Task 1: Data Cleaning and Preprocessing – Data Analyst Internship

Objective Clean and preprocess a raw marketing dataset by:

• Handling missing values
• Removing duplicate rows
• Standardizing text and date formats
• Renaming columns
• Correcting data types

This process prepares the dataset for analysis and ensures data quality.

Dataset Used

Name:Customer Personality Analysis

Filename: marketing_campaign.csv

Source:Kaggle Dataset

Description:The dataset contains customer demographics, spending habits, and campaign response data. Useful for customer segmentation and marketing analysis.

Data Cleaning Steps Performed

1. Removed Duplicates

   • Used drop_duplicates() to eliminate any duplicate entries.

2. Handled Missing Values

   • Filled missing values in the Income column with the median.
   • Dropped remaining rows with missing values using dropna().

3. Standardized Text Fields

   • Converted Education and Marital_Status to lowercase and removed extra spaces using .str.lower().str.strip().

4. Converted Date Formats

   • Converted Dt_Customer column to consistent datetime format (DD-MM-YYYY).

5. Renamed Columns

   • Renamed all columns to snake_case using string methods to ensure consistency and readability.

6. Corrected Data Types

   • Created new age column from Year_Birth (2025 - Year_Birth).
   • Ensured age is an integer and Dt_Customer is in datetime format.

Tools Used

• Python 3.x
• Pandas
• VS code

Output Files

• cleaned_marketing_campaign.csv – Final cleaned dataset
• 'marketing_campaign' - actual dataset
• data_cleaning.py – Python script used for cleaning
• README.md – Documentation file (this file)

Kaggle Datasets Suitable for Task 1

• ✅ Customer Personality Analysis (used)