• Investigated the online retail dataset and did data cleaning.
• Assessed the most and least expensive products
• Overlooked the sales numbers, using different features like countries, products, customers
• Performed Cohort Analysis to find the retention rate and for average sales quantity
• Retention rate increased from 2010 to 2011 (45.3% to 45.7%)
• Visualized how sales amount changed with time
• Took a new online retail dataset and I again did clean and processed the data
• Used FP-Algorithm to get frequent patterns from a portion of huge number of invoices in the dataset
• Used mlxtend library’s association_rule module to get the confidence metric, so I can sort them on that basis

For the dataset follow this Kaggle link : https://www.kaggle.com/code/keerthivasankannan/cohort-analysis-and-modeling-in-online-retail/data