People are falling sick due to Covid 19 and the government wants to vaccinate the citizens who are at highest risk, on priority. A few features that contribute to risk, has been identified alongwith their extent of contribution. The risk factors are to be calculated and measured against a threshold value. All citizens who score beyond this threshold value are to be categorised as Risk-prone.
The data given is as such:
| Citizen ID | Age | Sugar Level (Blood) | Pollutant Level (Air) |
|---|---|---|---|
| AAA | 20 | 122 | 20 |
| AAB | 35 | 180 | 35 |
| ACA | 50 | 220 | 65 |
| ABB | 70 | 300 | 30 |
Contribution of features to the disease:
| Feature | Weight |
|---|---|
| Age | 0.05 |
| Blood Sugar | 0.002 |
| Pollutant Level | 0.02 |
The high-risk people are those whose "weighted-sum of all features" falls above 4.0
FMML team members are required to develop a prototype (preferably, a python function) that works, as soon as possible, and host it on their personal GitHub account. The python function should accept input data, which would be a set of features for citizens of Madagascar, and inform which of those citizens are prone to risk.