In this Data Set comprises of sales transactions captured at a retail store. The analysis consists to understand the customer purchase behavior (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in current city), product details (product id and product category) and Total purchase_amount from last month.
Using the following techniques and tools in Tableau and in Python: Groups and Sets, Advanced Table Calculations, Analytic & Advanced Data Prep, Animations, Level of Detail Calculations, Numpy, Pandas and Random Forest Regression.
They aim is to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.
To access the the data analysis of these proyect click on the bottons below :)