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Top 9 Prediction Books

The AI Optify data team writes about topics that we think data scientists will love. AI Optify has affiliate partnerships so we may get a share of the revenue from your purchase.

Best Prediction Books - For this post, we have scraped various signals (e.g. online ratings and reviews, topics covered, author influence in the field, year of publication, social media mentions, etc.) from web. We have fed all above signals to an ML algorithm to compute a score and rank the top books related to prediction topic.

The readers will love our list because it is Data-Driven & Objective. Enjoy the list:

1. Moneyball: The Art of Winning an Unfair Game

Quality Score: 100/100

Moneyball is a quest for something as elusive as the Holy Grail, something that money apparently can't buy: the secret of success in baseball. The logical places to look would be the front offices of major league teams, and the dugouts, perhaps even in the minds of the players themselves. Lewis mines all these possibilities—his intimate and original portraits of big league ballplayers are alone worth the price of admission—but the real jackpot is a cache of numbers—numbers!—collected over the years by a strange brotherhood of amateur baseball enthusiasts: software engineers, statisticians, Wall Street analysts, lawyers and physics professors.

2. Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions

Quality Score: 93/100

When it comes to making decisions in our lives, we think we're making smart, rational choices. But are we? In this newly revised and expanded edition of the groundbreaking New York Times bestseller, Dan Ariely refutes the common assumption that we behave in fundamentally rational ways. From drinking coffee to losing weight, from buying a car to choosing a romantic partner, we consistently overpay, underestimate, and procrastinate. Yet these misguided behaviors are neither random nor senseless. They're systematic and predictable—making us predictably irrational.

3. The Signal and the Noise: Why So Many Predictions Fail--but Some Don't

Quality Score: 93/100

Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.

4. Naked Statistics: Stripping the Dread from the Data

Quality Score: 79/100

From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.

5. The Drunkard's Walk: How Randomness Rules Our Lives

Quality Score: 79/100

With the born storyteller's command of narrative and imaginative approach, Leonard Mlodinow vividly demonstrates how our lives are profoundly informed by chance and randomness and how everything from wine ratings and corporate success to school grades and political polls are less reliable than we believe. By showing us the true nature of chance and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives us the tools we need to make more informed decisions. From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire.

6. The Visual Display of Quantitative Information

Quality Score: 72/100

The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information.

7. Superforecasting: The Art and Science of Prediction

Quality Score: 72/100

Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?

8. The Wisdom of Crowds

Quality Score: 72/100

In this fascinating book, New Yorker business columnist James Surowiecki explores a deceptively simple idea: Large groups of people are smarter than an elite few, no matter how brilliant—better at solving problems, fostering innovation, coming to wise decisions, even predicting the future. With boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, behavioral economics, artificial intelligence, military history, and politics to show how this simple idea offers important lessons for how we live our lives, select our leaders, run our companies, and think about our world.

9. Applied Predictive Modeling

Quality Score: 58/100

Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance―all of which are problems that occur frequently in practice.