# Basic Probability Concepts for Data Science

## Motivation

Probability is one of the most common terminologies, not only in mathematics but also in the real world. We use the word probability frequently. About seven years ago, I was in my secondary level of education and got introduced to the term probability as a topic of mathematics. At that time, I had solved so many mathematical problems regarding probability. Unfortunately, it did not seem interesting to me. At last, when I was exploring spam-filtering techniques, I was amazed to know that the most popular Machine Learning algorithm for detecting spam filtering is the Naive Bayes classifier. Interestingly, the main idea of the classifier comes from probability. Therefore, I started to learn the concept of probability deeply and found so many indispensable practical uses in data science. It motivated me to learn more practically. Now, it’s time to share the knowledge.

## ✪ Probability

Probability is a numerical concept used to measure the chance of any specific event or outcome occurring. The value of the probability ranges from 0 to 1. If the value is closer to 1. Then we can assume it has a high probability to occur. In contrast, if the value lies closer to 0. Then it can be said it has a high probability to disappear. Probability is an effective technique that is used for non-trivial applications. Such as

• Spam Filter for inboxes in Gmail.
• Image Recognition System For Medical Image Analysis.

Two different processes are used to find probabilities.

i). Experimental or Empirical Probabilities.

ii). Theoretical Probabilities.

➤ Experimental or Empirical Probabilities

Empirical Probabilities is the process of estimating the probability by experimenting. We consider a coin toss event; we get only two outcomes head or tail if we throw a coin. Suppose, we want to find the probabilities of getting head. We can find it by following the below steps.

i). At first, we can flip the coin several times. We assume flip the coin 200 times.

ii).We can count the number of times the coins landed on the head. We assume the coin landed on the head 79 times.

iii). Now, we can divide the number heads by the numbers of the total of times we flip the coins.

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