ODSC - Open Data Science

SEPTEMBER 8, 2023

At this Fall’s Open Data Science Conference , I will talk about how to bring a systematic approach to the interpretation of clustering models. To get ready for that, let’s talk about data visualization for clustering models. data # center and scale clusterable features diabetesScaler = MinMaxScaler().fit(diabetesData)

Clustering 52

Clustering Data Science Data Scientist Predictive Analytics 52

Smart Data Collective

SEPTEMBER 15, 2021

Since Hadoop is designed to work with large computer clusters made from inexpensive commodity-grade PC hardware, it’s uniquely attractive to smaller businesses that need the same kind of power found at larger organizations without the upfront infrastructure investment. Leveraging Hadoop’s Predictive Analytic Potential.

Hadoop 130

Hadoop Apache Hadoop Predictive Analytics Database 130

Data Science Blog

MAY 22, 2023

der k-Nächste-Nachbarn -Prädiktionsalgorithmus (Regression/Klassifikation) oder K-Means-Clustering. Die Texte müssen in diese transformiert werden, eventuell auch nach diesen in Cluster eingeteilt und für verschiedene Trainingsszenarien separiert werden. Die Ähnlichkeitsbetrachtung erfolgt mit Distanzmessung im Vektorraum.

Deep Learning 244

Deep Learning Deep Learning Natural Language Processing AI 244

Pickl AI

JANUARY 21, 2025

K-Means Clustering K-means clustering partitions data into k distinct clusters based on feature similarity. It iteratively assigns points to clusters and updates centroids until convergence. Example: Organising documents into a tree structure based on topic similarity for better information retrieval systems.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

IBM Journey to AI blog

DECEMBER 20, 2023

Supervised learning is commonly used for risk assessment, image recognition, predictive analytics and fraud detection, and comprises several types of algorithms. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g., temperature, salary).

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Smart Data Collective

OCTOBER 13, 2021

Predictive analytics is an area of big data analysis that facilitates the identification of trends, exceptions and clusters of events, and all this allows forecasting future trends that affect the business. Prescriptive analytics. In forecasting future events.

Data Visualization 145

Data Visualization Big Data Big Data Predictive Analytics 145

Data Science Dojo

AUGUST 11, 2023

Models: Bridging data and predictive insights Models, in the context of data science, are mathematical representations of real-world phenomena. They play a pivotal role in predictive analytics and machine learning, enabling data scientists to make informed forecasts and decisions based on historical data patterns.

Data Science 195

Data Science Python Data Scientist Decision Trees 195

IBM Journey to AI blog

AUGUST 9, 2024

It extracts insights from historical data to make accurate predictions about the most likely upcoming event, result or trend. In short, predictive AI helps enterprises make informed decisions regarding the next step to take for their business.

AI 120

AI AI Decision Trees Algorithm 120

IBM Journey to AI blog

APRIL 1, 2024

Stacking strong data management, predictive analytics and GenAI is foundational to taking your product organization to the next level. Using standard data analytics practices, businesses can identify patterns and clusters within data to enable more accurate targeting of customers.

AI 95

AI AI Predictive Analytics Clustering 95

Data Science Blog

FEBRUARY 23, 2023

In this case, original data distribution have two clusters of circles and triangles and a clear border can be drawn between them. However in fact, with a help of unlabeled data in dotted lines, machine learning model might be able to recognize two clusters with a help of unlabeled data.

Supervised Learning 264

Supervised Learning Machine Learning Machine Learning Deep Learning 264

Pickl AI

FEBRUARY 2, 2025

Applications of Associative Classification Associative classification is a versatile technique used across multiple industries to improve decision-making and predictive analytics. It provides a collection of Machine Learning algorithms for data mining tasks such as classification, regression, clustering, and association rule mining.

Data Mining 52

Data Mining Data Mining Data Mining Decision Trees 52

Data Science Dojo

JULY 2, 2024

Unsupervised Learning : The system learns patterns and structures in unlabeled data, often identifying hidden relationships or clustering similar data points. Integrate data from various sources, preprocess it on the fly, and use predictive analytics to make immediate decisions.

AI 195

AI AI Algorithm Machine Learning 195

Data Science 101

OCTOBER 31, 2019

Banks use classification to predict if a client is going to default loan payment or not based on the client’s activities. It is a supervised learning technique used in predictive analytics to find a continuous value based on one or numerous variables. Clustering. Regression.

Data Mining 77

Data Mining Data Mining Data Mining Data Science 77

AWS Machine Learning Blog

JANUARY 30, 2023

In these cases, you might be able to speed up the process by distributing training over multiple machines or processes in a cluster. This post discusses how SageMaker LightGBM helps you set up and launch distributed training, without the expense and difficulty of directly managing your training clusters.

Algorithm 98

Algorithm Clustering Machine Learning Machine Learning 98

Towards AI

JULY 19, 2023

Use the following methods- Validate/compare the predictions of your model against actual data Compare the results of your model with a simple moving average Use k-fold cross-validation to test the generalized accuracy of your model Use rolling windows to test how well the model performs on the data that is one step or several steps ahead of the current (..)

Cross Validation 98

Cross Validation Clustering EDA Data Preparation 98

Dataconomy

MAY 25, 2023

AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions. Here are some key advantages: Enhanced predictive analytics AI-powered IoT devices can predict future outcomes and behaviors based on historical data patterns.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Towards AI

JUNE 27, 2023

With an impressive collection of efficient tools and a user-friendly interface, it is ideal for tackling complex classification, regression, and cluster-based problems. Its functionalities span from deep learning to text mining, data preparation, and predictive analytics, ensuring a versatile utility for developers and data scientists alike.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Pickl AI

AUGUST 1, 2024

Summary: Data mining functionalities encompass a wide range of processes, from data cleaning and integration to advanced techniques like classification and clustering. Clustering: Groups similar data points together without prior knowledge of group membership. Commonly used in market basket analysis to identify product affinities.

