Kaggle Retail Dataset: This dataset contains over 10 years of daily sales data for 45,000 food products across 10 stores. It includes fields like store, department, date, weekly sales, markup, and more. With over 500,000+ rows, it provides a lot of rich data to analyze retail sales patterns, perform forecasting, explore department performance, and get insights into pricing and promotion effectiveness. Some potential capstone projects could be building predictive sales models, optimizing inventory levels, detecting anomalies or outliers, comparing store or department performance, etc.
Online Retail II Dataset: This dataset from the UCI Machine Learning Repository contains transactions made by a UK-based online retail between 01/12/2009 and 09/12/2011. It includes fields like InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, and Country. With over 5,000 unique products and around 8,000 customers, it allows examining customer purchasing behaviors, product categories, sales trends over time. Capstone ideas could be customer segmentation, recommendation engines, predictive churn analysis, promotion targeting, assortment optimization, etc.
European Retail Study Dataset: This dataset was collected between 2013-2015 across 24 countries in Europe to study omni-channel retail. It provides information on over 42,000 customers, their purchase transactions, demographic details, online/offline shopping behaviors, returns etc. Some dimensions covered are age, gender, income-level, product categories purchased, channels used, spend amounts. This rich dataset opens up opportunities for multi-channel analytics, personalized experiences, loyalty program design, understanding cross-border trends at a continental scale.
Instacart Market Basket Analysis Dataset: This dataset collected over 3 million grocery orders from real Instacart customers. It includes anonymized order data with product names, quantities, added or removed from basket, purchase or cancellation. This provides scope for advanced market basket or transactional analysis to determine complementary or frequently bought together products, influencing factors on abandoned cart recovery, incremental sales from personalized recommendations, effects of out-of-stock items etc.
Walmart Sales Forecasting Dataset: This dataset contains daily sales data for 45 Walmart stores located in different regions collected over 3 years. Features include Store, Dept, Date, Weekly_Sales, Markup, etc. It can be leveraged to build statistical or deep learning models for short and long term demand forecasting across departments, developing automatic outlier detection capabilities, scenarion analysis during special events etc.
Target Customer Dataset: This dataset contains purchasing profiles for over 5000 anonymous Target customers encompassing their transactions over a 6 month period. It includes features like age, gender, marital status, home ownership, number of dependents, income, spend categories within Target like grocery, personal care, electronics etc. This could enable identifying high lifetime value segments, developing micro-segmentation strategies, testing personalization and targeted promotions approaches.
Kroger Customer Analytics Dataset: This dataset contains anonymous profiles of over 30,000 Kroger customers including their demographics, surveyed household & lifestyle characteristics, shopping behaviors and purchasing basket details. Variables provided are age, ethnicity, family status, income level, ZIP code, preferences like organic, wellness focused etc along with purchases across departments. Potential projects include customer churn analysis, propensity modeling for private label brands, targeted loyalty program personalization at scale.
These datasets offer rich retail data that span various dimensions – from transactions, customers, banners to omni-channel behavior. They enable diving deep into opportunities like forecasting, recommendations, segmentation, promotions analysis, supply chain optimization at scale suitable for many capstone project ideas exploring insights for retailers. The datasets are publicly available and of a good volume and variety to power meaningful analytical modeling and drive actionable business recommendations.