Datasets:

toolathon123
/

project_20260813_014231_9dbe9212

library_name: sklearn
tags:
  - anomaly-detection
  - isolation-forest
  - logistics
  - quality
  - risk-scoring
pipeline_tag: tabular-classification
license: apache-2.0

Isolation Forest — Daily Transport Quality Risk Model

Model: isolation_forest_quality.joblib Task: unsupervised anomaly detection on daily shipment-quality records Training date: 2026-08-13 Dataset: toolathon123/project_20260813_014231_9dbe9212 (2,000 shipments)

Pure NumPy implementation of an Isolation Forest (Liu, Ting & Zhou, 2008). The algorithm isolates observations by randomly partitioning the feature space with binary trees; points that are easy to isolate (i.e. require few splits) are anomalous. Because scikit-learn is not installed in the pipeline runtime, the model is shipped as a self-contained joblib blob with no external runtime dependency beyond NumPy.

Loading the model (reproducible)

import sys, joblib
from huggingface_hub import hf_hub_download

# 1) put the implementation module on the path
sys.path.insert(0, "model")           # model/isolation_forest.py
# (or: pip-style: download model/isolation_forest.py and import it)

# 2) download and load the serialized model
path = hf_hub_download("toolathon123/project_20260813_014231_9dbe9212",
                       "model/isolation_forest_quality.joblib", repo_type="dataset")
model = joblib.load(path)             # -> isolation_forest.IsolationForest

model/isolation_forest.py is the importable source of the model class — required so the pickle can resolve the IsolationForest class on any machine.

Model inputs (features)

The model consumes the 6 numeric features of each shipment:

#	Feature	Description
1	`delay_minutes`	Arrival delay in minutes
2	`damage_flag`	1 if cargo damaged, else 0
3	`temperature_deviation`	Deviation from target temperature (°C)
4	`humidity_deviation`	Deviation from target relative humidity (%RH)
5	`fuel_consumption`	Total fuel consumed (litres)
6	`quality_score`	Composite quality score 0–100

Categorical fields (route, carrier, origin, destination) are not used for scoring; they are kept in the dataset for drill-down and reporting.

Outputs

anomaly_score ∈ [0.5, 1.0] — Isolation Forest anomaly score. Higher ⇒ more anomalous.
high_risk ∈ {0, 1} — flag = 1 when anomaly_score >= threshold (0.6044), i.e. the shipment is flagged for manual review as a high-quality-risk order.

Daily update strategy

The model is retrained every operational day on that day's fresh train snapshot (data/train-*.parquet).
Hyper-parameters: n_estimators=100, max_samples=256, contamination=0.05 (anomaly threshold = 95th percentile of training anomaly scores).
model_metadata.json stores the trained thresholds, feature list and the day's KPIs for full traceability and comparison across days.

Performance / daily KPIs (2026-08-13)

Metric	Value
High-risk order ratio	5.00% (100 / 2000)
Avg delay (all shipments)	83.2 min
Avg delay (low-risk)	77.4 min
Avg delay (high-risk)	192.7 min
Avg delay improvement rate	6.93%
Damage rate	8.10%
Avg quality score	85.52
Avg temperature deviation	0.63 °C
Avg humidity deviation	4.57 %RH

Delay improvement rate = relative reduction in mean delay if the 100 high-risk shipments were remediated to the low-risk mean delay.

Intended use

Operational daily triage: prioritise manual inspection and re-planning for the top high-risk shipments, targeting the QIP goal of pushing the high-risk ratio below 5%.

Files

isolation_forest_quality.joblib — serialized model (joblib)
isolation_forest.py — importable NumPy IsolationForest implementation
model_metadata.json — training metadata, features, threshold and daily KPIs