All the given analytics models we have developed so far are critical for running a successful business. In this chapter, we developed customer segmentation based on the behavior of the customers. In order to do that, we used various algorithms, such as SVM, linear regression, decision tree, random forest, gradient boosting, voting-based models, and so on. By using the voting-based model, we achieved the best possible accuracy. Customer segmentation analysis is important for small and midsized organizations because these analysis help them optimize their marketing strategy as well as significantly improve the customer acquisition cost. I developed the code for the customer churn analysis, available at: https://github.com/jalajthanaki/Customer_churn_analysis, and for customer life-time value analysis at: https://github.com/jalajthanaki/Customer_lifetime_value_analysis . You can refer to them to learn more about customer analytics. You can read about customer analytics at: https://github...
Machine Learning Solutions
Machine Learning Solutions
Overview of this book
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job.
You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples.
The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.
In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Free Chapter
Credit Risk Modeling
Stock Market Price Prediction
Customer Analytics
Recommendation Systems for E-Commerce
Sentiment Analysis
Job Recommendation Engine
Text Summarization
Developing Chatbots
Building a Real-Time Object Recognition App
Face Recognition and Face Emotion Recognition
Building Gaming Bot
List of Cheat Sheets
Strategy for Wining Hackathons
Index
Customer Reviews