Machine Learning - Basic and most important libraries

#Basic and most important libraries
import pandas as pd , numpy as np
from sklearn.utils import resample
from sklearn.preprocessing import StandardScaler , MinMaxScaler
from collections import Counter
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.figure_factory as ff
import plotly

#Classifiers
from sklearn.ensemble import AdaBoostClassifier , GradientBoostingClassifier , VotingClassifier , RandomForestClassifier
from sklearn.linear_model import LogisticRegression , RidgeClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier 
from sklearn.naive_bayes import GaussianNB
from xgboost import plot_importance
from xgboost import XGBClassifier
from sklearn.svm import SVC

#Model evaluation tools
from sklearn.metrics import classification_report , accuracy_score , confusion_matrix
from sklearn.metrics import accuracy_score,f1_score
from sklearn.model_selection import cross_val_score

#Data processing functions
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import model_selection
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

import warnings
warnings.filterwarnings("ignore")