Retail Sales Data Analysis

A Python-based data analysis project that uncovers meaningful insights from retail sales data to drive business performance improvement. Through data cleaning, exploratory analysis, and visualization, this project provides actionable strategies for optimizing sales, inventory, and customer engagement.

📊 Key Features

• Data Preprocessing: Cleaned and preprocessed raw retail sales datasets to ensure data quality.
• Trend Analysis: Identified key sales trends, seasonal patterns, and customer purchasing behavior.
• Product Performance: Analyzed top-selling and underperforming products to support inventory optimization strategies.
• Data Visualization: Created clear and insightful visualizations using matplotlib and seaborn.
• Actionable Insights: Delivered data-driven recommendations to improve overall retail outcomes.

🛠️ Tech Stack

• Python: pandas, NumPy, matplotlib, seaborn
• Jupyter Notebook

📈 Outcome

This analysis transforms raw retail data into clear, actionable insights, empowering decision-makers to:

• Increase Sales Efficiency by focusing on high-performing products and trends.
• Optimize Stock Management by understanding demand patterns and seasonality.
• Enhance Customer Satisfaction through insights into buying behavior.