This is for people who connect dots. With this pandemic situation, a bunch of aspiring Data Science, ML engineers are already into training models predicting infection rates, creating data stories and writing articles on Covid-19. Now models have almost built and stories are written, what’s next? Deploying them in practice right? Have you ever wondered how FDA chooses to approve those vaccines? Have you ever wondered how they choose between a couple of tough competing vaccines? As a Data aspirant, have you ever wondered doing this bothers you? Yes, It does.

Let’s take real world examples, Pfizer and Moderna vaccines…

Not always Machine learning algorithms mean linear algebra and calculus, the best solutions are always simple. Yes, this blog aims at diving deep into one of the most efficient yet a naive Machine learning algorithm that is based on “probabilities”.

Naïve bayes has its roots strongly grounded in probabilities, we know that, probabilities are way of measuring the chance of an event happening. An event is any occurrence that has a probability attached to it.

**Example:** A coin has two sides head and tail. Here, getting head or tail is an event that has a probability of 0.5 …

The substantial part of **EDA **involves in visualizing data to derive business insights. Though numbers can give us the results as the size of the data grows, it becomes hard to interpret the inferences at a sight. Data visualization lends its helping hand to handle the hindrance.** Visualization **is so powerful that when you know what you want, you will get it. But, be sure to visualize what you want to do before you do it. Here, let’s look into how to apply data visualization techniques to get the at most information possible, hidden on the given data.

Data can…

ML Data Associate | Amazon