Now that we have presented three very useful classifiers, it is time for us to evaluate their accuracy on the testing set; in the training set, the three models appear to give us about the same accuracy of about 80%. However, before calculating testing accuracy, recall what we said in the previous chapter about the need for a reference point to know if this 80% is good or bad. Back in the previous chapter, we answered a version of this question—in the absence of any information about the customer, what would be our best guess for his payment status next month? In this case, we have only two choices: pay or default, and since most of the clients in our sample paid, in the absence of any information our best guess would be to always predict pay. This simple strategy (always predict pay) will be in this case called the null model, the model without...
Hands-On Predictive Analytics with Python
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Hands-On Predictive Analytics with Python
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Overview of this book
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.
The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.
Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.
By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
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Current Title:
Hands-On Predictive Analytics with Python
Table of Contents (11 chapters)
Preface
Free Chapter
The Predictive Analytics Process
Problem Understanding and Data Preparation
Dataset Understanding – Exploratory Data Analysis
Predicting Numerical Values with Machine Learning
Predicting Categories with Machine Learning
Introducing Neural Nets for Predictive Analytics
Model Evaluation
Model Tuning and Improving Performance
Implementing a Model with Dash
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