Weather Forecasting System Using Supervised Machine learning

Predict next-day rain in Australia

Problem Statement

Predict next-day rain by training classification models on the target variable RainTomorrow.

Content

The Rain in Australia dataset contains about 10 years of daily weather observations from numerous Australian weather stations. Here's a small sample from the dataset:

RainTomorrow is the target variable to predict. It means -- did it rain the next day, Yes or No?

Source & Acknowledgements

Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data.

An example of the latest weather observations in Canberra: http://www.bom.gov.au/climate/dwo/IDCJDW2801.latest.shtml

Data source:

http://www.bom.gov.au/climate/dwo/ and http://www.bom.gov.au/climate/data

To extract as much accuracy as possible, we have used several supervised machine learning models shown below.

1. Logistic Regression
2. Decision Tree
3. Random Forest Model

The following topics are used in this project:

• Exploratory data analysis and visualization
• Splitting a dataset into training, validation & test sets
• Filling/imputing missing values in numeric columns
• Scaling numeric features to a (0,1) range
• Encoding categorical columns as one-hot vectors
• Training a logistic regression model using Scikit-learn
• Evaluating a model using a validation set and test set
• Training and interpreting decision trees
• Training and interpreting random forests
• Overfitting & hyperparameter tuning