Logistic Regression & Supervised Machine Learning in Python

Learn with a case study on Predicting Survival of Titanic Passengers using Logistic Regression & Supervised ML in Python

We are starting this course with a case study. So, learn practically. Learn with a case study on Predicting Survival of Titanic Passengers using Logistic Regression & Supervised Machine Learning in Python

Uses of regression analysis

Regression analysis helps to find the significant relationship between dependent variable and independent variable
It helps to know the amount of impact caused by multiple independent variables on a dependent variable
It helps to compare the effects of variables measured using different scales. This comparison will help to bring out the best to be used for predictive modelling.
Regression analysis is used in businesses for a lot of reasons like to find out the factors responsible for business profit, to forecast the future value, to know how the interest rates can affect the stock price and so on.
Regression analysis is used as a quantitative research method which is used when the research involves modelling and analysis of several variables.

This course offers theoretical and practical training for researchers, statisticians and other professionals with particular emphasis on logistic regression model. This is a course where freshers can become an expert at using Python to analyze binary response data using logistic regression. There are many practical examples and case studies given in this course which will help you to learn better. After taking this course you will become a more confident user of python to compute logistic regression. Regression analysis is a form of predictive modelling technique which helps to determine the relationship between dependent and independent variable. It helps to find out the average value of the dependent variable when the independent variables are fixed. This chapter explains in detail about regression analysis and it also includes the areas of application of regression analysis, types of regression, regression models, assumptions of regression analysis and about Interpolation and Extrapolation.

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