path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
16150255/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import pandas as pd # For loading and processing the dataset
import torch
import torch.nn.functional as F
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket',... | code |
16150255/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import pandas as pd # For loading and processing the dataset
import torch
import torch.nn.functional as F
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket',... | code |
16150255/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], pref... | code |
48165864/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)] | code |
48165864/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.describe(include='all') | code |
48165864/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.info() | code |
48165864/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
48165864/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
port = df[['Port Name', 'Port Code']].drop_duplicates()
port[port['Port Name'].duplicated(keep=False)] | code |
48165864/cell_6 | [
"text_html_output_1.png"
] | import os
import seaborn as sns
import warnings
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib... | code |
48165864/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
48165864/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
48165864/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df | code |
48165864/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
48165864/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum() | code |
48165864/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
print('Unique values in Port Name: ' + str(df['Port Name'].nunique()))
print('Unique values in Port Code: ' + str(df['Port Code'].nunique())) | code |
48165864/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
48165864/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum() | code |
48165864/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date... | code |
16124388/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', '... | code |
16124388/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
print(dataset.keys())
print(len(dataset))
print(dataset.head()) | code |
16124388/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
dataset.describe().transpose() | code |
16124388/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1... | code |
16124388/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
print(dataset['Gender'].unique())
dataset['Gender_code'] = np.where(dataset['Gender'] == 'Male', 1, 0) | code |
16124388/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Ann... | code |
16124388/cell_16 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Ann... | code |
16124388/cell_17 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Ann... | code |
16124388/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1... | code |
130011352/cell_3 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import json
import os
source_list = set()
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_f... | code |
130011352/cell_5 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import json
import os
source_list = set()
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_f... | code |
1005853/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', label... | code |
1005853/cell_13 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data[train_data['Survived'] == 1]['Age'].isnull()) / len(train_data) | code |
1005853/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data['Pclass'].unique() | code |
1005853/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.describe() | code |
1005853/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', label... | code |
1005853/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', label... | code |
1005853/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.hist(train_data['Pclass'], color='lightblue')
plt.tick_params(top='off', bottom='on', ... | code |
1005853/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', label... | code |
1005853/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbott... | code |
1005853/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', label... | code |
1005853/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data[train_data['Survived'] == 0]['Age'].isnull()) / len(train_data) | code |
1005853/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(... | code |
1005853/cell_12 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data['Age'].isnull()) / len(train_data) | code |
1005853/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.head() | code |
2017164/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.dtypes | code |
2017164/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.r... | code |
2017164/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape | code |
2017164/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
test.shape
train.dtypes
test.dtypes
test.fillna('missing', inplace=True)
X = train.comment_text
... | code |
2017164/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.head() | code |
2017164/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 |
2017164/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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
sub.head() | code |
2017164/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
train.dtypes | code |
2017164/cell_15 | [
"text_plain_output_1.png"
] | import seaborn as sns
colormap = plt.cm.plasma
plt.figure(figsize=(8, 8))
plt.title('Correlation of features & targets', y=1.05, size=14)
sns.heatmap(data.astype(float).corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True) | code |
2017164/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape | code |
2017164/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.dtypes
test[test['comment_text'].isnull()] | code |
2017164/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
train.head() | code |
105218033/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pi... | code |
105218033/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = [... | code |
105218033/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection impor... | code |
105218033/cell_20 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pi... | code |
105218033/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEn... | code |
105218033/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = [... | code |
105218033/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEn... | code |
105218033/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = [... | code |
105218033/cell_17 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEn... | code |
105218033/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEnc... | code |
105218033/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
x_train.he... | code |
72097528/cell_9 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_co... | code |
72097528/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna(... | code |
72097528/cell_6 | [
"text_html_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)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() ... | code |
72097528/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 |
72097528/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv... | code |
72097528/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
train.describe() | code |
72097528/cell_17 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv',... | code |
72097528/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv... | code |
72097528/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_co... | code |
72097528/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_co... | code |
74060776/cell_9 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
ndf = df.copy()
ndf['Decision'] = ndf['Decision'].astype('category')
ndf['Decision'] = ndf['Decision'].cat.codes
sns.heatmap(ndf.... | code |
74060776/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.describe() | code |
74060776/cell_6 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
plt.figure(figsize=(12, 6))
sns.kdeplot(data=df[df['Decision'] == 'admit'], x='GMAT', shade=True, label='Admitted')
sns.kdeplot(d... | code |
74060776/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.head() | code |
74060776/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74060776/cell_7 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
plt.figure(figsize=(12, 6))
sns.kdeplot(data=df[df['Decision'] == 'admit'], x='GPA', shade=True, label='Admitted')
sns.kdeplot(da... | code |
74060776/cell_8 | [
"text_plain_output_1.png",
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
sns.jointplot(data=df, x='GPA', y='GMAT', hue='Decision') | code |
74060776/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sn... | code |
74060776/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.info() | code |
74060776/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # line... | code |
74060776/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
rfr = R... | code |
74060776/cell_10 | [
"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)
import seaborn as sns
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
ndf = df.copy()
ndf['Decision'] = ndf['Decision'].astype('category')
ndf['Decision'] = ndf['Decision'].cat.codes
ndf.head() | code |
74060776/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df['Decision'].unique() | code |
73061015/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / ... | code |
73061015/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
plt.figure(figsize=(8, 8))
for i in range(10):
plt.subplot(5, 5, i + 1)
plt.title(i)
plt.imshow(x_train[i].reshape(32, 32, 3)) | code |
73061015/cell_23 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_t... | code |
73061015/cell_26 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_t... | code |
73061015/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / ... | code |
73061015/cell_7 | [
"text_plain_output_1.png"
] | from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data() | code |
73061015/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import numpy as np
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.... | code |
73061015/cell_15 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / ... | code |
73061015/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / ... | code |
73061015/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float... | code |
73061015/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / ... | code |
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