# ============================== # CORE LIBRARIES # ============================== import numpy as np import pandas as pd # ============================== # VISUALIZATION # ============================== import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px import plotly.graph_objects as go # ============================== # MACHINE LEARNING (SKLEARN) # ============================== from sklearn.model_selection import ( train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV ) from sklearn.preprocessing import ( StandardScaler, MinMaxScaler, RobustScaler, OneHotEncoder, LabelEncoder ) from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.linear_model import ( LinearRegression, LogisticRegression ) from sklearn.ensemble import ( RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, GradientBoostingRegressor ) from sklearn.metrics import ( accuracy_score, classification_report, confusion_matrix, r2_score, mean_absolute_error, mean_squared_error ) # ============================== # ADVANCED ML # ============================== import xgboost as xgb import lightgbm as lgb import catboost as cb # ============================== # STATISTICS # ============================== import scipy.stats as stats import statsmodels.api as sm