path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17123567/cell_10 | [
"text_plain_output_1.png"
] | from google.cloud import bigquery
client = bigquery.Client()
dataset_ref = client.dataset('hacker_news', project='bigquery-public-data')
dataset = client.get_dataset(dataset_ref)
tables = list(client.list_tables(dataset))
table_ref = dataset_ref.table('full')
table = client.get_table(table_ref)
table.schema
clien... | code |
130010025/cell_11 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from lightgbm import LGBMClassifier
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing
import numpy as np
import os
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
class CFG:
HOME_DIR = '/kaggle/input/icr-ide... | code |
34120476/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePat... | code |
34120476/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | X_cv.shape | code |
34120476/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggl... | code |
34120476/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import LabelEncod... | code |
34120476/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.appen... | code |
34120476/cell_20 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
... | code |
34120476/cell_6 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
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
imagePaths = []
for dirname, _, filenames in os... | code |
34120476/cell_2 | [
"text_plain_output_1.png"
] | import cv2
import os
import keras
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from keras.preprocessing import image
from keras import models
from keras import layers
from keras import optimizers
from keras import applications
from keras.opti... | code |
34120476/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
... | code |
34120476/cell_8 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggl... | code |
34120476/cell_3 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
imagePaths.appen... | code |
34120476/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
X_train = X_train.reshape(X_train.shape[0], 3, img_width, img_height)
X_cv = X_cv.reshape(X_cv.shape[0], 3, img_width, img_height)
X_test = X_test.... | code |
34120476/cell_24 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggl... | code |
34120476/cell_14 | [
"text_plain_output_1.png"
] | X_test.shape | code |
34120476/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D,Conv2D,MaxPooling2D,BatchNormalization
from keras.models import Sequential, Model
X_train.shape
X_cv.shape
X_test.shape
img_width = img_height = 224
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
... | code |
34120476/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | X_train.shape | code |
34120476/cell_5 | [
"image_output_1.png"
] | from keras.preprocessing import image
from tqdm import tqdm
import cv2
import matplotlib.pyplot as plt
import os
import os
import numpy as np
import pandas as pd
import os
imagePaths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
if filename[-3:] == 'png':
... | code |
73093128/cell_21 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
def geo_short(location):
"""
Take address, cross-street, etc. and return geocoded point at which
lat/long can convenien... | code |
73093128/cell_23 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, she... | code |
73093128/cell_1 | [
"text_plain_output_1.png"
] | !pip install openpyxl | code |
73093128/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, sheet_name='data')
ramp_df.rename(columns={'Cross Street 1': 'CS_1', 'Cross Street 2': 'CS_2'}, inplace=True)
ramp_df.replace(' NE', 'NE', inplace=True)
ramp_df.Notes.fillna('None', inplace=True)
import math
d... | code |
73093128/cell_24 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, she... | code |
73093128/cell_22 | [
"text_html_output_1.png"
] | from geopandas.tools import geocode
import folium
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas.tools import geocode
import folium
agent = 'my_colls_app'
ramp_path = '../input/rampdatalog/RampDataLog.xlsm'
ramp_df = pd.read_excel(ramp_path, she... | code |
18161386/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
18161386/cell_3 | [
"image_output_11.png",
"text_plain_output_100.png",
"image_output_98.png",
"text_plain_output_201.png",
"text_plain_output_84.png",
"text_plain_output_56.png",
"text_plain_output_158.png",
"image_output_74.png",
"text_plain_output_181.png",
"text_plain_output_137.png",
"text_plain_output_139.png... | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data.csv')
data.info() | code |
18161386/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
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)
data = pd.read_csv('../input/data.csv')
data.drop(['id', 'Unnamed: 32'], axis=1, inplace=True)
x_data = data.drop(['diagnos... | code |
32070024/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/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 |
32070024/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean() | code |
32070024/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide.head() | code |
32070024/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist()
suicide.groupby('sex').suicide_rate.mean()
suicide.groupby('year... | code |
32070024/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
suicide = pd.read_csv('/kaggle/input/who-suicide-statistics/who_suicide_statistics.csv')
suicide['suicide_rate'] = suicide.suicides_no / suicide.population
suicide.columns.tolist() | code |
74052380/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_tes... | code |
74052380/cell_4 | [
"image_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('/kaggle/input/heart-disease-uci/heart.csv')
dataset.head() | code |
74052380/cell_23 | [
"image_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_tes... | code |
74052380/cell_19 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
cla... | code |
74052380/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 |
74052380/cell_15 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
cla... | code |
