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# Data manipulation and analysis
import numpy as np
import pandas as pd

# Data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Machine learning
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error, r2_score

# Feature preprocessing
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder
from sklearn.feature_selection import SelectKBest, f_regression, f_classif

# Model evaluation and validation
from sklearn.model_selection import cross_val_score, GridSearchCV

# Dimensionality reduction
from sklearn.decomposition import PCA

# Natural language processing
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

# Web scraping
import requests
from bs4 import BeautifulSoup

# Time series analysis
from statsmodels.tsa.arima.model import ARIMA

# Clustering
from sklearn.cluster import KMeans
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