path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
34142232/cell_13 | [
"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('../input/pm25-mean-annual-exposure/PM25_MAE.csv', index_col=0)
df = df.drop(['Country Code', 'Indicator Name', 'Indicator Code'], axis=1)
df = df.dropna(thresh=10, axis=1)
before = df.shape[0]
na_free = df.dropna(thresh=10, axis=... | code |
34142232/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)
df = pd.read_csv('../input/pm25-mean-annual-exposure/PM25_MAE.csv', index_col=0)
df = df.drop(['Country Code', 'Indicator Name', 'Indicator Code'], axis=1)
df = df.dropna(thresh=10, axis=1)
before = df.shape[0]
na_free = df.dropna(thresh=10, axis=... | code |
34142232/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/pm25-mean-annual-exposure/PM25_MAE.csv', index_col=0)
df = df.drop(['Country Code', 'Indicator Name', 'Indicator Code'], axis=1)
df = df.dropna(thresh=10, axis=1)
before = df.shape[0]
na_free = df.dropna(thresh=10, axis=... | code |
34142232/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/pm25-mean-annual-exposure/PM25_MAE.csv', index_col=0)
df = df.drop(['Country Code', 'Indicator Name', 'Indicator Code'], axis=1)
df = df.dropna(thresh=10, axis=1)
before = df.shape[0]
na_free = df.dropna(thresh=10, axis=... | code |
34142232/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 |
34142232/cell_14 | [
"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('../input/pm25-mean-annual-exposure/PM25_MAE.csv', index_col=0)
df = df.drop(['Country Code', 'Indicator Name', 'Indicator Code'], axis=1)
df = df.dropna(thresh=10, axis=1)
before = df.shape[0]
na_free = df.dropna(thresh=10, axis=... | code |
2036047/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.linear_model import RidgeClassifier, LogisticRegressionCV
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.naive_bay... | code |
2036047/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2036047/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_20 | [
"text_html_output_10.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"t... | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.linear_model import RidgeClassifier, LogisticRegressionCV
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.naive_bay... | code |
2036047/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
pyo.init_notebook_mode(connected=True)
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2036047/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_24 | [
"text_plain_output_1.png"
] | from pprint import pprint
import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
f... | code |
2036047/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import plotly.graph_objs as go
import plotly.offline as pyo
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
feature_columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(featur... | code |
2036047/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.linear_model import RidgeClassifier, LogisticRegressionCV
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.naive_bay... | code |
2036047/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.head() | code |
32070789/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from IPython import display
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNor... | code |
32070789/cell_13 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNormalization
from keras.layers... | code |
32070789/cell_20 | [
"text_plain_output_1.png"
] | from IPython import display
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNor... | code |
32070789/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import warnings
warnings.filterwarnings('ignore')
import numpy as np, pandas as pd, pylab as pl, h5py
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from IPython import display
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import Mode... | code |
32070789/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
def history_plot(fit_history):
keys = list(fit_history.history.keys())[0:4]
def ohe(x):
return OneHotEncoder(categories='auto').fit(x.reshape(-1, 1)).transform(x.reshape(-1, 1)).toarray().astype('int64')
def t... | code |
32070789/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython import display
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
def history_plot(fit_history):
keys = list(fit_history.history.keys())[0:4]
def ohe(x):
return OneHotEncoder(categories='auto').fit(x.reshape(-1, 1)).transform(x.reshape(-1, 1)).to... | code |
32070789/cell_15 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNormalization
from keras.layers... | code |
32070789/cell_17 | [
"text_plain_output_1.png"
] | from IPython import display
from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNor... | code |
32070789/cell_14 | [
"image_output_1.png"
] | from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNormalization
from keras.layers... | code |
32070789/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping
from keras.layers import Conv2D,MaxPooling2D,GlobalMaxPooling2D
from keras.layers import Dense,LSTM,GlobalAveragePooling1D,GlobalAveragePooling2D
from keras.layers import Input,Activation,Flatten,Dropout,BatchNormalization
from keras.layers... | code |
32070789/cell_5 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
def history_plot(fit_history):
keys = list(fit_history.history.keys())[0:4]
def ohe(x):
return OneHotEncoder(categories='auto').fit(x.reshape(-1, 1)).transform(x.reshape(-1, 1)).toarray().astype('int64')
def t... | code |
16130893/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
only... | code |
16130893/cell_6 | [
"image_output_1.png"
] | from IPython.display import Image
from tensorflow.keras import layers
import tensorflow as tf
def gen_model():
inputs = tf.keras.layers.Input(shape=(32, 32, 3))
x = inputs
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPool2D(2)(x)
... | code |
16130893/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkface_aligned_... | code |
16130893/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
print(os.listdir('../input/utkface_aligned_cropped/')) | code |
16130893/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import ma... | code |
16130893/cell_8 | [
"text_plain_output_1.png"
] | from IPython.display import Image
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import ma... | code |
16130893/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import numpy as np
import pandas as pd
import cv2
from IPython.display import Image
import matplotlib.pyplot as plt
import os
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
onlyfiles = os.listdir('../input/utkf... | code |
90130201/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_13 | [
"text_html_output_1.png"
] | 201 / 5110 | code |
90130201/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape | code |
90130201/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
stroke_df.describe() | code |
90130201/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.head(5) | code |
90130201/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
90130201/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
stroke_df = pd.read_csv('../input/stroke-prediction-dataset/healthcare-dataset-stroke-data.csv')
stroke_df.shape
na_count = []
for i in range(0, len(stroke_df.columns)):
na_count.append(stroke_df[stroke_df.columns[i]].isna().sum())
na_df = pd.DataFrame(zip(stroke_df.columns, na_count))
na_df.... | code |
105174236/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transform(df.region)
df['le_smoker'] = le.fit_transform(df.sm... | code |
105174236/cell_9 | [
"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/insurance/insurance.csv')
