KDnuggets

DECEMBER 1, 2023

Image by Author When you are getting started with machine learning, logistic regression is one of the first algorithms you’ll add to your toolbox. It's a Read more »

Predictive Modeling 160

Predictive Modeling Modeling Machine Learning IT 160

David Menninger's Analyst Perspectives

SEPTEMBER 16, 2021

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

Data Science 341

Data Science Data Lake Data mining Deep Learning 341

Analytics Vidhya

JUNE 26, 2024

Introduction In the field of machine learning, developing robust and accurate predictive models is a primary objective. Ensemble learning techniques excel at enhancing model performance, with bagging, short for bootstrap aggregating, playing a crucial role in reducing variance and improving model stability.

Machine Learning 304

Machine Learning Predictive Modeling Modeling Analytics 304

Analytics Vidhya

MARCH 22, 2022

Introduction Feature analysis is an important step in building any predictive model. This article was published as a part of the Data Science Blogathon. It helps us in understanding the relationship between dependent and independent variables.

Predictive Modeling 332

Predictive Modeling Data Science Publishing Modeling 332

Analytics Vidhya

MARCH 5, 2023

And our goal is to create a predictive model, such as Logistic Regression, etc. so that when we give the characteristics of a candidate, the model can predict whether they will recruit. The dataset revolves around the placement season of a Business School in India.

Predictive Modeling 335

Predictive Modeling Strategy Modeling Analytics 335

Analytics Vidhya

MAY 26, 2023

Imagine diving into the details of data analysis, predictive modeling, and ML. Envision yourself unraveling the insights and patterns for making informed decisions that shape the future. The concept of Data Science was first used at the start of the 21st century, making it a relatively new area of research and technology.

Data Science 319

Data Science Predictive Modeling Modeling Technology 319

Analytics Vidhya

FEBRUARY 25, 2023

Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.

Predictive Modeling 313

Predictive Modeling Testing Machine Learning Modeling 313

Analytics Vidhya

MAY 2, 2019

Introduction What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often. The post Statistics 101: Introduction to the Central Limit Theorem (with implementation in R) appeared first on Analytics Vidhya.

Statistics 300

Statistics Predictive Modeling Modeling Analytics 300

CIO Business Intelligence

NOVEMBER 27, 2024

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

Data Architecture 128

Data Architecture Cost-Benefit Machine Learning Experimentation 128

O'Reilly on Data

JANUARY 17, 2023

A common task for a data scientist is to build a predictive model. If it does, you suspect that the variable you’re trying to predict has mixed in with the variables used to predict it. You might say that the outcome of this exercise is a performant predictive model. That’s sort of true.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

Smarten

FEBRUARY 19, 2025

To accomplish these goals, businesses are using predictive modeling and predictive analytics software and solutions to ensure dependable, confident decisions by leveraging data within and outside the walls of the organization and analyzing that data to predict outcomes in the future.

Predictive Analytics 64

Predictive Analytics Analytics Predictive Modeling Forecasting 64

CIO Business Intelligence

DECEMBER 19, 2024

One of the fastest-growing industries in the world, climate tech and its companion area of nature tech require a wide range of skills to help solve significant environmental problems. In especially high demand are IT pros with software development, data science and machine learning skills.

IT 52

IT IoT Machine Learning Optimization 52

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

CIO Business Intelligence

MAY 21, 2024

With the generative AI gold rush in full swing, some IT leaders are finding generative AI’s first-wave darlings — large language models (LLMs) — may not be up to snuff for their more promising use cases. With this model, patients get results almost 80% faster than before.

IT 131

IT Cost-Benefit Experimentation Forecasting 131

CIO Business Intelligence

SEPTEMBER 10, 2024

We envisioned harnessing this data through predictive models to gain valuable insights into various aspects of the industry. This included predicting political outcomes, such as potential votes on pipeline extensions, as well as operational issues like predicting the failure of downhole submersible pumps, which can be costly to repair.

IT 122

IT Risk Risk Management Predictive Modeling 122

CIO Business Intelligence

MAY 14, 2024

You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. Private cloud platforms can leverage generative AI for anomaly detection applications in various domains, including cybersecurity, fraud detection, and predictive maintenance,” he says. The Milford, Conn.-based

IT 143

IT Data Processing Enterprise Modeling 143

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. It will also be a year of collaborative BI and artificial intelligence.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. Training and running these models require massive computing power, leading to a significant carbon footprint.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

CIO Business Intelligence

MARCH 19, 2025

Predictive modeling can help companies optimize energy consumption, while AI-driven insights can identify supply chain inefficiencies that lead to excessive waste. This lack of clear ROI can make it challenging for CDOs to justify sustainability investments to key decision-makers.

