customer-data-cleaning

Customer Data Cleaning Project

Overview

This project focuses on cleaning and preprocessing raw data to improve its quality and usability. We applied Pandas and NumPy functions to handle missing values, duplicates, and formatting issues.

Dataset

• Name: Customer Sales Data
• Issues Found:
  • Missing values in key columns.
  • Inconsistent date formats.
  • Duplicate records.
  • Irregular capitalization in text fields.

Cleaning Steps

1. Handling Missing Data: Used .fillna(), .dropna(), and .replace() functions.
2. Fixing Duplicates: Removed duplicate records.
3. Standardizing Formats: Converted dates to a common format (YYYY-MM-DD).
4. Normalizing Text Data: Corrected case inconsistencies in categorical fields.
5. Outlier Detection: Identified and removed outliers affecting sales trends.