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 (get_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
from google.cloud import bigquery"
cell 2: "def get_data():
    client = bigquery.Client()
    query = \"SELECT * FROM [REDACTED]\"
    [REDACTED]
    return client.query(query).to_dataframe()"
cell 3: "df = get_data()
df = df.rename(columns={\"old\": \"new\"})"
