In the era of digital commerce, online platforms generate enormous volumes of transaction data every day. This data contains valuable information about customer preferences, purchasing behaviour, product demand, pricing trends, and overall market dynamics. Understanding these behavioural patterns is essential for businesses to improve customer experience, optimize product strategies, and make informed decisions. This research presents a data-driven approach for analysing behavioural patterns in e-commerce transaction data. Product information was collected from Flipkart using automated web scraping techniques implemented with Selenium WebDriver and Python. The dataset primarily includes electronic product categories such as mobiles, headphones, smart watches, speakers, and accessories, along with attributes like price, ratings, and customer reviews. After collection, the raw data was stored in CSV format and processed using Python libraries including Pandas and NumPy. Data preprocessing techniques such as duplicate removal, handling missing values, formatting correction, and product categorization were applied to improve data quality and analytical accuracy. Exploratory Data Analysis (EDA) was then performed to identify customer preferences, spending behaviour, product popularity, and emerging demand trends. The study further demonstrates how visualization tools such as Power BI can transform complex transaction records into intuitive dashboards and reports. These visual insights help businesses quickly identify patterns, compare product performance, and support data-driven decision-making. Overall, the research highlights the practical value of combining web scraping, data preprocessing, behavioural analysis, and visualization to understand online consumer behaviour and enhance strategic business planning.
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