Data Analytics Corner: Modelling and Forecasting

Cadence

Weekly

Latest

2026-10-23

By

Dr Nilimesh Halder

Based on

12 recent issues

• Covers: Step-by-step statistical modeling, multimodal logistic regression, and forecasting workflows using Python and R.
• For: Analytics consultants, data scientists, and advanced engineering students.
• Best for: Data professionals needing technical code implementations for complex engineering and infrastructure problems.
• Skip if: Non-technical business managers or general audience readers seeking non-coding business advice.

Start with

• Transformer Fault Diagnosis in High-Voltage Substations: A Multimodal Logistic Regression Framework Integrating Dissolved Gas Analysis, Partial Discharge Signals, and Thermal Imaging Using Python: Diagnosing high-voltage transformer faults using multimodal logistic regression and Python.
• Dam Safety Risk Zonation Using Multimodal Logistic Regression: Combining Seepage Monitoring Records, Piezometric Readings, and Satellite-Based Deformation Signals Using R: Building an R-based framework for dam safety risk zonation from multiple sensor sources.
• Pavement Distress Classification in Urban Road Networks: A Multimodal Logistic Regression Approach Fusing Ground-Penetrating Radar, Surface Deflection Data, and Traffic Loading Histories Using Python: Classifying urban pavement distress risk using Python and multi-source road sensor data.

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