#libraries for data manipulation

import numpy as np

import pandas as pd

#libraries for data visualization

import matplotlib.pyplot as plt

import seaborn as sns

$\%$matplotlib inline

#to remove warning

import warnings

warnings.filterwarnings$(\text{'}$ignore$\text{'})$

#to impute na

from sklearn.impute import SimpleImputer

#libraries for model building

from sklearn import metrics

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.model$\_$selection import GridSearchCV

from sklearn.linear$\_$model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier

from xgboost import XGBClassifier

# To tune model, get different metric scores and split data

from sklearn.model$\_$selection import GridSearchCV

from sklearn.model$\_$selection import train$\_$test$\_$split, StratifiedKFold, cross$\_$val$\_$score

from sklearn.metrics import f$1\_$score, accuracy$\_$score, recall$\_$score, precision$\_$score

# To oversample and undersample data

from imblearn.over$\_$sampling import SMOTE

from imblearn.under$\_$sampling import RandomUnderSampler

# To do hyperparameter tuning

from sklearn.model$\_$selection import RandomizedSearchCV

# for creating a pipeline

# To be used for creating pipelines and personalizing them

from sklearn.pipeline import Pipeline

from sklearn.compose import ColumnTransformer