Getting the datasets and research. 

Humidity, wind, temperature and sea levels play a part in the weather.

There are datasets available in data.gov.sg where we will get the past datasets to train the model.

​

I will be using scikit learn to try the different model available.

Pandas to convert the datasets to a data frame.

​

The learning seems to be underfitting because there isn't any sufficient data sets provided to train the model.

But I have learnt a new model Gradient Boost Regression Classifier.

​

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

​

We decided that in the future with the datasets collected by KareRain can be used for ML.

This allows to build up the datasets and can be used to predict weather more accurately.

​

We still decided to host the machine learning model on Microsoft azure instead.

​

This doesn't have any much update as there wasn't any to work on due to insufficient data.

​

​

​

​