Python facilitates the development of fundamentals of machine learning like linear regression and logistic regression.

Understanding Linear Regression

In the process of linear regression, the input data is studied and is used as training data to train the algorithm. This algorithm will actually predict the value of new data as per its training model. Pythons Visualization, Pandas library and model evaluation is used to create a machine learning model based on linear regression.

Understanding Logistic Regression

As a popular methodology to develop machine learning algorithms for deep learning and neural networks, logistic regression is used for more practical cases of machine learning.

It is more of a predictive algorithm. To begin with, developing logistic regression in Python one needs to make some assumptions like the dependent and independent variables. Remember you would need a lot of training data for Logistic Regression. The steps to be followed are

To build the logistic regression model in python we would prefer SciKit library and start with

Loading the data set, understanding the available data, splitting the data into training data and test data. The training data set must be used to train the logistic regression model to predict the desired results while test data sets are used to determine the accuracy of the logistic model developed.

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