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How to be a HERO in Machine Learning/Data Science Competitions

At present to master machine learning models one has to participate in the competition which is appearing in various platforms. So how somebody who is new to ml can become a  hero  from  zero . The guideline is in this article. The idea for this is not too hard. Just patience and some hard work are required. I will take an example of a Competition that is just finished within top 10. So the competition generally gives you the problem in which some of the features are hidden because they want you to  explore the data  and come up with the feature that explains the target value. By exploring I mean to say the few things: Look at the data. Get the sense of the data. Find the correlation of all features with a target value. Try new features made up of existing features. Exploration needs some  cleaning of the data  also. Because in general, the host will add the noise into the data so that it becomes a trouble for us to achieve good accuracy. By cleaning I...
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DBSCAN Clustering Algorithm-with maths

  DBSCAN is a short-form of   D ensity- B ased   S patial   C lustering of   A pplications with   N oise. It is an unsupervised algorithm that will take the set of points and make them into some sets which have the same properties. It is based on the density-based clustering and it will mark the outliers also which do not lie in any of the cluster or set. There are some terms that we need to know before we proceed further for algorithm: Density Reachability A point “p” is said to be   density reachable from a point “q” if point “p” is within ε distance from point “q” and “q” has a sufficient number of points in its neighbors which are within distance ε. Density Connectivity A point “p” and “q” are said to be density connected if there exists a point “r” which has a sufficient number of points in its neighbors and both the points “p” and “q” is within the ε distance. This is a chaining process. So, if “q” is neighbor of “r”, “r” is neighbor of “s”, “s” ...
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Random Forest and how it works

  Random Forest Random Forest is a Machine Learning Algorithm based on Decision Trees. Random forest works on the ensemble method which is very common these days. The ensemble method means that to make a decision collectively based on the decision trees. Actually, we make a prediction, not simply based on One Decision Tree, but by an unanimous Prediction, made by ‘ K’  Decision Trees. Why should we use There are four reasons why should we us e  the random forest algorithm. The one is that it can be used for both  classification and regression  businesses. Overfitting is one critical problem that may make the results worse, but for the Random Forest algorithm, if there are enough trees in the forest, the classifier  won’t overfit  the model. The third reason is the classifier of Random Forest can handle  missing values , and the last advantage is that the Random Forest classifier can be modeled for  categorical values. How does the Random...
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