Analytics Vidhya

DECEMBER 4, 2022

Source: Canva Introduction The real-world data can be very messy and skewed, which can mess up the effectiveness of the predictive model if it is not addressed correctly and in time. The consequences of skewness become more pronounced when a large model is […].

Predictive Modeling 372

Predictive Modeling Data Science Publishing Modeling 372

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

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

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

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

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 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

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

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

O'Reilly on Data

JANUARY 17, 2023

Building Models. 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. (If You might say that the outcome of this exercise is a performant predictive model.

Machine Learning 363

Machine Learning Predictive Modeling Software Modeling 363

CIO Business Intelligence

APRIL 24, 2025

Select a suitable revenue model Leverage subscription-based approaches and commercialization strategies for direct sales to businesses, research institutions, or government agencies, Sikichs Young advises. Data-as-a-service, where companies compile and package valuable datasets, is the base model for monetizing data, he notes.

Data-driven 134

Data-driven Marketing Risk Sales 134

CIO Business Intelligence

FEBRUARY 6, 2025

The hype around large language models (LLMs) is undeniable. 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. This article reflects some of what Ive learned.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

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 62

Predictive Analytics Analytics Predictive Modeling Forecasting 62

CIO Business Intelligence

MARCH 22, 2022

Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Nvidia claims it can do so up to 45,000 times faster than traditional numerical prediction models.

Modeling 144

Modeling Machine Learning Forecasting Optimization 144

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

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

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code. .”

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

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’s fabulous.”

IT 131

IT Cost-Benefit Experimentation Forecasting 131

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 339

Machine Learning Testing Modeling Cost-Benefit 339

Rocket-Powered Data Science

SEPTEMBER 20, 2021

The data scientists need to find the right data as inputs for their models — they also need a place to write-back the outputs of their models to the data repository for other users to access. The semantic layer bridges the gaps between the data cloud, the decision-makers, and the data science modelers. What is a semantic layer?

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

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

CIO Business Intelligence

DECEMBER 19, 2024

Government agencies and nonprofits are looking for data scientists and engineers to help with climate modeling and environmental impact analysis. 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.

IT 52

IT IoT Machine Learning Optimization 52

DataRobot Blog

JULY 22, 2022

Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.

Modeling 105

Modeling Risk Management Testing Machine Learning 105

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.’ The team can share the models and, in so doing, learn from the process.

Predictive Modeling 52

Predictive Modeling Modeling Forecasting Data Science 52

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. Outputs from trained AI models include numbers (continuous or discrete), categories or classes (e.g., The Challenge with Defining AI Goals. It also requires buy-in and alignment at the C-level.

Advertising 362

Advertising Cost-Benefit ROI Machine Learning 362

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

MAY 14, 2024

Enterprises need to ensure that private corporate data does not find itself inside a public AI model,” McCarthy says. You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. We’re keeping that tight control and keeping it in the private cloud.”

IT 143

IT Data Processing Enterprise Modeling 143

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

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

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 Cloudera AI Inference service is a highly scalable, secure, and high-performance deployment environment for serving production AI models and related applications.

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

CIO Business Intelligence

JULY 28, 2022

They specialize in building powerful algorithms, and analyzing, processing, and modeling data so they can then interpret the results to create actionable plans. They specialize in building powerful algorithms, and analyzing, processing, and modeling data so they can then interpret the results to create actionable plans.

Data Science 143

Data Science IT Machine Learning Data-driven 143

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

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

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. from 2022 to 2028.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

CIO Business Intelligence

MARCH 19, 2025

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. However, embedding ESG into an enterprise data strategy doesnt have to start as a C-suite directive.

IT 59

IT Data Governance Data-driven Metrics 59