Project Overview
The project involves collaborating with a bank to clean and reformat data from a recent marketing campaign promoting personal loans. The provided CSV file, “bank_marketing.csv,” needs to be processed and split into three separate CSV files: client.csv, campaign.csv, and economics.csv. Each file must adhere to specific data structures and types for easy integration into a PostgreSQL database for future campaigns. Tasks include renaming columns, managing missing values, and converting data types according to the given specifications.
Script Summary
This script cleans and splits the bank’s marketing data into three CSV files (client.csv, campaign.csv, economics.csv) using pandas and numpy. It standardizes the data by fixing formatting issues, converting columns to appropriate types (e.g., boolean, datetime), and removing unnecessary columns. The result is a clean, structured dataset ready for database integration.
