path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122249691/cell_14 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.go... | code |
122249691/cell_10 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flowe... | code |
122249691/cell_5 | [
"image_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('r... | code |
90142598/cell_6 | [
"text_plain_output_1.png"
] | from keras.layers.core import Dense
from keras.layers.core import Dense
from keras.layers.core import Dense
from keras.models import Sequential
from keras.models import Sequential
from keras.models import Sequential
import numpy as np
import numpy as np
import numpy as np
import numpy as np # linear algebra
i... | code |
90142598/cell_3 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers.core import Dense
from keras.models import Sequential
import numpy as np
import numpy as np # linear algebra
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
training_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], 'float32')
target_data = np.array([[0],... | code |
90142598/cell_5 | [
"text_plain_output_1.png"
] | from keras.layers.core import Dense
from keras.layers.core import Dense
from keras.models import Sequential
from keras.models import Sequential
import numpy as np
import numpy as np
import numpy as np # linear algebra
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
trai... | code |
18116047/cell_13 | [
"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 scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.figure(figsize=(20, 10))
sns.countplot(x='a... | code |
18116047/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.describe(hosp.age) | code |
18116047/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes | code |
18116047/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp.head(5) | code |
18116047/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.info() | code |
18116047/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy as scipy
from scipy import stats
import os
print(os.listdir('../input')) | code |
18116047/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['AdmitDiagnosis'].unique().shape | code |
18116047/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
hosp['age'].unique().shape | code |
18116047/cell_15 | [
"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 scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
scipy.stats.chisquare(... | code |
18116047/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.describe() | code |
18116047/cell_14 | [
"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 scipy as scipy
import seaborn as sns
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum()
plt.xticks(rotation=90)
plt.figure(figsize=(20... | code |
18116047/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age) | code |
18116047/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy as scipy
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape
scipy.stats.kurtosis(hosp.age)
hosp.isnull().sum() | code |
18116047/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
hosp = pd.read_csv('../input/mimic3d.csv')
hosp.dtypes
hosp.shape | code |
1005822/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import ma... | code |
1005822/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any() | code |
1005822/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
sns.heatmap(df.corr(), vmax=0.8, square=True, annot=True, fmt='.2f') | code |
1005822/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import numpy as np
import pandas as pd
import seaborn as ... | code |
1005822/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fi... | code |
1005822/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/HR_comma_sep.csv')
df.describe() | code |
1005822/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import ma... | code |
1005822/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
1005822/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fi... | code |
1005822/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_val_score
from sklearn.tree import DecisionTreeRegressor
import nu... | code |
128000273/cell_42 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pickle
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/... | code |
128000273/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
dtrain.describe() | code |
128000273/cell_9 | [
"image_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum() | code |
128000273/cell_34 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pickle
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/... | code |
128000273/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
le = preprocessing.LabelEncoder()
dtrain['VIP'] = le.fit_transform(dtrain['VIP'])
oe = preprocessing.OneHotEnco... | code |
128000273/cell_44 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pickle
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/... | code |
128000273/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
print(dtrain['Destination'].unique()) | code |
128000273/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtest.head() | code |
128000273/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
dtrain.info() | code |
128000273/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
def func(pct, allvals):
absolute = int(np.round(pct / 100.0 * np.sum(allvals)))
retur... | code |
128000273/cell_45 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
import seaborn as sns
dtrain = pd.read_csv... | code |
128000273/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
def func(pct, allvals):
absolute = int(np.round(pct / 100.0 * np.sum(allvals)))
retur... | code |
128000273/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pickle
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().... | code |
128000273/cell_8 | [
"image_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
print('Shape of train data: ', dtrain.shape)
print('Shape of test data: ', dtest.shape) | code |
128000273/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
def func(pct, allvals):
absolute = int(np.round(pct / 100.0 * np.sum(allvals)))
retur... | code |
128000273/cell_38 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import pickle
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna... | code |
128000273/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128000273/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
def func(pct, allvals):
