One of the most interesting uses of Machine Learning and Data Science can be found in the business domain where one might need to analyse the given data for problems such as identifying the number of customers a company can expect, the type of customers a company needs to focus on to maximize profits, etc.

With this particular Black Friday sale analysis, we are more interested in figuring out how much will a customer spend based on certain attributes such as their Age group, City Category, etc. (Discussed in more detail later).

_This project is a part of an ongoing Hackathon on Analytics Vidhya known as the _Black Friday Sales Prediction

NOTE: The complete Python code can be accessed here.

Also, take a look at thisBlackFriday Visualization_ I created on Tableau._

Introduction

A retail company “ABC Private Limited” wants to **understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. **They have shared purchase summary of various customers for a selected high volume products from last month.

They want to build a model to predict the purchase amount of customer against various products which will help them to create a personalized offer for customers against different products.

Step 0: Understanding the Problem

We must understand what the problem demands from us before we begin to play with the data. In this case, we are asked to predict the ‘Purchase Amount’ which is a continuous variable. Now that we know we are going to predict a continuous variable, we can say with certainty that this is a Regression Problem and we can use various regression algorithms such as Linear Regression, Ridge Regression, Decision Tree Regression, Ensemble Techniques, Neural Networks or any other preferred Regression technique.

#machine-learning #regression-analysis #black-friday #xgboost #analytics-vidhya