Halloween Dataset Cleaning & Exploratory Data Analysis (EDA) Using Python — Cleaning and Analyzing a Highly Messy Dataset with 140+ Columns

Worked on a highly unclean Halloween dataset containing approximately 140 columns, performing extensive data cleaning and exploratory data analysis (EDA) using Python. The project involved handling missing values, identifying and addressing data inconsistencies, analyzing data distributions, and exploring patterns and relationships between variables. Used Pandas and NumPy for data manipulation and cleaning, and Matplotlib and Seaborn for data visualization and analysis. The goal was to transform raw, messy data into a cleaner, more structured, and analysis-ready dataset while extracting meaningful insights.