E-Commerce customer prediction using machine learning in python

 

E-Commerce customer prediction using machine learning

 

 PROJECT ID: PYTHON10

 

PROJECT NAME: E-Commerce customer prediction using machine learning

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

E-commerce – is one of the first industry that started using all the benefits of machine learning. Zalando, Asos – companies that have entire artificial intelligence (AI), deep learning departments. They invest a lot of money to have better knowledge on their clients, personalize offers for a particular customer, improve customer experience and automate manual processes. Recommendation engine and machine learning in ecommerce industry directly converts into profits and increase companies market share with better customer acquisitio.

Addepto machine learning consulting team has analyzed which solutions have the biggest potential today. You could find few machine learning use cases below. They can help monetize your data and improve customer experience like Asos and Zalando:

1. Recommendation engine (recommender system)

Machine Learning in ecommerce have few key use cases. Personalization and recommendation engine is the hottest trend in global ecommerce space. With the use of artificial intelligence and the processing of huge amounts of data, you can thoroughly analyze the online activity of hundreds of millions of users. On its basis you are able to create product recommendations, tailored to a specific customer or group (auto-segmentation).

Let’s see how recommendation engine in ecommerce works. By analyzing collected big data on the current traffic on websites, you can determine, which sub-pages the client used. You could identify what he was looking for and where he spent most of the time. Based on various information: profile of previous customer activity, its preferences (e.g. favorite color), social media data, location and weather – results will be displayed on a personalized page with suggested products that will most likely interest them.

2. Personalization of the content on the website

Properly personalized content on the website or mobile application increases conversion and customer engagement. The selection of the best content is possible thanks to machine learning algorithms. Thus algorithms could find patterns in the data based on the processing of a large amount of structured and unstructured data (including images and text).

 

AI algorithms take into account various factors such as: favorite style and color, image intensity, activity history, preferences, etc.. The results on the website are adapted to the personal preferences of each individual person. In this way recommendation in ecommerce could help you to increase your revenues.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Predictions using Machine Learning in Ecommerce

 Predicting whether a given user will make a purchase in a specific product category in real time – so that the seller can react accordingly (eg, call that person or send email with engaging content). It gives you the opportunity to increase conversions while the customer, for example, is considering buying.

Predicting whether the user will be returning and what purchases he will make at certain times. This will help in matching the right marketing message to that person to increase the conversion of the future purchase and to encourage the person to return.

Customer lifetime value prediction (CLTV or LTV) – to predict how much money particular user will spend in your shop. Accurate estimation of the future customer value allows effectively allocate marketing expenses, identificate and care for high-value customers and reduce exposure to losses.

Customer churn prediction will discover customers who are risky to leave. The implemented solution will allow you to react quickly on the customers who are probably will stop buying from you. Such system will increase retention rate and will bring you a stable stream of revenue.

Prediction of client’s size – personalized size recommendations reduce the chargebacks for both the company and customers. It reduces company’s or customers costs and definitely increases customer satisfaction.

Prediction of demand for specific product categories – this will help to meet all customer needs and trends in the future. This will cause that customers will be happy to return to the your online store where most of the goods are available and can be bought immediately.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

PROJECT SOFWARE	ZIP
PROJECT REPORT PAGE	60 -80 Pages
CAN BE USED IN	Marketing (MBA)
PROJECT COST	1500/- Only
PDF SYNOPSIS COST	250/- Only
PPT PROJECT COST	300/- Only
PROJECT WITH SPIRAL BINDING	1750/- Only
PROJECT WITH HARD BINDING	1850/- Only
TOTAL COST (SYNOPSIS, SOFTCOPY, HARDBOOK, and SOFTWARE, PPT)	2500/- Only
DELIVERY TIME	1 OR 2 Days (In case Urgent Call: 8830288685)
SUPPORT / QUERY	www.projectsready.in
CALL	8830288685
Email	help@projectsready.in
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