74052380/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
cla... | code |
16157465/cell_4 | [
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filename):
return pd.read_csv(path + filename, skiprows=2, header=None, sep=' ', usecols=... | code |
16157465/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
atom_list = []
for filename in os.listdir("../input/structures"):
atom_list = atom_list + list(read_xyz(path, filename)['atom'])
atom_list = set(atom_list)
print(atom_list)
"""
print("{'O', 'H', 'C', 'F', 'N'}") | code |
16157465/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
train.head() | code |
16157465/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filena... | code |
16157465/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filena... | code |
16157465/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
coupling_types = set(train['type'])
print(coupling_types) | code |
16157465/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/train.csv')
def read_xyz(path, filena... | code |
105187012/cell_42 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
X_train | code |
105187012/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.head(10) | code |
105187012/cell_25 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:,... | code |
105187012/cell_57 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn import linear_model
Lasso_reg = linear_model.Lasso(alpha=50, max_iter... | code |
105187012/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_30 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_44 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = Decisi... | code |
105187012/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape | code |
105187012/cell_29 | [
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier... | code |
105187012/cell_54 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_te... | code |
105187012/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape | code |
105187012/cell_50 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as p... | code |
105187012/cell_52 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_t... | code |
105187012/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.info() | code |
105187012/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = s... | code |
105187012/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_59 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.... | code |
105187012/cell_58 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn import linear_model
Lasso_reg = linear_model.Lasso(alpha=50, max_iter... | code |
105187012/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique() | code |
105187012/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data.plot()
plt.show() | code |
105187012/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
data['HeartDisease'].value_counts().plot(kind='bar') | code |
105187012/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.sh... | code |
105187012/cell_47 | [
"text_html_output_1.png"
] | from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train = s... | code |
105187012/cell_24 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum()
data.shape
X = data.iloc[:,... | code |
105187012/cell_53 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
import numpy as np
from sklearn.preprocessing import StandardScaler
sc = StandardScaler(with_mean=False)
X_train ... | code |
105187012/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-failure-prediction/heart.csv')
data.shape
data.nunique()
data.dropna(inplace=True)
data.isnull().sum() | code |
73081571/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Mond... | code |
73081571/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
... | code |
73081571/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of_week, 'Calories': calory_intake})
weekly_calory_count.plot('Days', 'Calori... | code |
73081571/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
ca... | code |
73081571/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
... | code |
73081571/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
import pandas as pd
import pandas as pd
days_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
calory_intake = [2385, 1543, 1293, 2019, 4201, 1203, 2309]
weekly_calory_count = pd.DataFrame({'Days': days_of... | code |
90138608/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.pairplot(df) | code |
90138608/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.info() | code |
90138608/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
len(df) | code |
90138608/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.head() | code |
90138608/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90138608/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.catplot(x='Happiness levels(Country)', y='City', kind='bar', data=df.nlargest(10, 'Happines... | code |
90138608/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns
sns.kdeplot(x='Life expectancy(years) (Country)', data=df, shade=True) | code |
90138608/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.tail() | code |
90138608/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/healthy-lifestyle-cities-report-2021/healthy_lifestyle_city_2021.csv')
df = data.copy()
df.columns | code |
72088106/cell_6 | [
"application_vnd.jupyter.stderr_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
def getFiles():
""" Dictonary to get the right Files"""
dict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
dict[fi... | code |
72088106/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 |
72088106/cell_5 | [
"text_html_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
def getFiles():
""" Dictonary to get the right Files"""
dict = {}
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
dict[fi... | code |
2035143/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, neighbors
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from sklearn import tree
import graphviz
from sklearn.model_selection import cross_val_scor... | code |
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