Dataconomy

MARCH 17, 2025

They often play a crucial role in clustering and segmenting data, helping businesses identify trends without prior knowledge of the outcome. K-Means K-Means clustering is a technique that segments data into distinct groups based on similarities.

Decision Trees 91

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AWS Machine Learning Blog

FEBRUARY 25, 2025

SVM-based classifier: Amazon Titan Embeddings In this scenario, it is likely that user interactions belonging to the three main categories ( Conversation , Services , and Document_Translation ) form distinct clusters or groups within the embedding space. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

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Data Science Dojo

AUGUST 24, 2023

By enabling faster development time, better model performance, more reliable deployments, and enhanced efficiency, MLOps is instrumental in unlocking the full potential of harnessing ML for business intelligence and strategy. Examples include: Cross-validation techniques for better model evaluation.

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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

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Pickl AI

SEPTEMBER 12, 2024

Importance of Data Science Data Science is crucial in decision-making and business intelligence across various industries. Business Intelligence (BI): Analysing data to support decision-making and improve business performance.

Data Analyst 52

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Mlearning.ai

MAY 23, 2023

In the final stage, the results are communicated to the business in a visually appealing manner. This is where the skill of data visualization, reporting, and different business intelligence tools come into the picture. What is Cross-Validation? Perform cross-validation of the model.

Data Science 52

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Pickl AI

APRIL 26, 2024

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Then, I would use clustering techniques such as k-means or hierarchical clustering to group customers based on similarities in their purchasing behaviour. In my previous role, we had a project with a tight deadline.

Data Analyst 52

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