Industrial Machine Learning

Data Driven
Decisions

Doxazo Consulting delivers machine learning solutions for industrial operations. From predictive maintenance to process optimisation and yield forecasting, we turn raw data into decisions that drive real results. Built by engineers, for engineers.

10+
Models Deployed
98.24%
Best Model Accuracy
AWS
Cloud Deployment
SHAP
Explainable AI
Chemical
Engineering
What We Do

Our Services

01
Predictive Maintenance

Detect equipment failures before they occur using advanced LSTM and XGBoost models integrated directly with your control systems.

LSTM XGBoost SCADA
02
Yield Forecasting

Accurate process yield predictions using multivariate deep learning models trained on real operational data from your facility.

LSTM SARIMA Time Series
03
Explainable AI

Every prediction comes with a clear, interpretable explanation using SHAP values so your team understands the reasoning behind every result.

SHAP XGBoost Transparency
04
Classification Models

End-to-end classification pipelines for quality control, anomaly detection and process categorisation across industrial environments.

XGBoost Random Forest SVC Logistic Regression
05
Cloud Deployment

Production-ready machine learning applications deployed on AWS EC2 with Flask, accessible from any browser and running continuously.

AWS EC2 Flask Python
06
BI Dashboards

Interactive Power BI dashboards that transform raw industrial data into actionable insights for operational and management teams.

Power BI DAX Visualisation
Portfolio

Selected Projects

XGBoost · SHAP · Deployed
Breast Cancer Diagnosis
XGBoost classifier with SHAP explainability deployed on AWS EC2. Achieves 97.37% accuracy with individual patient prediction explanations via waterfall charts.
Linear Regression · SHAP · Deployed
Student Productivity Predictor
End-to-end machine learning pipeline with SHAP explanations deployed on AWS EC2. Predicts productivity scores from lifestyle and academic data.
LSTM · Deep Learning · Industrial
Biogas Yield Forecasting
Multivariate LSTM model forecasting biogas yield from temperature, pH and pressure sensor data. Direct industrial application in waste-to-energy operations.
SARIMA · LSTM · Comparison
SARIMA vs LSTM Analysis
Rigorous head-to-head comparison of classical SARIMA and deep learning LSTM models for univariate time series forecasting.
Logistic Regression · SVC · Random Forest
Titanic Survival Prediction
Comparative analysis of three classification models on the Titanic dataset. Evaluated across accuracy, precision and recall with full metric reporting.
SHAP · Explainability · XGBoost
SHAP Explainability Study
Comprehensive study of SHAP values including beeswarm plots, waterfall charts and feature importance analysis for model transparency and interpretability.
Naive Bayes · NLP · Deployed
Email Spam Detection
Compared multiple spam detection models with Naive Bayes achieving the best performance. Deployed live on AWS EC2 as a working web application.
Power BI · Manufacturing · Analytics
Manufacturing Downtime Dashboard
Interactive Power BI dashboard analysing manufacturing downtime patterns, root causes and operational efficiency metrics for industrial decision makers.
Power BI · Churn · Analytics
Customer Churn Insights
Power BI dashboard revealing customer churn patterns with actionable insights designed to guide targeted retention strategy and reduce attrition.
SVR · Random Forest · XGBoost
Regression Model Comparison
Systematic comparison of regression models with hyperparameter tuning, outlier analysis and scaling experiments across continuous target datasets.
SVC · Random Forest · XGBoost
Classification Model Comparison
Full comparison of classification models on categorical targets with GridSearchCV optimisation, metric evaluation and performance benchmarking.
SVC · Optimisation · Tuning
SVC Performance Optimisation
Demonstrates how targeted data cleaning and hyperparameter tuning dramatically improved SVC model performance from a weak baseline to production quality.
Who We Are

About Doxazo Consulting

Doxazo Consulting was founded by an MSc Chemical Engineering graduate with deep hands-on experience in industrial processes and machine learning. We sit at the intersection of engineering domain knowledge and modern artificial intelligence, a combination that is uncommon and genuinely valuable when deploying models in real operational settings.

Our work spans predictive analytics, process optimisation, deep learning forecasting and explainable AI. We do not just build models. We build systems that operators can trust, understand and act on. Every prediction we deliver comes with a clear explanation of the reasoning behind it.

We take a rigorous, evidence-based approach to every engagement. Our solutions are tested, documented and built to perform under real-world conditions.

XGBoost & Gradient Boosting
LSTM & Deep Learning
SARIMA & Time Series
SHAP Explainability
AWS EC2 Deployment
Flask & Python
Power BI Dashboards
SCADA Integration
Chemical Engineering
Random Forest
Linear & Logistic Regression
Support Vector Machines
Get In Touch

Let Us Work Together

If you have an industrial machine learning challenge, we would like to hear about it. Whether you need predictive maintenance, yield forecasting or a full ML pipeline, send us a message and we will respond promptly.