The document discusses a drone purchasing assistant created by Oveis Hassanijalilian that analyzes weather station and farm data to predict how many acres farmers will be unable to plant each year due to weather. By using the assistant's predictions to order drones 3 weeks in advance, a company can earn $70,000 per drone deployed. The assistant uses an AdaBoost classifier with decision trees trained on 10-fold cross validated weather and planting data from 2009-2018 to predict prevented planting acres with 84% average F1 score.

1. Drone Purchasing Assistant
Oveis Hassanijalilian
Insight Data Science Fellow

2. Company’s mission

3. Planting can be done:
Mid May - mid June
Weather is the major
factor that hinders
farmers from planting

4. Some portions of the fields can NOT be planted
- $$$ Prevented Acres
+ $$$
+ $$$

5. The company measures the prevented acres

6. BUT the company does not know how many acres
they will fly in advance
3 weeks delivery delay

7. Source of data
- North Dakota Agricultural
Weather Station (NDAWN)
Scraped and cleaned data for
2009 - 2018
- Farm Service Agency (FSA)
- Planted acres
- Prevented acres

8. Model Features
- Frequency of number of
consecutive days with no rain
- Total rain
- Latitude / Longitude

9. Model Features
Average and standard deviation of:

10. Target Variable
Prevented acres
Planted acres
x 100
BUT the dataset was imbalanced

11. Classification and SMOTE
0% - 5% 5% - 10% 10% - 20% 20% - 30% 30% - 70%
Class 1
387
Class 2
73
Class 3
44
Class 4
15
Class 5
16

12. Model and validation
AdaBoost classifier with decision tree as base
estimator
20% test set
10-fold cross validation

13. Achieved 84% average f1-score

14. $ 70,000 gain per drone
Using my app and ordering
drones 3 weeks in advance
this company can earn $70K
per drone

15. Oveis Hassanijalilian