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. Figure 1: The main components of a model as defined by banking industry regulators.

Risk 111

Risk Modeling IT Risk Management 111

CIO Business Intelligence

OCTOBER 13, 2023

Here are the top 11 roles companies are currently hiring for, or have plans to hire for, to directly address their emerging gen AI strategies. It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. genetic counseling, genetic testing).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Smarten

JUNE 20, 2018

The business can use this information for forecasting and planning, and to test theories and strategies. In prediction, the objective is to “model” all the components to some trend patterns to the point that the only component that remains unexplained is the random component. Linear Trend. Stationary/Stationarity.

Forecasting 40

Forecasting Statistics Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more variables (X, and Y). For instance, if promotions and holiday seasons are significant factors, these factors should be given more focus when devising a marketing strategy. What is Multiple Linear Regression Analysis?

Business Analysis 40

Business Analysis IT Sales Statistics 40

Smarten

JUNE 26, 2018

Business Problem: A research agency wants to predict the likelihood of each election candidate being voted on by each voter and in turn devise a strategy to take proactive steps. Let’s look at two use cases: Use Case – 1.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 25, 2018

Sales team segments that are facing challenges based on any current discounting strategy can be identified and a deal negotiation strategy can be improved and optimized. Business Benefit: The business marketing team can focus on risky customer segments in an efficient way in order to avoid losing those customers.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 29, 2018

The enterprise can also identify any gap between the targeted and estimated growth and develop an appropriate strategy to reduce this gap in order to achieve targets and results. Business Benefit: By analyzing the various combinations of predictor variables, the business can forecast product growth, trends, patterns and seasonality, if any.

Forecasting 40

Forecasting Enterprise IT Business Intelligence 40

Birst BI

AUGUST 23, 2018

For example, there are a plethora of software tools available to automatically develop predictive models from relational data, and according to Gartner, “By 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.” [1]

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

Birst BI

OCTOBER 30, 2018

The foundation of predictive analytics is based on probabilities. To generate accurate probabilities of future behavior, predictive analytics combine historical data from any number of applications with statistical algorithms. A well-designed credit scoring algorithm will properly predict both the low- and high-risk customers.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Smarten

JUNE 27, 2018

In this example, the segments that have a response rate higher than the overall response rate can be targeted first since they will require less effort to convert to a purchase, whereas a different marketing strategy must be devised for the lower segments (segments that have a response rate less than the overall rate).

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Insight

AUGUST 5, 2020

Summary statistics (i.e. This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion. the Mel-frequency cepstrum).

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Let’s also look at the basic descriptive statistics for all attributes. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. You can add a Makefile command test that will run all of your notebooks by issuing. test: py.test --nbval notebooks/*.ipynb. 05) in predicting changes in attendance. The lower the RMSE, the better the prediction.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Smarten

JUNE 26, 2018

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with better strategy of products placement and product bundling. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

Business Intelligence 40

Business Intelligence Sales Predictive Modeling Statistics 40

The Unofficial Google Data Science Blog

MARCH 31, 2016

Finally, through a case study of a real-world prediction problem, we also argue that Random Effect models should be considered alongside penalized GLM's even for pure prediction problems. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

AUGUST 20, 2019

The problem is that a new unique identifier of a test example won’t be anywhere in the tree. Feature extraction means moving from low-level features that are unsuitable for learning—practically speaking, we get poor testing results—to higher-level features which are useful for learning. Separate out a hold-out test set.

Testing 68

Testing Modeling Interactive Measurement 68

Smarten

JUNE 29, 2018

Business Problem: A retail store manager wants to conduct Market Basket analysis to come up with a better strategy of product placement and product bundling. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

Business Analysis 40

Business Analysis IT Business Intelligence Sales 40

Smarten

DECEMBER 5, 2022

Gartner predicts that, ‘90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.’. Paired Sample T Test. Chi Square Test. Independent Sample T Test. Descriptive Statistics. Multiple Linear Regression. ARIMAX Forecasting.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Testing 52

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

datapine

NOVEMBER 27, 2019

In 2020, BI tools and strategies will become increasingly customized. Accordingly, the rise of master data management is becoming a key priority in the business intelligence strategy of a company. However, businesses today want to go further and predictive analytics is another trend to be closely monitored.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Smarten

JUNE 21, 2018

Time series forecasting methods are used to extract and analyze data and statistics and characterize results to more accurately predict the future based on historical data. Business Benefit : If projected claims are lower than expected then proper marketing strategy can be devised to improve sales.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40

Jet Global

MAY 1, 2023

Other money-making strategies include adding users in a per-seat structure or achieving price dominance in the market due. This strategy will ultimately increase sales, and prove a competitive advantage. Advanced Analytics Provide the unique benefit of advanced (and often proprietary) statistical models in your app.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52