Data Mining 52

Data Mining Data Mining Data Mining Clustering 52

AWS Machine Learning Blog

DECEMBER 15, 2023

In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster. On the Import data page, for Data Source , choose DocumentDB and Add Connection.

Machine Learning 111

Machine Learning Machine Learning AWS ML 111

Pickl AI

SEPTEMBER 19, 2024

While both handle vast datasets across clusters, they differ in approach. It distributes large datasets across multiple nodes in a cluster , ensuring data availability and fault tolerance. Data is processed in parallel across the cluster in the map phase, while in the Reduce phase, the results are aggregated.

Hadoop 52

Hadoop Big Data Big Data Clustering 52

Smart Data Collective

SEPTEMBER 17, 2019

AI uses cluster analytics and predictive analytics to audit pages and identify search terms that will be popular in the future. AI predicts customer needs. Search Engine Journal has discussed the role of AI in modern SEO. They said that AI has the following five benefits: AI helps optimize keywords better.

AI 89

AI AI Predictive Analytics Clustering 89

Pickl AI

NOVEMBER 5, 2024

Predictive analytics improves customer experiences in real-time. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics. Key Takeaways Data-driven decisions enhance efficiency across various industries.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

phData

JANUARY 29, 2024

Users can perform a wide range of data operations, such as data cleansing, transformation, blending, modeling, predictive analytics, and spatial analytics. We will walk you through connecting Snowflake Data Cloud to an Alteryx Analytics Cloud Service, with a focus on authentication using an OAuth 2.0 Create a new user.

Database 52

Database Analytics Analytics Clustering 52

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Apache Spark: A fast processing engine that supports both batch and real-time analytics, making it suitable for a wide range of applications. What is Big Data?

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Apache Spark: A fast processing engine that supports both batch and real-time analytics, making it suitable for a wide range of applications. What is Big Data?

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Dataconomy

JUNE 13, 2023

This analysis may involve feature engineering, dimensionality reduction, clustering, classification, regression, or other statistical modeling approaches. Their portfolio includes tools for data exploration, predictive analytics, and decision optimization to support a wide range of business applications.

Power BI 103

Power BI Artificial Intelligence Data Analysis Data Analysis 103

Pickl AI

JULY 12, 2024

Algorithms in ML identify patterns and make decisions, which is crucial for applications like predictive analytics and recommendation systems. Supervised Learning Algorithms In supervised learning , algorithms learn from labelled data to predict outcomes for unseen data points.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

Pickl AI

AUGUST 27, 2024

It provides tools for classification, regression, clustering, and more. Machine Learning and Prediction Machine Learning models are increasingly used in Data Analysis for prediction and decision-making. These models can be used to predict outcomes and make informed decisions.

Data Analysis 52

Data Analysis Data Analysis Python Data Analyst 52

Pickl AI

DECEMBER 14, 2023

Cluster Analysis It involves grouping similar data points based on certain characteristics. In customer segmentation for e-commerce, cluster analysis can help identify distinct customer groups with similar purchasing behaviour, enabling businesses to tailor marketing strategies for each segment.

Data Analysis 52

Data Analysis Data Analysis Decision Trees Analytics 52

Heartbeat

SEPTEMBER 25, 2023

Machine learning is important in predictive analytics, enabling healthcare providers to assess disease risk by analyzing patient data and electronic health records. In this pursuit, predictive analytics powered by machine learning algorithms is a powerful tool for disease risk assessment.

Machine Learning 52

Machine Learning Machine Learning Algorithm Predictive Analytics 52

phData

APRIL 18, 2023

Daily Net Asset Value (NAV) computation, portfolio performance analysis, and reporting can become efficient and reduces time to market, with Snowflake’s multi-cluster concurrency architecture that separates data from computing. If your organization’s prevailing solution is unable to keep pace, perhaps it’s time to restrategize.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Pickl AI

OCTOBER 15, 2024

Applications : Stock price prediction and financial forecasting Analysing sales trends over time Demand forecasting in supply chain management Clustering Models Clustering is an unsupervised learning technique used to group similar data points together. Popular clustering algorithms include k-means and hierarchical clustering.

Decision Trees 52

Decision Trees Hypothesis Testing Clustering Data Analysis 52

Pickl AI

FEBRUARY 21, 2023

Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making. Clustering: This technique groups data points into clusters based on similarity.

Data Mining 52

Data Mining Data Mining Data Mining Data Scientist 52

Dataversity

JUNE 16, 2021

A lousy hit wastes a lot of time and energy predicting the future and understanding the newest trends. The post A Guide to Predictive Data Analytics (Making Decisions for the Future) appeared first on DATAVERSITY. Click to learn more about author Ram Tavva. But those problems […].

Analytics 98

Analytics Analytics Predictive Analytics Clustering 98

Pickl AI

SEPTEMBER 17, 2024

Apache Hadoop Hadoop is an open-source framework that allows for distributed storage and processing of large datasets across clusters of computers using simple programming models. Predictive analytics can help identify potential health risks before they become critical issues.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

FEBRUARY 19, 2025

Improving Operational Efficiency and Predictive Capabilities IoT data visualization enhances efficiency by identifying bottlenecks and optimising processes. Predictive analytics, powered by visual insights, helps forecast equipment failures, energy consumption, and demand patterns.

Data Visualization 52

Data Visualization Power BI Tableau Internet of Things 52

Pickl AI

NOVEMBER 18, 2024

Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. Predictive analytics uses historical data to forecast future trends, such as stock market movements or customer churn.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52