FEATURE ENGINEERING FOR SALES DATASET

Abstract

In today’s data-driven business environment, organizations generate a large volume of sales data from various sources such as retail transactions, e-commerce platforms, and customer purchases. However, raw sales data often contains missing values, inconsistent formats, and irrelevant attributes, which makes it difficult to analyse and derive meaningful insights. Feature engineering plays a crucial role in transforming raw data into meaningful and useful features that improve data analysis and decision making processes. The main objective of this project is to perform feature engineering on a sales dataset to enhance the quality of data and extract valuable business insights. The project involves several stages including data collection, data preprocessing, feature creation, exploratory data analysis (EDA), and data visualization. Various data preprocessing techniques such as handling missing values, removing duplicate records, and transforming data into appropriate formats are applied. Python programming language is used for performing feature engineering and data analysis with the help of libraries such as Pandas, NumPy, Matplotlib, and Seaborn. New features such as revenue, profit margin, and category-based analysis are generated from the raw dataset. Exploratory data analysis is conducted to understand sales patterns, product performance, and revenue distribution. Furthermore, Tableau is used to develop interactive dashboards that visually represent sales trends, profitability analysis, and category distribution. The results obtained from this project help businesses understand sales performance, identify profitable products, and support data-driven decision making. This project demonstrates the importance of feature engineering in transforming raw sales data into meaningful insights for business analytics