Lensiq predicted
99 last-mile courier deliveries in Lebanon
with 92.93% accuracy
Trained on 1,000 real delivery records. Tested on 99 it had never seen. The model got 92 right, and flagged its own uncertainty on almost every mistake.
Model insights
What drives delivery success
The model learned these five factors matter most, ranked by their influence on the outcome.
First attempts succeed far more than retries. Third attempts fail at high rates.
Confirmed deliveries arrive at significantly higher rates than unconfirmed ones.
More experienced drivers navigate challenges, traffic, and edge cases better.
Longer routes increase exposure to delays, traffic, and missed windows.
Storms and heavy rain meaningfully reduce delivery success rates.
Confidence
The model knows when it's sure
Confidence distribution: 99 predictions
When the model was wrong, its average confidence was 68.7% vs 96.1% on correct predictions. It knew when it was uncertain.
Set a confidence threshold of 85% and you would catch almost every mistake before dispatch.
Where it went wrong
All 7 mistakes and what they reveal
5 of 7 were Low or Medium confidence. The model was uncertain on nearly every mistake. That's the signal you act on.
| # | Area | Package | Weather | Attempt | Predicted | Actual | Confidence | Level |
|---|---|---|---|---|---|---|---|---|
| 14 | Beirut | Medium Parcel | Clear | 1 | Not Delivered | Delivered | 60.1% | Medium |
| 30 | North Lebanon | Document | Rain | 1 | Not Delivered | Delivered | 90.2% | High |
| 41 | Mount Lebanon | Large Parcel | Clear | 3 | Not Delivered | Delivered | 59.2% | Low |
| 44 | Beirut | Fragile | Rain | 2 | Delivered | Not Delivered | 75.3% | Medium |
| 55 | Zarqa | Small Parcel | Clear | 3 | Not Delivered | Delivered | 54% | Low |
| 69 | Irbid | Small Parcel | Storm | 1 | Not Delivered | Delivered | 53.1% | Low |
| 83 | Beirut | Large Parcel | Clear | 1 | Delivered | Not Delivered | 89% | High |
A simple confidence threshold would have flagged these for manual review before dispatch.
Compared to 96.1% on the 92 correct ones. The gap makes the signal clear.
Sample results
Row-by-row predictions
A sample of 8 rows from the test run, including both correct predictions and the two highest-profile mistakes.
| # | Area | Package | Weather | Attempt | Predicted | Actual | Confidence | Level | Correct |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Mount Lebanon | Small Parcel | Clear | 1 | Delivered | Delivered | 100% | High | |
| 5 | Beirut | Medium Parcel | Storm | 2 | Not Delivered | Not Delivered | 100% | High | |
| 7 | Amman | Large Parcel | Rain | 1 | Delivered | Delivered | 100% | High | |
| 12 | Amman | Fragile | Rain | 1 | Delivered | Delivered | 78.6% | Medium | |
| 14 | Beirut | Medium Parcel | Clear | 1 | Not Delivered | Delivered | 60.1% | Medium | |
| 26 | Beirut | Large Parcel | Clear | 3 | Not Delivered | Not Delivered | 100% | High | |
| 30 | North Lebanon | Document | Rain | 1 | Not Delivered | Delivered | 90.2% | High | |
| 36 | Mount Lebanon | Small Parcel | Clear | 1 | Delivered | Delivered | 99.99% | High |
Bottom line
The model learned real operational logic
Not rules someone wrote. Patterns it discovered from 1,000 real deliveries.
Confirm before you dispatch
Pre-confirmation is the second strongest signal in the data. Unconfirmed deliveries fail at significantly higher rates. A quick confirmation call before dispatch is one of the highest-ROI actions in your operation.
Match driver experience to route difficulty
Sending a new driver on a long route in bad weather is a compounding risk. The model learned that experience, distance, and weather interact. Each one manageable alone, dangerous in combination.
Treat repeat attempts as risk signals
Attempt number is the single strongest predictor in the dataset. A third delivery attempt has very different odds than a first. The model quantifies that risk so you can act before the driver leaves the warehouse.
These are not rules someone programmed. The model found them by studying your operations. Every business has its own version of this logic. Lensiq extracts it from your data and turns it into predictions you can act on in real time.
This is what your operations data can do.
Lensiq turns your historical delivery records into a live prediction engine. No ML team required.
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