TL;DR Built ANN using pytorch to predict if it is going to rain tomorrow or not going to rain tomorrow
Provided in the repo, it is AUS daily wheather data with target label know as RainTomorrow
- The network.gz.pdf is visualization of how the network looks
- Only 2 hidden layers are being utilized
- Output is applied with sigmoid function in order to return 1 or 0
precision recall f1-score support
No rain 0.85 0.96 0.90 19413
Raining 0.74 0.40 0.52 5525
accuracy 0.84 24938
macro avg 0.80 0.68 0.71 24938
weighted avg 0.83 0.84 0.82 24938
epoch 0
Train set - loss: 2.513, accuracy: 0.779
Test set - loss: 2.517, accuracy: 0.778
epoch 100
Train set - loss: 0.457, accuracy: 0.792
Test set - loss: 0.458, accuracy: 0.793
epoch 200
Train set - loss: 0.435, accuracy: 0.801
Test set - loss: 0.436, accuracy: 0.8
epoch 300
Train set - loss: 0.421, accuracy: 0.814
Test set - loss: 0.421, accuracy: 0.815
epoch 400
Train set - loss: 0.412, accuracy: 0.826
Test set - loss: 0.413, accuracy: 0.827
epoch 500
Train set - loss: 0.408, accuracy: 0.831
Test set - loss: 0.408, accuracy: 0.832
epoch 600
Train set - loss: 0.406, accuracy: 0.833
Test set - loss: 0.406, accuracy: 0.835
epoch 700
Train set - loss: 0.405, accuracy: 0.834
Test set - loss: 0.405, accuracy: 0.835
epoch 800
Train set - loss: 0.404, accuracy: 0.834
Test set - loss: 0.404, accuracy: 0.835
epoch 900
Train set - loss: 0.404, accuracy: 0.834
Test set - loss: 0.404, accuracy: 0.836
- Prediction for rain is not good for recall as the number of data sample for rain is only 20% and not rain has 80% (bias factor)
- Current data preprocessing only able to push accuracy up to 84%, even with more epochs
- Clone this repo
- Make sure you have Pytorch installed
- Run
main.py - Optimize and have fun