Algiers Airport Data Mining
Large-scale data analysis extracting passenger patterns and operational bottlenecks from over 50,000 passenger records.
System Architecture & Overview
A comprehensive data analysis project examining Algiers Airport operational efficiency. The study processed over 50,000 passenger records, check-in timelines, and flight statistics to isolate major bottleneck patterns.
Airport logistics suffer from non-linear peak hours. By applying modern clustering and dimensionality reduction, we transformed unstructured logs into actionable facility optimizations.
Key Deliverables & Capabilities
- K-Means Passenger Segmentation: Classifies passengers into operational profiles (business, family, tourist) to optimize queue routing.
- Principal Component Analysis: Reduced 15+ complex service variables into core variance vectors.
- Association Rule Mining: Found critical connections between delay frequencies, baggage handling loads, and gate allocations.
Critical Challenge & Pivot
Raw airport records contained extensive missing timestamps and noisy outliers. We built custom imputers and robust standard scalers to clean the dataset without introducing bias.
System Benchmarks & Outcomes
Identified 3 highly critical bottleneck zones in passenger handling systems, presenting actionable queue management improvements to decrease terminal wait times.
Engineering Stack
Managed heavy data cleaning, merging, and tabular aggregations on the raw dataset.
Supplied the machine learning implementations for K-Means clustering, PCA, and association mining.