path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128010282/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.head() | code |
128010282/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns | code |
128010282/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV, cross_validate, cross_val_score, cross_val_predict
from sklearn.linea... | code |
128010282/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr() | code |
128010282/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.describe() | code |
128010282/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
sns.barplot(Data) | code |
128010282/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
feature_name = list(Data.columns[:-1])
plt.figure(figsize=(30, 30))
for i in range(len(feature_name))... | code |
128010282/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
Data.hist(figsize=(30, 30)) | code |
128010282/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
feature_name = list(Data.columns[:-1])
Data.drop('NOX', ax... | code |
128010282/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
plt.figure(figsize=(5, 5))
plt.pie([Data.shape[0], Data.isnull().sum().sum()], labels=['Not_Null', 'Null'], autopct='%1.2f%%') | code |
128010282/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape
Data.columns
Data.isnull().sum().sum()
Data.corr()
sns.heatmap(Data.corr(), cmap='hot', annot=True) | code |
128010282/cell_5 | [
"image_output_1.png"
] | import pandas as pd
Data = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
Data.shape | code |
34151195/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
plt.tight_layout()
colnames = data.columns.values
plt.tight_layout()
data.plo... | code |
34151195/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
data.hist(figsize=(12, 10))
plt.tight_layout()
plt.show() | code |
34151195/cell_11 | [
"text_html_output_1.png"
] | from pandas.plotting import scatter_matrix
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 matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
... | code |
34151195/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 |
34151195/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
plt.tight_layout()
colnames = data.columns.values
data.plot(kind='density', fi... | code |
34151195/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
df.head() | code |
34151195/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
plt.tight_layout()
colnames = data.columns... | code |
34151195/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/pima-indians-diabetes-database/diabetes.csv')
data = df.iloc[:, 0:8]
data.head() | code |
128043001/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/classification-problem-uni/data_inlf_train.csv', encoding='gbk')
train.head() | code |
128043001/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('/kaggle/input/classification-problem-uni/data_inlf_train.csv', encoding='gbk')
y = train['inlf'].values
train.drop(['inlf'], axis=1, inplace=True)
x = train.values
scaler = ... | code |
128043001/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('/kaggle/input/classification-problem-uni/data_inlf_train.csv', encoding='gbk')
y = train['inlf'].values
train.drop(['inlf'], axis=... | code |
128043001/cell_12 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('/kaggle/input/classification-problem-uni/data_inlf_train.csv', encoding='gbk')
y = train['inlf'].values
train.drop(['inlf'], axis=... | code |
18153363/cell_25 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn import metrics
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClass... | code |
18153363/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(... | code |
18153363/cell_23 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn import metrics
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClass... | code |
18153363/cell_33 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
fro... | code |
18153363/cell_29 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
fro... | code |
18153363/cell_11 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt # Matlab-style plotting
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.of... | code |
18153363/cell_19 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt # Matlab-style plotting
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.of... | code |
18153363/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(... | code |
18153363/cell_7 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(... | code |
18153363/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt # Matlab-style plotting
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.of... | code |
18153363/cell_28 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
fro... | code |
18153363/cell_8 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(... | code |
18153363/cell_3 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(... | code |
18153363/cell_22 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
fro... | code |
18153363/cell_27 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
fro... | code |
18153363/cell_12 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt # Matlab-style plotting
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.of... | code |
18153363/cell_5 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import matplotlib.pyplot as plt # Matlab-style plotting
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly.of... | code |
122245079/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Co... | code |
122245079/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Converter(string):
lis = string.split()
res = [eva... | code |
122245079/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum() | code |
122245079/cell_6 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns | code |
122245079/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 |
122245079/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Co... | code |
122245079/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Converter(string):
lis = string.split()
res = [eva... | code |
122245079/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Co... | code |
122245079/cell_3 | [
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.head(10) | code |
122245079/cell_24 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Co... | code |
122245079/cell_12 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.columns
