Implementing a neural network allows customizability for the number of hidden layers and neurons in each layer. Implemented Adam optimizer using both momentum and velocities for weights and biases.
Used OpenWeather Air-Pollution API to get data from Jan 1st, 2015 to Jan 1st, 2025.
Trained the model over the data.
- From OpenWeather API:
- Air quality index ranges from 1-5
- This project follows this encoding:
- 0 (good): 1-2
- 1 (moderate): 3-4
- 2 (bad): 5+
- This is further encoded using one hot encoding
- [1, 0, 0] = 0
- [0, 1, 0] = 1
- [0, 0, 1] = 2
- Model accuracy post-training: 92.03%
- Layout used: Input, HL1, HL2, HL3, Output
- Neuron count: 8, 20, 15, 10, 3
- Loss over time
