Conditional Probability with examples For Data Science

As the name suggests, Conditional Probability is the probability of an event under some given condition. And based on the condition our sample space reduces to the conditional element.

For example, find the probability of a person subscribing for the insurance given that he has taken the house loan. Here sample space is restricted to the persons who have taken house loan.

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Probability Basics for Data Science

Probability in itself is a huge topic to study. Applications of probability are found everywhere whether it is medical science, share market trading, sports, gaming Industry and many more. However in this post my focus is on to explain the topics which are needed to understand data science and machine learning concepts.

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A Complete Guide to K-Nearest Neighbors Algorithm – KNN using Python

k-Nearest Neighbors or kNN algorithm is very easy and powerful Machine Learning algorithm. It can be used for both classification as well as regression that is predicting a continuous value. The very basic idea behind kNN is that it starts with finding out the k-nearest data points known as neighbors of the new data point for which we need to make the prediction. And then if it is regression then take the conditional mean of the neighbors y-value and that is the predicted value for new data point. If it is classification then it takes the mode (majority value) of the neighbors y value and that becomes the predicted class of the new data point.

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A Complete Guide to Principal Component Analysis – PCA in Machine Learning

Principal Component Analysis or PCA is a widely used technique for dimensionality reduction of the large data set. Reducing the number of components or features costs some accuracy and on the other hand, it makes the large data set simpler, easy to explore and visualize. Also, it reduces the computational complexity of the model which makes machine learning algorithms run faster. It is always a question and debatable how much accuracy it is sacrificing to get less complex and reduced dimensions data set. we don’t have a fixed answer for this however we try to keep most of the variance while choosing the final set of components.

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