Logistic Regression

Logistic regression is a classification algorithm that assigns observations to discrete classes. Some examples of classification problems are email spam or not spam, online transactions fraud or not fraud, and tumor malignant or benign. Logistic regression utilizes the logistic sigmoid function to convert its output into a probability value.

• It is a predictive analysis algorithm based on the concept of probability.
• The hypothesis of logistic regression tends to limit the cost function between 0 and 1.

The sigmoid function is used to convert map values to probabilities. The function maps any real value into another value between 0 and 1. In machine learning, we use sigmoid to map predictions to probabilities.

The main goal of Gradient descent is to minimize the cost value. i.e. min J(θ)

This project is divided into three parts

1. sentiment analysis model,(build features from our text)
2. building a web application
3. deploying it to Heroku.