Contents
- Introduction to Regression……………………………………………………………………………
- What is Multinominal Logistic Regression? ………………………………………………………………
- Predicting Mobile Price range using Python………………………………………………………..
- Conclusion…………………………………………………………………………………………………………….
- Introduction to Regression
Regression analysis is a Predictive modelling technique used to analyse the relationship between the one Dependent variable usually denoted as “Y” and multiple independent variablesenoted as (X1, X2, etc.,).
There are multiple Regression Techniques as mentioned below, However Linear and Logistic Regression analysis were widely used by data analysts.
Types of Regression Techniques: –
- Linear Regression
- Logistics Regression
o Binominal Logistics Regression
o Multinominal Logistic Regression
- Ridge Regression
- Lasso Regression
- Polynomial Regression
- Bayesian Linear Regression
2.What is Multinominal Logistic Regression?
Multinomial Logistic Regression is an extension of Logistic Regression. By Default, Logistic is limited to Two-Class classification problem like (True or False, Yes or No, Male or Female) etc.,
If more than two classes need to predict like one-vs-rest (Small, Medium, Big) can allow logistic regression to be used for multi-class classification problems, although
they require that the classification problem first be transformed into multiple binary classification problems.
Instead, Multinominal logistic Regression is an extension to the Logistic regression model that involves changing the loss function to cross-entropy loss and predict multinominal probability distribution to support native multi-class classification problems.
A logistic regression that is adapted to learn and predict multinomial probability distribution is called as Multinominal Logistic Regression (predicts more than two classes for each input).
Similarly, by default or standard logistic regression is referred as Binominal Logistic Regression (predicts two classes for each input).
- Predicting Mobile Price range using Python
Problem Statement: –
Market size of mobile phones if growing every day and so is the competition. To capture the maximum market electronics companies, try to make improvements in their products. However, sales of mobile phones depend on various factors like demand, technology, marketing, brand, availability, user experience, service, price, etc. As we understand that selling price makes a huge difference when it comes to sales and profits. Estimating an optimal price for a new mobile phone can be a tricky task especially when you are new in the business or when you want to launch a new kind of mobile phone in the market.
As part of this exercise, we will try to estimate the price range for a given mobile phone using given feature information. These details are collected from various similar companies.
DATASET Attribute Information: –
With given different Independent variables (Xs) like battery_power, clock_speed, dual_sim etc., we need to predict multi-class price range (Y) low cost – 0, medium cost – 1, high cost – 2 and very high cost – 3.
Download required Python libraries
Perform Basic Data Analysis and Preparation
Perform Statistical analysis using describe ()
Check the Correlation within the variables
Building the Logistic Regression model, getting Dependent variable (Y ‘price_range’) and Independent variables (Xs)
Predict the y_test ( Price range) with given input (X_test) with defined model.
Print the classification report to understand the model accuracy.
Predicting Mobile Price range using Python
- Conclusion
Multinominal Logistic Regression is an extension of logistic regression, it is used to analysis Multi-class classification problems for every single Input.
Author: – G. Boobalan
Linkedin Profile: https://www.linkedin.com/in/gboobalan/
