Comprehending consumer behaviour in virtual retail transactions is crucial for e-commerce enterprises to achieve prosperity. Transaction data analysis yields insightful information that may be used to develop plans for many areas of the company. The major priority is enhancing client satisfaction is one of transaction analysis's main advantages. Businesses may find pain areas, improve the overall online buying experience, and streamline operations by carefully examining the complete consumer journey. Increased client loyalty and satisfaction are a result of this optimisation. Another important factor that is influenced by transaction insights is personalisation. Businesses may customise marketing campaigns by exploring the unique tastes, purchase history, and browsing behaviours of each consumer. This personalisation includes making product recommendations that are appropriate, creating promotions that are specifically targeted, and providing an enhanced online experience.

This dataset collects vital information for organisations looking to improve their client base understanding by including a variety of factors connected to customer purchase habits. Age, gender, purchase amount, preferred payment methods, frequency of transactions, and feedback ratings are among the aspects that customers can choose from. Data is also provided on the kinds of goods bought, how often they buy, when they like to shop, and how they use special offers. This 4720-record dataset provides a starting point for companies wishing to use data-driven insights to improve decision-making and customer-focused strategy.

Primary Objective In this challenge, the objective is to analyze and derive insights from two distinct datasets related to online retail transactions.

Examples for Analysis Top-Selling Products and Categories: - Identify and analyze the top-selling products and categories based on transaction frequency or revenue. Customer Purchasing Behavior Patterns: - Investigate trends in customer purchasing behavior based on demographics (age, gender, etc.). Customer Ratings and Product Satisfaction: - Analyze customer ratings and feedback to understand overall product satisfaction. Impact of Discounts or Promotions: - Explore the impact of discounts or promotions on sales by analyzing transaction data during promotional periods. Seasonality of Product Purchases: - Investigate the seasonality of product purchases by analyzing monthly or quarterly sales trends.

Tech Stack : Jupyter Notebook/ Google Colab, Sklearn Matplotlib, Python, Seaborn