Category: Machine Learning

Diabetes Prediction Using Machine Learning

In this article, we will be predicting that whether the patient has diabetes or not on the basis of the features we…

Forest Fire prediction using Machine Learning – Analytics Vidhya

Forest, bush, or vegetarian fire, can be described as any uncontrolled and non-prescribed combustion or burning of plants in a natural setting such as a forest, grassland, etc. In this article we are not determining if a forest fire will take place or not, we are predicting the confidence of the forest fire based on some attributes.
Well, the first question arises as that why we even need Machine learning to predict forest fire in that particular area? So, yes the question is valid that despite having the experienced forest department who have been dealing with these issues for a long time why is there a need for ML, having said that answer is quite simple that the experienced forest department can check on 3-4 parameters from their human mind but ML on other hand can handle the numerous parameters whether it can be latitude, longitude, satellite, version, and whatnot, so dealing with this multi-relationship of a parameter that is responsible for the fire in the forest we do need ML for sure!

Random Forest Algorithm for Absolute Beginners in Data Science

Random forests are a supervised Machine learning algorithm that is widely used in regression and classification problems and produces, even without hyperparameter tuning a great result most of the time. It is perhaps the most used algorithm because of its simplicity. It builds a number of decision trees on different samples and then takes the majority vote if it’s a classification problem.

I am assuming you have already read about Decision Trees, if not then no need to worry we’ll read everything from start. In this article, we’ll figure out how the Random Forest algorithm works, how to use it, and the math intuition behind this simple algorithm.

Before learning this algorithm let’s first see what are Ensemble techniques.