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Reliable data. Faster decisions. Better execution.

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Python code implementing linear regression with L2 regularization, including functions for prediction, cost computation, gradient calculation, and gradient descent optimization with early stopping and iteration logging.
Python code defining RandomForestClassifier evaluation with varying parameters, collecting results, and setting XGBoost parameters for multiclass or binary classification tasks.
Python code defining and training four sequential neural network models with varying layer sizes and regularization using TensorFlow Keras.
Python code defining and training four sequential neural network models with varying layer sizes and regularization using TensorFlow Keras.
Python code snippet setting XGBoost classifier parameters, training the model with early stopping, and printing best iteration and accuracy.
Python code implementing Gaussian probability density for anomaly detection, calculating precision, recall, F1 score, and finding best epsilon threshold.

Challenge. Learn. Transform.
‍We'll guide you every step of the way

Circular data lifecycle diagram with eaQbe at the center (Your Decision Intelligence Partner), showing a loop: Data Foundations → Data Integration (ETL) → Analytics & ML → BI & Visualization → Decisions & Actions (agentic execution) → Continuous Improvement.

We design resilient data architectures that combine robust foundations with modern BI, Machine Learning, and agentic workflows

Our solutions

Three levers to turn data into operational results

Transform your workforce into data-driven experts

Data science & BI Trainings

Scale your operations with an intelligent digital workforce

Agentic Workflows

Align your technology and strategy with actionable Data Intelligence

Data consulting

Our methodology

A structured consulting approach grounded in CRISP-DM and delivered through Scrum

Step 1
Step 2
Step 3

1. Frame the challenge

We start by structuring the project at business level: stakeholder alignment, governance setup, and qualitative interviews across departments. Through steering committees and structured discussions, we clarify objectives, success criteria, constraints, and interdependencies. In parallel, we assess available data sources to ground decisions in operational reality.

2. Build and iterate

We translate business objectives into data pipelines and models. This phase covers features engineering, and iterative modeling, with frequent validation points. Working in short cycles, we test assumptions, refine configurations, and ensure models remain explainable, robust, and aligned with business intent.

3. Validate and operationalize

We evaluate results against the initial business objectives. Once validated, we deploy solutions into existing systems, support user adoption, and document outcomes. This phase closes the loop with stakeholders and sets the foundation for continuous improvement.

From passion to impact eaQbe

Built on a deep commitment to data science and technology, we are driven by a bold vision: making data science accessible, actionable, and transformative for businesses of all sizes.

Expertise

We bring engineering-level rigor to business constraints, turning complex data into reliable systems.

Innovation

We continuously explore new methods, architectures, and workflows to better align technology with business realities.

Learning

We design our work to build autonomy, by breaking complexity down, transferring knowledge, and enabling teams to operate, adapt, and improve over time.

✦ About Us

Data Systems Building Blocks

We design and integrate the right components to build reliable, maintainable data systems within your existing environment.

✦ Contact us

Read our latest resources on docs.eaQbe.com

Browse all posts

Demystifying Neural Networks & Deep Learning

A comprehensive guide to understanding the "Black Box" of hidden layers. Learn how hardware evolution and Big Data revived Neural Networks, and explore the math behind Gradient Descent and Activation Functions.

Decision Trees & Ensemble Methods

A comprehensive guide to interpretable supervised learning. Understand the mechanics of recursive partitioning and Information Gain, and learn how to evolve simple trees into powerful Random Forest and XGBoost models.

CRISP-DM: The Data Science Lifecycle

The essential framework for managing data science projects. Master the six iterative phases, from Business Understanding to Deployment ; and learn to combine CRISP-DM with SCRUM to transform raw data into actionable value.

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