I am working on data and trying to make a prediction on it using supervised learning.
Problem Statement: Let's consider we have data of routers as given below with probable values.
- Name (Unique)
- Brand : Cisco, Huawei, Netgear
- Location: Bedroom(B), Kitchen(K), Hall(H)
- IP address : Unique
- MacAddress : Unique
- Serial Number : Unique
- Firmware version: Varying like 1.0.0, 2.0.1, 3.1.0 etc
- state: running, discovering, rebooting
Strength: Strong/Weak: 1/0
Rules governing 'Strength' output is given below.
- Brand == Cisco ==> Strength == Strong in all locations. B + H + K
- Brand == Huawei ==> Strength == Strong in Hall and kitchen only. H + K
- Brand == Netgear ==> Strength == Strong in kitchen only. K
We consider Brand and Location only for predicting Signal Strength.
Sample Train Data
|Cisco |Bedroom | Strong |
|Huawei |Bedroom | Weak |
|Netgear|Hall | Weak |
|Cisco |Kitchen | Strong |
|Huawei |Hall | Strong |
|Netgear|Kitchen | Strong |
Sample Test Data
|Cisco |Hall | Strong |
|Huawei |Kitchen | Strong |
|Netgear|Bedroom | Weak |
1- Can this problem statement solved using machine learning or machine learning is an overkill?
2- What algorithm/architecture to be used to solve such a problem? Is normal neural network enough or CNN is more appropriate for this problem consider scaling in future?
3- Can we include any other feature for better accuracy?
4. How much data is enough to start with?
Kindly share your suggestions.
Thanks in advance!!!
What I have tried:
Question is about problem analysis and its validation.
Tried with SGD and it seems to work.