IT 59

IT Data Governance Data-driven Metrics 59

Smarten

JUNE 20, 2023

Citizen Data Scientists Can Use Assisted Predictive Modeling to Create, Share and Collaborate! Gartner has predicted that, ‘40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’

Predictive Modeling 52

Predictive Modeling Modeling Forecasting Data Science 52

CIO Business Intelligence

JULY 28, 2022

And machine learning engineers are being hired to design and build automated predictive models. I think it is very important because the algorithms this team is writing are helping our business to predict likely outcomes and make better decisions. “I To deal with this hiring problem, they’ve had to get creative.

Data Science 143

Data Science IT Machine Learning Data-driven 143

CIO Business Intelligence

AUGUST 20, 2024

For example: City planning can be revolutionized through AI-driven urban digital twin models, predictive modeling, and simulations that empower city officials to make informed decisions, anticipate challenges, and proactively shape their future direction.

IT 122

IT Internet of Things IoT Data-driven 122

Dataiku

FEBRUARY 5, 2024

We’re back with the final article of our three-part series on building our first predictive model. We’ve laid the groundwork , learned how to build and evaluate the model , and now we want to learn how to interpret it.

Modeling 90

Modeling Predictive Modeling IT 90

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 139

Predictive Analytics Analytics Predictive Modeling Data mining 139

CIO Business Intelligence

MAY 17, 2022

The new features include simplified self-service tools like Data Stories, smart suggestions through Einstein Discovery, and collaboration tools to work on shared data models. Having been acquired by Salesforce in 2019 , Tableau is also deepening ties with its parent company’s AI capabilities, which are branded as Einstein.

IT 98

IT Predictive Modeling Machine Learning Dashboards 98

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. If accurate, this prediction can help a retailer ensure that they do not run out of stock, which means that there is no lost revenue because a product is out of stock. Conclusion.

Advertising 362

Advertising Cost-Benefit ROI Machine Learning 362

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data science team may be focused on feature importance metrics, feature engineering, predictive modeling, model explainability, and model monitoring. What is a semantic layer? That’s a good question, but let’s first explain semantics. The way that I explained it to my data science students years ago was like this.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. As machine learning advances globally, we can only expect the focus on model risk to continue to increase.

Risk 111

Risk Modeling IT Risk Management 111

O'Reilly on Data

MARCH 24, 2020

This is important: consider how many modern models need to operate at scale and in real time (such as Google’s search engine and the relevant tweets that Twitter surfaces in your feed). Model results will then be less reliant on individuals making hundreds of micro-decisions. Why is it important to automate data preparation?

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics in business Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

Smart Data Collective

MARCH 3, 2021

The impact of predictive modelling on personal injury cases. Predictive modelling is a technology that evolved together with big data analytics. Predictive modelling handles the less obvious or even hidden claim outcomes. The legal sector is still in its infancy when it comes to big data and analytics.

Analytics 133

Analytics Big Data Predictive Modeling Insurance 133

DataKitchen

SEPTEMBER 21, 2023

Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes. Because let’s face it, your customers don’t care where the problem originated—they want it fixed and fast. What is Data in Place?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Rocket-Powered Data Science

FEBRUARY 12, 2021

Interest in AI is high and growing, specifically in the areas of smart analytics, customer-centricity, chatbots, and predictive modeling. In the rest of this article, we will refer to IPA as intelligent automation (IA), which is simply short-hand for intelligent process automation. So, what about Intelligent Automation?

ROI 234

ROI Digital Transformation Reporting Machine Learning 234

Rocket-Powered Data Science

JULY 6, 2021

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. MLOps “done right” addresses sustainable model operations, explainability, trust, versioning, reproducibility, training updates, and governance (i.e.,

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

Cloudera

DECEMBER 4, 2024

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. The emergence of GenAI, sparked by the release of ChatGPT, has facilitated the broad availability of high-quality, open-source large language models (LLMs).

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” Cloud computing? BI is useful.

Machine Learning 336

Machine Learning Testing Modeling Cost-Benefit 336

Smarten

NOVEMBER 26, 2024

Today, organizations look to data and to technology to help them understand historical results, and predict the future needs of the enterprise to manage everything from suppliers and supplies to new locations, new products and services, hiring, training and investments. But too much data can also create issues.

Machine Learning 64

Machine Learning Data Quality Predictive Modeling Metadata 64