df.describe() | code |
105174236/cell_23 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder(... | code |
105174236/cell_20 | [
"text_html_output_1.png"
] | from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder(... | code |
105174236/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)
df = pd.read_csv('../input/insurance/insurance.csv')
df.head() | code |
105174236/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transform(df.region)
df['le_smoker'] = le.fit_transform(df.sm... | code |
105174236/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 |
105174236/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
print(df.columns) | code |
105174236/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transfo... | code |
105174236/cell_28 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
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/insuran... | code |
105174236/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)
df = pd.read_csv('../input/insurance/insurance.csv')
df.info() | code |
105174236/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transform(df.region)
df['le_smoker'] = le.fit_transform(df.sm... | code |
105174236/cell_16 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transform(df.region)
df['le_smoker'] = le.fit_transform(df.sm... | code |
105174236/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/insurance/insurance.csv')
print('Is there any missing value? \n')
df.isnull().sum() | code |
105174236/cell_27 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
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/insuran... | code |
105174236/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/insurance/insurance.csv')
df.isnull().sum()
le = LabelEncoder()
df['le_region'] = le.fit_transform(df.region)
df['le_smoker'] = le.fit_transform(df.sm... | code |
73097374/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_c... | code |
73097374/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/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
print(features.nunique())
features.head() | code |
73097374/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from xgboost import XGBRegressor
import pandas as pd # data processing, CSV file I/O (e.g. pd.... | code |
73097374/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import xgboost as xgb
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73097374/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
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], a... | code |
73097374/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
te... | code |
73097374/cell_3 | [
"text_plain_output_1.png",
"image_output_2.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/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head()
print(train) | code |
73097374/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor(n_estimators=5000, n_jobs=4, learning_rate=0.005, max_depth=5, colsample_bytree=0.5, tree_method='hist', random_state=0)
model.fit(X_train, y_train, early_stopping_rounds=5, eval_set=[(X_valid, y_valid)], eval_metric=... | code |
33111929/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data.head(10) | code |
33111929/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data = data.drop(data.loc[data.x <= 0].index)
data = data.drop(data.loc[data.y <= 0].index)
data = data.drop(data.loc[data.z <= 0].index)
data['ratio'] = d... | code |
33111929/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
... | code |
33111929/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes | code |
33111929/cell_8 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data.info() | code |
33111929/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data = data.drop(data.loc[data.x <= 0].index)
data = data.drop(data.loc[data.y <= 0].index)
data = data.drop(data.loc[data.z <= 0].index)
data['ratio'] = d... | code |
33111929/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data = data.drop(data.loc[data.x <= 0].index)
data = data.drop(data.loc[data.y <= 0].index)
data = data.drop(data.loc[data.z <= 0].index)
data['ratio'] = d... | code |
33111929/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data = data.drop(data.loc[data.x <= 0].index)
data = data.drop(data.loc[data.y <= 0].index)
data = data.drop(data.loc[data.z <= 0].index)
data['ratio'] = data.x / data.y
premium = ['D', 'E', 'F', 'G', 'H']
def data_split(status):
... | code |
33111929/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/diamonds/diamonds.csv')
data.dtypes
data.describe() | code |
17144077/cell_2 | [
"text_html_output_1.png"
] | import os
import os
import numpy as np
import pandas as pd
import os
import os
import cv2
import random
import numpy as np
import pandas as pd
import scipy as sp
import torch
from fastai.vision import *
import glob
print(os.listdir('../input/fastai-pretrained-models')) | code |
17144077/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17144077/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)
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sample_submission.csv')
train = ImageList.from_df(train_df, path=PATH, cols='id_code', folder='train_images', suffix='.png')
test = ImageLis... | code |
17144077/cell_15 | [
"text_html_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sample_submission.csv')
train = ImageList.from_df(train_df, path=PATH, cols='id... | code |
17144077/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sample_submission.csv')
train = ImageList.from_df(train_df, path=PATH, cols='id... | code |
17144077/cell_14 | [
"image_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sample_submission.csv')
train = ImageList.from_df(train_df, path=PATH, cols='id... | code |
17144077/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import cv2
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)
import scipy as sp
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sampl... | code |
17144077/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import cohen_kappa_score
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
SIZE = 224
train_df = pd.read_csv(PATH + '/train.csv')
test_df = pd.read_csv(PATH + '/sample_submission.csv')
train = ImageList.from_df(train_df, path=PATH, cols='id... | code |
105190429/cell_9 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.head() | code |
105190429/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
data.drop_duplicates(inplace=True)
data = data.reset_index(drop=True)
plt.figure(... | code |
105190429/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
data.drop_duplicates(inplace=True)
data = data.reset_index(drop=True)
plt.figure(... | code |
105190429/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.info() | code |
105190429/cell_1 | [
"text_plain_output_1.png"
] | !pip install catboost
!pip install shap | code |
105190429/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
duplicate | code |
105190429/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
data.drop_duplicates(inplace=True)
data = data.reset_index(drop=True)
plt.figure(... | code |
105190429/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
data.drop_duplicates(inplace=True)
data = data.reset_index(drop=True)
plt.figure(... | code |
105190429/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum()
duplicate = data[data.duplicated()]
data.drop_duplicates(inplace=True)
data = data.reset_index(drop=True)
print(f'There are {data.shape[0]} records and {data.shape[1]} colum... | code |
105190429/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.describe().T
data.isnull().sum() | code |
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