', 'quantity', 'footnotes', 'category']
df_solar = df[df.commodity.str.contains('Electricity - total net installed capacity of electric power plants, solar')]
df_max = df_solar.groupby(pd.Grou... | code |
33102430/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.info() | code |
33102430/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.head() | code |
33102430/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/... | code |
33102430/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.describe() | code |
33102430/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns | code |
50219234/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import ShuffleSplit... | code |
50219234/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
concat_california_array = np.concatenate((california.data, np.reshape(california.target, (california.target.shape[0], 1))), axis=1)
california_df = pd.DataFrame(concat_california_array, columns=california.feature_names + ['price'])
plt.figure(fi... | code |
50219234/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | print(california.DESCR) | code |
50219234/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
concat_california_array = np.concatenate((california.data, np.reshape(california.target, (california.target.shape[0], 1))), axis=1)
california_df = pd.DataFrame(concat_california_array, columns=california.feature_names + ['price'])
california_df.head(3) | code |
50219234/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegresso... | code |
50219234/cell_48 | [
"image_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import RobustScaler
from xgboost import XGBRegressor
import numpy as np
import numpy as np
import pandas as pd
conc... | code |
50219234/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
concat_california_array = np.concatenate((california.data, np.reshape(california.target, (california.target.shape[0], 1))), axis=1)
california_df = pd.DataFrame(concat_california_array, columns=california.feature_names + ['... | code |
50219234/cell_45 | [
"image_output_1.png"
] | from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
... | code |
50219234/cell_32 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import RobustScaler
i... | code |
50219234/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
concat_california_array = np.concatenate((california.data, np.reshape(california.target, (california.target.shape[0], 1))), axis=1)
california_df = pd.DataFrame(concat_california_array, columns=california.feature_names + ['... | code |
50219234/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import RobustScaler
i... | code |
50219234/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSe... | code |
50219234/cell_27 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.preprocessing import RobustScaler
rs = RobustScaler()
X_train_rs = rs.fit_transform(X_train)
X_test_rs = rs.transform(X_test)
from skle... | code |
50219234/cell_37 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegresso... | code |
50219234/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
concat_california_array = np.concatenate((california.data, np.reshape(california.target, (california.target.shape[0], 1))), axis=1)
california_df = pd.DataFrame(concat_california_array, columns=california.feature_names + ['... | code |
50231668/cell_4 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50231668/cell_6 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50231668/cell_2 | [
"text_plain_output_1.png"
] | for num in range(1, 1001):
if num > 0:
for i in range(1000, num):
if num % i == 0:
break
else:
print(num) | code |
50231668/cell_7 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50231668/cell_8 | [
"text_plain_output_1.png"
] | def binary_search_recursive(array, element, start, end):
if start > end:
return -1
mid = (start + end) // 2
if element == array[mid]:
return mid
if element < array[mid]:
return binary_search_recursive(array, element, start, mid - 1)
else:
return binary_search_recursiv... | code |
50231668/cell_5 | [
"text_plain_output_1.png"
] | def linearsearch(arr, x):
for i in range(len(arr)):
if arr[i] == x:
return i
return -1
arr = ['10', '20', '30', '40', '50', '60', '70']
x = '50'
print('element nya ' + str(linearsearch(arr, x))) | code |
2041736/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
btc['ohlc_average'] = (btc['open'] + btc['high'] + btc['low'] + ... | code |
2041736/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
btc.tail() | code |
2041736/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
df.tail() | code |
2041736/cell_23 | [
"text_html_output_1.png"
] | from datetime import datetime, timedelta
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == ... | code |
2041736/cell_20 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200, random_state=101)
reg.fit(X_train, y_train)
accuracy = reg.score(X_test, y_test)
accuracy = accuracy * 100
accuracy = float('{0:.4f}'.format(accuracy))
preds = reg.predic... | code |
2041736/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True) | code |
2041736/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
sns.set(... | code |
2041736/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=200, random_state=101)
reg.fit(X_train, y_train)
accuracy = reg.score(X_test, y_test)
accuracy = accuracy * 100
accuracy = float('{0:.4f}'.format(accuracy))
print('Accuracy is:... | code |
2041736/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any() | code |
2041736/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import cross_validation
from sklearn import preprocessing
import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
bt... | code |
2041736/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape | code |
2041736/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
btc['Price_After_Month'] = btc['close'].shift(-30) | code |
2041736/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
btc.tail() | code |
2041736/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
df.head() | code |
2041736/cell_17 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
from sklearn import preproces... | code |
2041736/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', 'market'], axis=1, inplace=True)
btc.isnull().any()
btc.shape
btc.head() | code |
2041736/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from datetime import datetime, timedelta
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor
import pandas as pd
df = pd.read_csv('../input/crypto-markets.csv', parse_dates=['date'], index_col='date')
btc = df[df['symbol'] == 'BTC']
btc.drop(['volume', 'symbol', 'name', 'ranknow', ... | code |
17118879/cell_21 | [
"text_plain_output_1.png"
] | import pathlib
import random
import tensorflow as tf
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0]
all_paths = [str(path) for path i... | code |
17118879/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df.info() | code |
17118879/cell_23 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_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')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214']
classes_dist = pd.DataFrame(train_df['di... | code |
17118879/cell_20 | [
"text_plain_output_1.png"
] | import pathlib
import random
import tensorflow as tf
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.png'))
all_paths[0]
all_paths = [str(path) for path i... | code |
17118879/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pathlib
import random
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv... | code |
17118879/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pathlib
import random
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.... | code |
17118879/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
os.getcwd() | code |
17118879/cell_11 | [
"text_plain_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')
test_df = pd.read_csv('../input/test.csv')
sample_sub_df = pd.read_csv('../input/train.csv')
train_df[train_df.id_code == '5d024177e214']
classes_dist = pd.DataFrame(train_df['di... | code |
17118879/cell_19 | [
"text_html_output_1.png"
] | from IPython.core.display import Image
from IPython.display import display
import pathlib
import random
train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
root_path = pathlib.Path(train_images_path)
for item in root_path.iterdir():
break
all_paths = list(root_path.glob('*.pn... | code |
17118879/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17118879/cell_7 | [
"text_plain_output_1.png"
] | train_images_path = '../input/train_images'
test_images_path = '../input/test_images'
print(train_images_path) | code |
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