Identifying Shopping Trends using Data Analysis This project analyzes shopping trends based on a dataset of 3,900 customer transactions. It uncovers insights into customer behavior, demographic influences, and seasonal patterns to aid businesses in optimizing strategies.

Features Data Analysis: Key questions on demographics, spending, and trends addressed using Python. Visual Insights: Clear visualizations for trends like purchase frequency, seasonal spending, and product preferences. Actionable Insights: Recommendations for marketing, inventory, and customer engagement. Technologies Used Programming Language: Python Libraries: Pandas, Matplotlib, Seaborn, NumPy Environment: Jupyter Notebook Dataset The dataset contains: