imports 24

# General Imports
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, KFold
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

# 1. Linear Regression
from sklearn.linear_model import LinearRegression

# 2. Polynomial Regression (using PolynomialFeatures)
from sklearn.preprocessing import PolynomialFeatures

# 3. Ridge Regression
from sklearn.linear_model import Ridge

# 4. Lasso Regression
from sklearn.linear_model import Lasso

# 5. Elastic Net Regression
from sklearn.linear_model import ElasticNet

# 6. Bayesian Ridge Regression
from sklearn.linear_model import BayesianRidge

# 7. Ordinary Least Squares Regression (OLS)
# OLS is also implemented by LinearRegression, so no specific import needed

# 8. Huber Regression
from sklearn.linear_model import HuberRegressor

# 9. Theil-Sen Estimator
from sklearn.linear_model import TheilSenRegressor

# 10. Quantile Regression
from sklearn.linear_model import QuantileRegressor

# 11. Decision Tree Regression
from sklearn.tree import DecisionTreeRegressor

# 12. Random Forest Regression
from sklearn.ensemble import RandomForestRegressor

# 13. Gradient Boosting Regression
from sklearn.ensemble import GradientBoostingRegressor

# 14. XGBoost Regression
import xgboost as xgb

# 15. LightGBM Regression
import lightgbm as lgb

# 16. CatBoost Regression
from catboost import CatBoostRegressor

# 17. Support Vector Regression (SVR)
from sklearn.svm import SVR

# 18. K-Nearest Neighbors Regression (KNNR)
from sklearn.neighbors import KNeighborsRegressor

# 19. Principal Component Regression (PCR)
from sklearn.decomposition import PCA
from sklearn.linear_model import LinearRegression

# 20. Partial Least Squares Regression (PLSR)
from sklearn.cross_decomposition import PLSRegression

# 21. Artificial Neural Networks (ANN) Regression
from sklearn.neural_network import MLPRegressor

# 22. Multi-layer Perceptron (MLP) Regression
from sklearn.neural_network import MLPRegressor

# 23. Stochastic Gradient Descent (SGD) Regression
from sklearn.linear_model import SGDRegressor

# 24. Bayesian Regression
# This is another term that can refer to several models, including BayesianRidge
from sklearn.linear_model import BayesianRidge