<ul><li class="ql-align-justify">Predictive models can be biased if the data used to create them is biased.</li><li class="ql-align-justify">For example, if past hiring decisions were biased, the model may predict unfair hiring outcomes.</li><li class="ql-align-justify">It’s important to examine data carefully to avoid unintentional bias.</li></ul>

Bias in Data and Models

<ul><li class="ql-align-justify">Machine learning algorithms are the backbone of predictive analytics.</li><li class="ql-align-justify">These algorithms learn from data and make predictions without being explicitly programmed.</li><li class="ql-align-justify">Over time, they improve and refine their predictions as more data is fed into them.</li></ul>

The Role of Machine Learning

<ul><li class="ql-align-justify">Predictive models look for patterns in past data to forecast future events.</li><li class="ql-align-justify">Techniques include regression, decision trees, and machine learning.</li><li class="ql-align-justify">These models get smarter over time with more data.</li></ul>

Understanding Predictive Modeling

<ul><li class="ql-align-justify">By predicting customer preferences, companies can tailor their offerings.</li><li class="ql-align-justify">It’s used to personalize marketing, improve customer service, and increase sales.</li><li class="ql-align-justify">Companies can predict when a customer might leave and intervene to keep them.</li></ul>

Predictive Analytics Can Enhance Customer Experience

<ul><li class="ql-align-justify">Predictive analytics raises questions about privacy and fairness.</li><li class="ql-align-justify">Companies must ensure that they’re using data responsibly and ethically.</li><li class="ql-align-justify">The power of predictive analytics should be used to benefit society, not harm it.</li></ul>

Ethical Considerations

<ul><li class="ql-align-justify">It uses historical data to predict future outcomes.</li><li class="ql-align-justify">Companies use it to make better decisions and predict customer behaviors.</li><li class="ql-align-justify">It’s becoming essential in industries like healthcare, finance, and retail.</li></ul>

Predictive Analytics is the Future of Decision-Making

<ul><li class="ql-align-justify">Predictive models rely on large sets of historical data.</li><li class="ql-align-justify">The more accurate and varied the data, the better the predictions.</li><li class="ql-align-justify">Data sources include sales records, social media, and even sensor data.</li></ul>

Data is Key to Predictive Analytics

<ul><li class="ql-align-justify">Predictive analytics is increasingly becoming a core business tool.</li><li class="ql-align-justify">It is used to optimize everything from supply chains to marketing strategies.</li><li class="ql-align-justify">As technology evolves, its potential to revolutionize industries grows even further.</li></ul>

The Growing Importance of Predictive Analytics

<ul><li class="ql-align-justify">Predictive analytics helps you without you even knowing it.</li><li class="ql-align-justify">Examples include movie recommendations on Netflix or credit card fraud detection.</li><li class="ql-align-justify">It’s also used to predict things like weather, stock prices, or disease outbreaks.</li></ul>

Applications in Everyday Life

<ul><li class="ql-align-justify">Predictive models are not 100% accurate and can fail in unexpected ways.</li><li class="ql-align-justify">Data privacy and security concerns arise when using personal data for predictions.</li><li class="ql-align-justify">Some industries face challenges due to the complexity of their data.</li></ul>

Challenges in Predictive Analytics

Without data, you're just another person with an opinion.

Overview of predictive analytics and its real-world applications

"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: <ul> <li>What type of mortgage risk Chase Bank predicted before the recession.</li> <li>Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves.</li> <li>Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.</li> <li>Five reasons why organizations predict death — including one health insurance company.</li> <li>How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual.</li> <li>Why the NSA wants all your data: machine learning supercomputers to fight terrorism.</li> <li>How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!</li> <li>How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.</li> <li>How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison.</li> <li>182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. </li> </ul> How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Bias in Data and Models

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