This notebook loads company+policy data from BigQuery using a SQL query and joins it to an insurance table; preprocesses the resulting DataFrame (renames columns, handles missing values, builds tenure buckets, normalizes insurance types); defines helper functions (extract_data) and then filters customer subsets by network/rede.

Neurobyte outline:
- Imports and environment setup
- BigQuery client init and SQL extraction into a DataFrame
- Preprocessing function(s) applied to the extracted dataset
- Filtering / segmentation into final analysis subsets

cell 1: "import pandas as pd
import numpy as np
from google.cloud import bigquery"
cell 2: "def extract_data():
    client = bigquery.Client()
    query = \"SELECT * FROM [REDACTED] LIMIT 100\"
    return client.query(query).to_dataframe()"
cell 3: "df = extract_data()
df = df.rename(columns={\"old_name\": \"new_name\"})
df[\"tenure_segment\"] = pd.cut(df[\"tenure_days\"], bins=[0, 30, 90, 365])"
