{"id":23706986,"url":"https://github.com/shriansh8619/eda_customer_behavior","last_synced_at":"2026-04-16T19:03:41.201Z","repository":{"id":268880489,"uuid":"905740890","full_name":"shriansh8619/EDA_Customer_Behavior","owner":"shriansh8619","description":"This project analyzes Nielsen transaction data using Python to uncover sales trends, customer preferences, and purchasing patterns. It provides insights to optimize inventory, create targeted promotions, and improve store performance. 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This project analyzes transaction data to identify preferences, buying patterns, and decision-making trends. Using **Exploratory Data Analysis** (EDA) techniques like data visualization and summary statistics, we uncover valuable insights to enhance customer satisfaction, retention, and profitability.\n### The Nielsen Advantage\nNielsen's transaction data from supermarkets and grocery stores provides a clear picture of consumer habits, offering businesses a powerful tool to understand their customers.\n### Project Goal\nThe aim is to identify key customer trends and buying patterns. Using Python's data analysis tools,it help businesses refine strategies and improve customer retention and profits.\n### Customer Behavior Analysis Insights\nThis analysis used a rich dataset with details like monthly sales, store codes, bill IDs, product quantities, and brand information. Leveraging Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn, we achieved the following:\n\n- **Sales Trends:** Identified peak and low sales months to optimize inventory and promotions\n  ![image](https://github.com/user-attachments/assets/48d1a3f7-e476-47ad-9327-48d18e2c45be)\n\n- **Top Performers:** Analyzed sales by store to highlight high and low performers.\n  ![image](https://github.com/user-attachments/assets/45ffdb69-9e50-4cd4-ab34-3b758683c8fd)\n\n- **Customer Preferences:** Pinpointed popular products, brands, and categories for targeted marketing.\n![image](https://github.com/user-attachments/assets/c513d691-86e6-4b1e-848b-5f5533f88bbc)\n\n- **Daily \u0026 Weekly Patterns:** Understand customer purchasing habits across days and weeks, informing staffing decisions and inventory control.\n![image](https://github.com/user-attachments/assets/005cd582-6c7b-4934-926b-7b3f4ce6e68e)\n\n\n### Key Business Insights\nThe analysis provided valuable insights for supermarkets:\n\n- **Store-Specific Strategies:** High-performing stores like N8 set benchmarks, while low performers like N7 need targeted improvements.\n- **Data-Driven Promotions:** Promotions at the start of the month, when customers spend more, can boost sales.\n- **Inventory Optimization:** Understanding sales patterns helps manage stock levels efficiently, avoiding both shortages and surpluses.\n\n### Next Steps for Exploration\n\nThis analysis sets the stage for further investigation:\n\n- **Validate Findings:** Use statistical methods to confirm insights.\n- **Explore Additional Factors:** Examine how demographics, store locations, and promotions affect customer behavior.\n\n### Conclusion: Data-Driven Success\n\nBy analyzing Nielsen transaction data with Python, this project highlights the power of data-driven insights in the retail industry. Supermarkets can use these findings to optimize strategies, improve customer experiences, and drive sustainable growth.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshriansh8619%2Feda_customer_behavior","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshriansh8619%2Feda_customer_behavior","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshriansh8619%2Feda_customer_behavior/lists"}