ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.preprocessing import MinMaxScaler
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline as imbpipeline
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_classification, load_breast_cancer

ksv-muralidhar

import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator,TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression

ksv-muralidhar

import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator,TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import cross_validate, train_test_split
from sklearn.metrics import mean_squared_error

ksv-muralidhar

import numpy as np
import pandas as pd
from apyori import apriori
from termcolor import colored

data = pd.read_csv("../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv",header=1)

ksv-muralidhar

import pandas as pd
from sklearn.model_selection import train_test_split,StratifiedKFold,cross_validate
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

ksv-muralidhar

from sklearn.compose import ColumnTransformer

ksv-muralidhar

fig,ax = plt.subplots(2,3,figsize=(15,10))
row = col = 0
np.random.seed(11)
norm_dist = np.random.randn(1000)
for n,i in enumerate(np.linspace(5,100,6)):
    if (n>0) & (n%3==0):
        row += 1
        col = 0
    sns.histplot(x=norm_dist,bins=int(i),ax=ax[row,col])
    ax[row,col].set_title(f'bins = {int(i)}')

ksv-muralidhar

import requests
import pandas as pd
from IPython.display import display

ksv-muralidhar

from sklearn.model_selection import learning_curve