Duration Estimation for Future Canadian Armed Forces Operations
Collated and examined historical mission data using Python, R and derived insights — patterns and correlations — that helped improve Canadian Armed Forces mission planning.
The Problem
The Canadian Armed Forces required a data-driven methodology to better predict the duration of future operations based on historical mission data. Manual analysis of heterogeneous mission datasets was insufficient for systematic forecasting.
The Solution
A research report was generated for the Department of National Defence to help improve future mission planning based on data-driven insights. The project was conducted in partnership with Defence Research and Development Canada from January to April 2020.
Why It's Hard
Historical mission data was preprocessed by collating, imputing, transforming, and eliminating data from the CAF datastore. Feature selection was performed through variable importance random forest technique. A random forest classifier was developed to predict duration classes for a future mission, optimizing resources based on historical trends for similar missions grouped by region and context.
The Process
Discovery
Data Acquisition & Cleaning
Collated, imputed, transformed, and cleaned heterogeneous historical mission data from the CAF datastore for analysis.
Data inventoryCleaning pipelineArchitecture
Feature Selection & Model Development
Applied variable importance random forest technique for feature selection, then developed a random forest classifier to predict mission duration classes.
Feature importance reportModel specShip
Research Report Delivery
Generated a research report for the Department of National Defence to improve future mission planning using data-driven insights.
Research reportDND briefing
Architecture
CAF Datastore
Historical Mission Data
Data Pipeline
SQL / R / MATLAB
Random Forest Classifier
Weka / R
Tableau Dashboard
Visualization & Reporting
CAF Datastore
Historical Mission Data
Data Pipeline
SQL / R / MATLAB
Random Forest Classifier
Weka / R
Tableau Dashboard
Visualization & Reporting
How It Works
Data Preparation
Historical CAF mission records are collected, cleaned, and transformed into a structured dataset suitable for ML analysis.
Classification
A random forest classifier groups missions by region and context, then predicts duration classes based on learned patterns.
Planning Insights
Results are visualized and delivered as a report, helping planners optimize resource allocation for future operations.
Techniques
- ✓Random Forest
- ✓Machine Learning
- ✓Data Cleaning
Technologies
- ✓SQL
- ✓R
- ✓MATLAB
- ✓Tableau
- ✓Weka