Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. The Role of Big Data. Client Data Accessibility.

Big Data 141

Big Data Finance Risk Management Risk 141

Smarten

NOVEMBER 8, 2022

By providing this course as a free online offering Smarten hopes to further support and encourage users and businesses to embrace the very real benefits of the Citizen Data Scientist approach to analytics and objective, data-driven metrics and results. About Smarten.

Business Intelligence 52

Business Intelligence Predictive Modeling Statistics Data-driven 52

Smarten

JUNE 10, 2022

Predictive Modeling to support business needs, forecast, and test theories. KPIs allow the business to establish and monitor KPIs for objective metrics. Assisted Predictive Modeling. Key Performance Indicators (KPIs).

Slice and Dice 52

Slice and Dice Analytics Key Performance Indicator Business Intelligence 52

IBM Big Data Hub

SEPTEMBER 19, 2023

Prescriptive analytics: Prescriptive analytics predicts likely outcomes and makes decision recommendations. An electrical engineer can use prescriptive analytics to digitally design and test out various electrical systems to see expected energy output and predict the eventual lifespan of the system’s components.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

IBM Big Data Hub

JUNE 29, 2023

In order to take a proactive approach to asset reliability, maintenance managers rely on two widely used metrics: mean time between failure, (MTBF) and mean time to repair (MTTR). Both KPIs help predict how assets will perform and assist managers in planning preventive and predictive maintenance.

Cost-Benefit 86

Cost-Benefit Measurement Management Predictive Analytics 86

CIO Business Intelligence

MAY 26, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 121

Advertising Machine Learning Software Measurement 121

Birst BI

OCTOBER 30, 2018

Descriptive analytics techniques are often used to summarize important business metrics such as account balance growth, average claim amount and year-over-year trade volumes. The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers. Accounts in use.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Juice Analytics

MARCH 11, 2021

3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about. Predictive models to take descriptive data and attempt to tell the future. He also tests data accuracy and product functionality.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

CIO Business Intelligence

MAY 28, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 113

Advertising Machine Learning Software Measurement 113

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Moreover, advanced metrics like Percentage Regional Sales Growth can provide nuanced insights into business performance. What is Data in Use?

Testing 169

Testing Data Quality Predictive Modeling Metrics 169

Business Over Broadway

SEPTEMBER 27, 2018

genetic counseling, genetic testing). Analytics in these types of projects may be less valuable due to lack of generalizability (to the other customers) and poor models (e.g., underspecified) due to omitted metrics. Machine Learning and Predictive Modeling of Customer Churn. segmentation on steroids).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

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

MLWhiz

NOVEMBER 30, 2014

In online advertising, click-through rate (CTR) is a very important metric for evaluating ad performance. As a result, click prediction systems are essential and widely used for sponsored search and real-time bidding. For this competition, we have provided 11 days worth of Avazu data to build and test prediction models.

Advertising 40

Advertising Predictive Modeling Metrics Testing 40

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. On a broader level, it asks if machines can demonstrate human intelligence.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. This might require making batch and individual predictions. CML supports model prediction in either batch mode or via a RESTful API for individual model predictions.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

IBM Big Data Hub

OCTOBER 16, 2023

ML also provides the ability to closely monitor a campaign by checking open and clickthrough rates, among other metrics. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Many stock market transactions use ML.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

CIO Business Intelligence

MARCH 5, 2024

CIOs and IT leaders are at the center and must decide what copilots to test, who should receive access, and whether experiments are delivering business value. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation. Generative AI, IT Strategy

Cost-Benefit 117

Cost-Benefit Advertising Experimentation Software 117

Domino Data Lab

APRIL 21, 2021

from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set." Fraudulent transactions are 0.17% of the test set. 2f%% of the test set." Fraudulent transactions are 50.00% of the test set. Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

DataKitchen

JANUARY 17, 2022

Testing and validating analytics took as long or longer than creating the analytics. The business analysts creating analytics use the process hub to calculate metrics, segment/filter lists, perform predictive modeling, “what if” analysis and other experimentation. Requirements continually change. Data is not static.

Experimentation 130

Experimentation Data Lake Marketing Predictive Modeling 130

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. And I meant every word of it.

Testing 112

Testing Experimentation Risk Optimization 112

DataRobot Blog

DECEMBER 1, 2022

As you upload your data, DataRobot will do some initial exploratory data analysis to get a deeper understanding of the dataset prior to model training. Next, choose your target variable—in this instance it is automatically detected as a classification problem and an optimization metric is recommended. Automate Feature Engineering.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. The Leaderboard of trained models—ordered based on your metric.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

IBM Big Data Hub

JULY 5, 2023

It allows the company to run tests and predict performance based on simulations. With a good digital twin, it’s possible to predict how an asset will perform under certain conditions and what it’s projected lifespan will be. A digital twin is a virtual representation of an asset that a company intends to purchase.

Strategy 57

Strategy Management IoT Cost-Benefit 57

CIO Business Intelligence

AUGUST 15, 2023

In partnership with OpenAI and Microsoft, CarMax worked to develop, test, and iterate GPT-3 natural language models aimed at achieving those results. The CarMax team also gathered, scrubbed and formatted data from thousands of vehicles to feed into the models, fine-tuning them as the project advanced.

IT 94

IT Manufacturing IoT Cost-Benefit 94

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. 42% of data scientists are solo practitioners or on teams of five or fewer people. The platform has parity with the core Jupyter capabilities, so users are able to onboard without a steep learning curve.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

A single model may also not shed light on the uncertainty range we actually face. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range. Over the life of the forecast, the data scientist will publish historical accuracy metrics.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Insight

FEBRUARY 19, 2020

I held out 20% of this as a test set and used the remainder for training and validation. Feature Selection and Engineering Most of the inputs to my model were taken either as is from the data source, or with minimal processing. Scatterplot of the predicted ROI vs. the true ROI for the hold-out test set. A New Hope ).

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

AUGUST 22, 2019

GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. This test set is available at download.tensorflow.org/data/questions-words.txt.].

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

OCTOBER 23, 2019

You can add a Makefile command test that will run all of your notebooks by issuing. test: py.test --nbval notebooks/*.ipynb. One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic. In Figure 6.8,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Smarten

JUNE 29, 2018

Given a set of transactions, this process aims to find the rules that enable us to predict the occurrence of a specific item based on the occurrence of other items in the transaction. While there are numerous metrics and factors used in this technique, for this example, we will only consider two factors namely, Support and Confidence.

Business Analysis 40

Business Analysis IT Business Intelligence Sales 40

Occam's Razor

JANUARY 7, 2019

There is too much goodness in modeling that you are not taking advantage of. From segmentation models to identifying incrementality to predictive modeling to survival analysis to clustering to time series to… I could keep going on and on. Hire an experienced statistician to be a part of your analytics team.

Marketing Analytics 88

Marketing Analytics Marketing Analytics Strategy 88

datapine

JANUARY 31, 2022

Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated. This methodology of “test, look at the data, adjust” is at the heart and soul of business intelligence. 5) Find improvement opportunities through predictions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

NOVEMBER 18, 2014

Short story #2: Predictive Modeling, Quantifying Cost of Inaction. You can hover over each box to get a sense of the key metrics. Short story #2: Predictive Modeling, Quantifying Cost of Inaction. Thank goodness for predictive models. Short story #1: Treemaps, Sunbursts, Packed Trees, Oh My!

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

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. That’s enterprise-wide agile curiosity, question-asking, hypothesizing, testing/experimenting, and continuous learning. That’s data democratization.

Data Science 285

Data Science Forecasting Business Intelligence Sales 285