def Price_Converter(string):
lis = string.split()
res = [eva... | code |
122245079/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
df = pd.read_csv(os.path.join(dirname, filename))
df.isnull().sum()
df.info() | code |
106194409/cell_21 | [
"image_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.describe(include='object').round().T
df2.head() | code |
106194409/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
df2.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2... | code |
106194409/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.describe(include='object').round().T | code |
106194409/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.info() | code |
106194409/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.impute import KNNImputer
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')... | code |
106194409/cell_20 | [
"image_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.describe(include='object').round().T
df2.info() | code |
106194409/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe() | code |
106194409/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.head() | code |
106194409/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
print('Percentage of missing data per feature:\n')
round(df1.isna().sum() / df1.shape[0], 2) | code |
106194409/cell_19 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2)
df1.head() | code |
106194409/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.describe() | code |
106194409/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2)
df1.info() | code |
106194409/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T | code |
106194409/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2)
print('Statistical Distribution of Passengers NOT in CryoSleep\n')
round(df1[df1['CryoSleep'] == F... | code |
106194409/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2)
df1[df1['CryoSleep'] == True][['Room Service', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck']] = 0.... | code |
106194409/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.head() | code |
106194409/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2)
print('Statistical Distribution of Passengers in CryoSleep\n')
round(df1[df1['CryoSleep'] == True]... | code |
106194409/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
df2.describe(include='object').round().T
for df in [df1, df2]:
df.isna().mean().plot(kind='barh', figsi... | code |
106194409/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import missingno as msno
import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df1.describe(include='object').round().T
df2.describe(include='object').round().T
round(df1.isna().sum() / df1.shape[0], 2... | code |
106194409/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df1 = pd.read_csv('../input/spaceship-titanic/train.csv')
df2 = pd.read_csv('../input/spaceship-titanic/test.csv')
df2.info() | code |
128047896/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_nrow_now
lost = l... | code |
128047896/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_nrow_now
lost = l... | code |
128047896/cell_30 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = w... | code |
128047896/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_nrow_now
lost = l... | code |
128047896/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_nrow_now
lost = l... | code |
128047896/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = w... | code |
128047896/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_nrow_now
lost = l... | code |
128047896/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp
wp_nrow = wp.shape[0]
def lost_record():
wp_nrow_now = wp.shape[0]
lost = wp_nrow - wp_n... | code |
128047896/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
wp = pd.read_csv('https://raw.githubusercontent.com/kunal-mallick/Kaggle-Project/Working/Water%20Quality(Drinking%20Water%20Potability)/src/main/resources/water_potability.csv')
wp | code |
328147/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarget = LabelEncoder().fit(gatrain.gr... | code |
328147/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
phone.head(3) | code |
328147/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss | code |
328147/cell_18 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarge... | code |
328147/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
gatrain.head(3) | code |
328147/cell_17 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
letarge... | code |
128017162/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matp... | code |
128017162/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
train['Class'].value_counts() | code |
128017162/cell_25 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pand... | code |
128017162/cell_23 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackere... | code |
128017162/cell_30 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import nump... | code |
128017162/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
preds_list = test['Image']
preds_list
preds_list | code |
128017162/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
train.head(5) | code |
128017162/cell_11 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd
train_dir = '/kaggle/input/hackerearth/dataset/Train Images'
test_dir = '/kaggle/input/hackerearth/dataset/Test Images'
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
datagen = ImageDataGenerator(rescale=1.0 ... | code |
128017162/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping, Callback
from tensorflow.keras.layers import Dropout, Dense, Activation, Flatten, Conv2D, MaxPool2D, BatchNormalization
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pand... | code |
128017162/cell_1 | [
"text_plain_output_1.png"
] | # !pip install seaborn
# !pip install tensorflow
# !pip install keras
# # !pip install sklearn
!pip install visualkeras
# !pip install pydot
# !pip install opencv-python
# !pip install numpy
# !pip install pandas
# !pip install matplotlib
# !pip install tensorflow
# !pip install keras | code |
128017162/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
test.head(5) | code |
128017162/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/hackerearth/dataset/train.csv')
test = pd.read_csv('/kaggle/input/hackerearth/dataset/test.csv')
test | code |
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