absolute = int(np.round(pct / 100.0 * np.sum(allvals)))
retur... | code |
128000273/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtrain.isna().sum()
dtest.isna().sum()
le = preprocessing.LabelEncoder()
dtrain['VIP'] = le.fit_transform(dtrain['VIP'])
oe = prep... | code |
128000273/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtest.isna().sum()
dtest.describe() | code |
128000273/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtest.isna().sum() | code |
128000273/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtest = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
dtest.isna().sum()
dtest.info() | code |
128000273/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dtrain = pd.read_csv('/kaggle/input/spaceship-titanic/train.csv')
dtrain.head() | code |
1007003/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | TRAIN_PATH = '../input/train.csv'
TEST_PATH = '../input/test.csv'
train = pandas.read_csv(TRAIN_PATH)
test = pandas.read_csv(TEST_PATH) | code |
33115465/cell_5 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"image_output_17.png",
"text_plain_output_9.png",
"image_output_14.png",
"image_output_23.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.pn... | from matplotlib.ticker import MaxNLocator
from scipy.stats import gaussian_kde
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
import matplotlib.cm as cm
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
... | code |
129033410/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)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/cell_4 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.head() | code |
129033410/cell_33 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression()
log_reg.fit(x_train, y_train) | code |
129033410/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape | code |
129033410/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
129033410/cell_26 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nuni... | code |
129033410/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.rea... | code |
129033410/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/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 |
129033410/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.info() | code |
129033410/cell_45 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import chi2, SelectKBest
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g... | code |
129033410/cell_18 | [
"text_plain_output_1.png",
"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
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isF... | code |
129033410/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True) | code |
129033410/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
log_reg = LogisticRegression()
log_reg.fit(x_train, y_train)
y_pred = log_reg.predict(x_test)
print(classification_report(y_test, y_pred)) | code |
129033410/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/cell_35 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
log_reg = LogisticRegression()
log_reg.fit(x_train, y_train)
y_pred = log_reg.predict(x_test)
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
co... | code |
129033410/cell_24 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nuni... | code |
129033410/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)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [featu... | code |
129033410/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nuni... | code |
129033410/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)
dataset = pd.read_csv('../input/financial-dataset-for-fraud-detection-in-a-comapny/Fraud.csv')
dataset.shape
dataset.nunique().sort_values(ascending=True)
target = 'isFraud'
features = [feature for feature in dataset.columns... | code |
129033410/cell_12 | [
"text_plain_output_1.png"
] | import seaborn as sns | code |
129033410/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
log_reg = LogisticRegression()
log_reg.fit(x_train, y_train)
y_pred = log_reg.predict(x_test)
accuracy_score(y_test, y_pred) | code |
104120688/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('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2)
round(df.isna().sum(... | code |
104120688/cell_9 | [
"application_vnd.jupyter.stderr_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('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df | code |
104120688/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104120688/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104120688/cell_11 | [
"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('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isna().sum() / len(df.index) * 100, 2) | code |
104120688/cell_19 | [
"text_html_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104120688/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 |
104120688/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104120688/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum()
round(df.isn... | code |
104120688/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('../input/titanic/train.csv')
df
df.drop(['Pclass', 'Name', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1, inplace=True)
df.isna().sum() | code |
104120688/cell_5 | [
"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('../input/titanic/train.csv')
df | code |
18137853/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import matplotlib.patches as patches
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 numpy as np
import pandas as pd
import os, os.path
from xml.etree import ElementTree as ET
def parse_annotati... | code |
18137853/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, os.path
print(os.listdir('../input')) | code |
18137853/cell_7 | [
"image_output_2.png",
"image_output_1.png"
] | from keras.optimizers import Adam
import tensorflow as tf
from keras.optimizers import Adam
from keras import backend as K
class AdamWithWeightnorm(Adam):
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = se... | code |
18137853/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from PIL import Image
from PIL import Image
from keras.initializers import RandomNormal
from keras.models import Model, Sequential
from keras.optimizers import Adam
from tqdm import tqdm, tqdm_notebook
import matplotlib.patches as patches
import matplotlib.patches as patches
import matplotlib.pyplot as plt
imp... | code |
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