Batch Release Risk Predictor — Zenaviq Learning

What This Tool Does

This tool uses a trained Machine Learning model (Random Forest Classifier) to predict whether a pharmaceutical batch is likely to Pass, be placed on Hold, or Fail release testing — based on process parameters recorded during manufacturing.

This is a demonstration model trained on synthetic data for educational purposes. It is not intended for use in actual batch release decisions.

Batch Process Parameters

Temperature Variance (0.0 – 4.0°C)

Deviation from setpoint in °C

Humidity Deviation (0.0 – 5.0%)

% RH deviation from target

Line Speed Variation (0.0 – 6.0%)

% variation from target speed

Equipment Age (1 – 8 years)

Years since last full qualification

Operator Experience (1 – 5 years)

Years of experience on this line

Batch Size (50,000 – 200,000)

Total units in this batch

Try These Example Batches

Enter these values to see how the model responds to different risk profiles:

✅ Low Risk Batch

Temp: 0.4 · Humidity: 0.6 · Speed: 0.8
Equipment: 2 yrs · Operator: 4 yrs · Size: 100,000

⚠ Borderline — Hold

Temp: 0.9 · Humidity: 1.5 · Speed: 2.0
Equipment: 4 yrs · Operator: 2 yrs · Size: 100,000

❌ High Risk — Fail

Temp: 2.6 · Humidity: 4.1 · Speed: 4.8
Equipment: 7 yrs · Operator: 1 yr · Size: 200,000

How This Model Works

This model was trained on 640 historical batch records using a Random Forest classifier — an ensemble of decision trees that vote on the outcome.

The model learned which combinations of parameters correlate with batch failures — without being told any rules explicitly.

Most influential features:
1. Line speed variation (27%)
2. Humidity deviation (25%)
3. Temperature variance (21%)
4. Equipment age (20%)
5. Operator experience (6%)

Training vs Production — What You're Seeing

This tool shows you the production side of an ML model. The training happened separately — a data scientist ran Python code on historical batch data, built and validated the model, then deployed the decision rules here. Your role as a pharma professional is to interpret and act on the prediction — not to build the model.

Training (once)

Historical data → Python → Model built

→

Validation (once)

Model tested for accuracy

→

You are here
New batch → Prediction → Your decision

Educational Tool Notice: This predictor was built using synthetically generated data for the Zenaviq Learning course AI in Pharma: Practical Foundation for Beginners. It is not trained on real batch data and must not be used for actual batch release decisions. All release decisions in a regulated environment require qualified person sign-off and compliance with applicable GMP requirements.