Model building is only a part of your data science project. The trained machine learning model should be deployed as a service on the web to the general public or users. In this article we will learn how to deploy a machine learning model using Flask and Heroku.

Flask is a python web framework. Flask lets you develop web applications very easily. The “Hello world” app in flask is just seven lines of code but learning how to build a full-fledge web applications with any framework takes a lot of work. Flask is always a good choice for beginners because…

Naïve Bayes is an algorithm primarily used for classification. The major use cases are spam classification, medical diagnosis, recommendation engines, text classification etc. Naïve Bayes classifier is a probabilistic classifier which means that given a input, the classifier predicts the probability of the input being classified for all the classes. it is also called conditional probability.

The foundation of this algorithm is the classical Bayes theorem. It describes the probability of an event based on the prior knowledge of conditions that might be related to the event. Theorem can be formulated as follows:

**Introduction**

Logistic regression is primarily used for binary classification. Some of the Examples are — predicting whether the patient has heart disease or not, predicting whether the transaction is fraudulent or not, Email span detection. In linear regression we model the data as follows:

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