Learn Data Science using Python

Another post starts with you beautiful people! Continuing our Machine Learning track today we will apply the Naive Bayes Classifier but before that we need to understand the Bayes Theorem . So let’s first understand the Bayes Theorem. Bayes Theorem works on conditional probability. Conditional probability is the probability that something will happen, given that something else has already occurred. Using the conditional probability, we can calculate the probability of an event using its prior knowledge. Below is the formula for calculating the conditional probability. where P(H) is the probability of hypothesis H being true. This is known as the prior probability. P(E) is the probability of the evidence(regardless of the hypothesis). P(E|H) is the probability of the evidence given that hypothesis is true. P(H|E) is the probability of the hypothesis given that the evidence is there. We can understand the above concept with a classic example of coin that I summar

Another post starts with you beautiful people! I hope you have enjoyed and must learn something from my previous post about  Cross Validation & ROC . In this post we are going to learn  Principal Component Analysis  or POC. Principal Component Analysis (PCA) is a simple yet popular and useful linear transformation technique that is used in numerous applications, such as stock market predictions, the analysis of gene expression data, and many more. The main idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. Importantly, the dataset on which PCA technique is to be used must be scaled . The results are also sensitive to the relative scaling. As a layman, it is a method of summarizing data. Imagine some wine bottles on a dining table. Each wine is described by its attribut