Published Paper Details:

Agriculture is the key point for survival. For agriculture, rainfall is most important. These days rainfall prediction has become a major problem. Prediction of rainfall gives awareness to people and they can get to know in advance about rainfall to take certain precautions to protect their crop from rainfall. Machine Learning algorithms are mostly useful in predicting rainfall. Some of the major Machine Learning algorithms are ARIMA Model(Auto-Regressive Integrated Moving Average), Artificial Neural Network, Logistic Regression, Support Vector Machine and Self Organizing Map. Two commonly used models predict seasonal rainfall such as Linear and Non-Linear models. The first models are the ARIMA Model. While using Artificial Neural Network(ANN) predicting rainfall can be done using Back Propagation NN, Cascade NN or Layer Recurrent Network. Artificial NN is the same as Biological Neural Networks. We will use a kaggle dataset to train and test a Decision Tree Classifier, Logistic Regression & Random Forest Classifier model to predict whether there is going to be rainfall tomorrow or not. We will split the dataset into training and testing data and will be using the training dataset to train the model and testing dataset to observe the accuracy of the model.