models.py

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
# import algorithms for classification  
from sklearn.linear_model import LogisticRegression, SGDClassifier, RidgeClassifier 
from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier,HistGradientBoostingClassifier 
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.tree import DecisionTreeClassifier 
from sklearn.svm import SVC 
from xgboost import XGBClassifier,XGBRFClassifier 
from sklearn.neural_network import MLPClassifier 
from lightgbm import LGBMClassifier 
from sklearn.naive_bayes import MultinomialNB,CategoricalNB 
# import algorithms for regression 
from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, Lasso, ElasticNet 
from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor,GradientBoostingRegressor,HistGradientBoostingRegressor 
from sklearn.neighbors import KNeighborsRegressor 
from sklearn.tree import DecisionTreeRegressor 
from sklearn.svm import SVR 
from xgboost import XGBRegressor, XGBRFRegressor 
from sklearn.neural_network import MLPRegressor 
from lightgbm import LGBMRegressor 
from sklearn.naive_bayes import GaussianNB 
 
 
 
# dictionary where keys are name of algorithm and values are algorithm for classifier 
algos_class = { 
 "Logistic Regression": LogisticRegression(), 
 "SGD Classifier": SGDClassifier(), 
 "Ridge Classifier": RidgeClassifier(), 
 "Random Forest Classifier": RandomForestClassifier(), 
 "AdaBoost Classifier": AdaBoostClassifier(), 
 "Gradient Boosting Classifier": GradientBoostingClassifier(), 
 "Hist Gradient Boosting Classifier": HistGradientBoostingClassifier(), 
 "K Neighbors Classifier": KNeighborsClassifier(), 
 "Decision Tree Classifier": DecisionTreeClassifier(), 
 "SVC": SVC(), 
 "XGB Classifier": XGBClassifier(), 
 "XGBRF Classifier": XGBRFClassifier(), 
 "MLP Classifier": MLPClassifier(), 
 "LGBM Classifier": LGBMClassifier(), 
 "Multinomial Naive Bayes": MultinomialNB(), 
 "Categorical Naive Bayes": CategoricalNB()} 
 
# dictionary where keys are name of algorithm and values are algorithm for regression 
algos_reg = { 
 "Linear Regression": LinearRegression(), 
 "SGD Regressor": SGDRegressor(), 
 "Ridge Regressor": Ridge(), 
 "Lasso Regressor": Lasso(), 
 "ElasticNet Regressor": ElasticNet(), 
 "Random Forest Regressor": RandomForestRegressor(), 
 "AdaBoost Regressor": AdaBoostRegressor(), 
 "Gradient Boosting Regressor": GradientBoostingRegressor(), 
 "Hist Gradient Boosting Regressor": HistGradientBoostingRegressor(), 
 "K Neighbors Regressor": KNeighborsRegressor(), 
 "Decision Tree Regressor": DecisionTreeRegressor(), 
 "SVR": SVR(), 
 "XGB Regressor": XGBRegressor(), 
 "XGBRF Regressor": XGBRFRegressor(), 
 "MLP Regressor": MLPRegressor(), 
 "LGBM Regressor": LGBMRegressor(), 
 "Gaussian Naive Bayes": GaussianNB()} 
 
# dataframe where index are name of algorithm as "algorithm name" , column is algorithm as "algorithm" 
 
Classification_models = pd.DataFrame(data=algos_class.values(), index=algos_class.keys()) 
 
Regression_models = pd.DataFrame(data=algos_reg.values(), index=algos_reg.keys())