HELIO

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from datetime import datetime, timedelta

class DataAnalyzer:
    def __init__(self, data_path: str):
        self.df = pd.read_csv(data_path)
        self.setup_analysis()
    
    def setup_analysis(self):
        """Initial data setup and cleaning"""
        # Handle missing values
        self.df = self.df.dropna(subset=['revenue', 'customer_id'])
        
        # Convert da...

import tableauserverclient as TSC
import pandas as pd
import requests
from typing import Dict, List, Optional
import json
from datetime import datetime

class TableauDashboardManager:
    def __init__(self, server_url: str, username: str, password: str, site_id: str = ''):
        self.server_url = server_url
        self.username = username
        self.password = password
        self.site_id = site_id
        self.server = None
        self.auth_token = None
    
    def connect_to_server(sel...

import pandas as pd
import numpy as np
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_squared_error, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Dict, Tuple, List

class StatisticalAnalyzer:
    def __init__(self, data: pd.DataFrame):
        self.data = ...

import logging
import time
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class ProcessingResult:
    success: bool
    processed_records: int
    errors: List[str]
    execution_time: float

class DataPipelineProcessor:
    def __init__(self, max_retries: int = 3, batch_size: int = 1000):
        self.max_retries = max_retries
        self.batch_size = batch_size
        self.logger = logging.getLogger(__name__)
    
    def process_batch(self, data: List[Dict[...

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score
import joblib
from typing import Dict, Tuple, Any
import warnings
warnings.filterwarnings('ignore')...