Book Image

Artificial Intelligence and Machine Learning Fundamentals

By : Zsolt Nagy
Book Image

Artificial Intelligence and Machine Learning Fundamentals

By: Zsolt Nagy

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Table of Contents (10 chapters)
Artificial Intelligence and Machine Learning Fundamentals
Preface

Lesson 5: Using Trees for Predictive Analysis


Activity 10: Car Data Classification

This section will discuss how to build a reliable decision tree model capable of aiding your company in finding cars clients are likely to buy. We will be assuming that you are employed by a car rental agency focusing on building a lasting relationship with its clients. Your task is to build a decision tree model classifying cars into one of four categories: unacceptable, acceptable, good, very good.

The data set can be accessed here: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation. Click the Data Folder link to download the data set. Click the Data Set Description link to access the description of the attributes.

Evaluate the utility of your decision tree model.

  1. Download the car data file from here: https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data. Add a header line to the front of the CSV file to reference it in Python more easily:

    Buying,Maintenance,Doors,Persons,LuggageBoot,Safety...