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Regression techniques in Machine Learning

Machine learning has become the sexiest and very trendy technology in this world of technologies, Machine learning is used every day in our life such as Virtual assistance, for making future predictions, Videos surveillance, Social media services, spam mail detection, online customer support, search engine resulting prediction, fraud detection, recommendation systems, etc. In machine learning, Regression is the most important topic that needed to be learned. There are different types of Regression techniques which we will know in this article.

Introduction:

Regression algorithms such as Linear regression and Logistic regression are the most important algorithms that people learn while they study about Machine learning algorithms. There are numerous forms of regression that are used to perform regression and each has its own specific features, that are applied accordingly. The regression techniques are used to find out the relationship between the dependent and independent variables or features. It is a part of data analysis that is used to analyze the infinite variables and the main aim of this is forecasting, time series analysis, modeling.

What is Regression?

Regression is a statistical method that mainly used for finance, investing and sales forecasting, and other business disciplines that make attempts to find out the strength and relationship among the variables.

There are two types of the variable into the dataset for apply regression techniques:

  1. Dependent Variable that is mainly denoted as Y
  2. Independent variable that is denoted as x.

And, There are two types of regression

  1. Simple Regression: Only with a single independent feature /variable
  2. Multiple Regression: With two or more than two independent features/variables.

Indeed, in all regression studies, mainly seven types of regression techniques are used firmly for complex problems.

  • Linear regression
  • Logistics regression
  • Polynomial regression
  • Stepwise Regression
  • Ridge Regression
  • Lasso Regression

Linear regression:

It is basically used for predictive analysis, and this is a supervised machine learning algorithm. Linear regression is linear approach to modeling the relationship between scalar response and the parameters or multiple predictor variables. It focuses on the conditional probability distribution. The formula for linear regression is Y = mX+c.

Where Y is the target variable, m is the slope of the line, X is the independent feature, and c is the intercept.

Simple Linear Regression in Machine learning - Javatpoint

Additional points on Linear regression:

  1. There should be a linear relationship between the variables.
  2. It is very sensitive to Outliers and can give a high variance and bias model.
  3. The problem of occurring multi colinearity with multiple independent features

Logistic regression:

It is used for classification problems with a linear dataset. In layman’s term, if the depending or target variable is in the binary form (1 0r 0), true or false, yes or no. It is better to decide whether an occurrence is possibly either success or failure.

 

Logistic Regression

Additional point:

  1. It is used for classification problems.
  2. It does not require any relation between the dependent and independent features.
  3. It can after by the outliers and can occur underfitting and overfishing.
  4. It needs a large sample size to make the estimation more accurate.
  5. It needs to avoid collinearity and multicollinearity.

Polynomial regression:

The polynomial regression technique is used to execute a model that is suitable for handling non-linear separated data. It gives a curve that is best suited to data points, rather than a straight line.
The polynomial regression suits the least-squares form. The purpose of an analysis of regression to model the expected y value for the independent x of the dependent variable. 
The formula for this Y=  β0+ β0x1+e
Polynomial Regression - Towards Data Science
Additional  features: 
Look particularly for curve towards the ends to see if those shapes to patterns make logical sense. More polynomials can lead to weird extrapolation results. 

Step-wise Regression:

It is used for statistical model fitting regression with predictive models. It is done automatically. 
The variable is supplemented or removed from the explanatory variable set at every step. The main approaches for the regression are reverse elimination and bidirectional elimination and step by step approaches. 
The formula for this: b = b(sxi/sy)
Additional points: 
  1. This regression provides two things, the very first one is to add prediction for each steep and remove predictors fro each step.
  2. It starts with the most significant predictor into the ML model and then adds features for each step.
  3. The backward elimination starts with all the predictors into the model and then removes the least significant variable.

Ridge Regression: 

It is a method that used when the dataset having multicollinearity which means, the independent variables are strongly related to each other. Although the least-squares estimates are unbiased in multicollinearity, So after adding the degree of bias to the regression, ridge regression can reduce the standard errors.
Ridge Regression for Better Usage - Towards Data Science

Additional points:

  1. In this regression, normality is not to be estimated the same as Least squares regression.
  2. In this regression, the value could be varied but doesn’t come to zero.
  3. This uses the l2 regularization method as it is also a regularization method.

Lasso Regression:

Lasso is an abbreviation of the Least Absolute shrinkage and selection operator. This is similar to the ridge regression as it also analyzes the absolute size of the regression coefficients. And the additional features of that are it is capable of reducing the accuracy and variability of the coefficients of the Linear regression models.

Lasso regression in matlab - Stack Overflow

 

Additional points: 
  1. Lasso regression shrinks the coefficients aero, which will help in feature selection for building a proper ML model.
  2. It is also a regularization method that uses l1 regularization.
  3. If there are many correlated features, it picks only one of them and shrinks it to the zero.

 

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Artificial Intelligence in E-commerce

The internet has opened the door for revolutionizing various sectors. E-commerce sector is one of them. E-commerce sectors have unlocked new opportunities and scope for retailers. Retailers also have never seen such a growth in their sales. Artificial intelligence is taking E-commerce to the next level. In this article, we are going to discuss 10 applications of artificial intelligence in E-commerce.

  1. Chatbots
    E-commerce websites are using chatbots to improve the customer support service. Chatbots are providing 24/7 customer support to buyers. Visit any recognized E-commerce website. You will be prompted with a chat box asking what do you want or how can I help. You can tell your requirements in the chatbox and you will be served with highly filtered results. Chatbots are built using artificial intelligence and are able to communicate with humans. They can also collect your past data and provide you with a very personalized user experience.
  2. Image search
    Ever come across a situation where you liked any product or item but don’t what it is called or what it is? Artificial intelligence service eases this task for you. The concept of image search is implemented in E-commerce websites with the application of artificial intelligence. Artificial intelligence has made it possible to understand images. Buyers can make a search on the basis of images. Mobile apps of E-commerce websites can find the product by just pointing the camera towards the product. This eliminates the need for keyword searches.
  3. Handling Customer Data
    E-commerce platforms have two things in abundance. On is an endless list of products and other is data. E-commerce has to deal with a lot of data every day. This data can be anything like daily sales, the total number of items sold, the number of orders received in an area or as a whole and what not. It has to take care of customer data also. Handling that amount of data is not possible for a human. Artificial intelligence can not only collect this data in a more structured form but also generate proper insights out of this data.This helps in understanding the customer behaviour of the whole populations as well as of the individual buyer. Understanding the customer buying pattern can make E-commerce to make changes wherever needed and predicting the next buy of the user also.
  4. Recommendation SystemsHave you ever experienced how E-commerce websites like Amazon are constantly showing the products similar you just checked? Well, this is the application of artificial intelligence in E-commerce. AI and machine learning algorithms can predict the behaviour of the buyer from its past searches, likings, frequently bought products. By predicting the behaviour of the user, E-commerce websites are able to recommend the products that user is highly interested in. This improves the user experience as the user no longer have to spend hours searching the product. It also helps the E-commerce websites to improve their sales.
  5. Inventory management
    The inventory management is one of the most important areas in any business. You have to keep yourself updated on how much inventory you are holding and how much more is needed. There are thousands of product categories over the E-commerce websites. Keeping an eye on the inventory of all the products daily is not possible for a human. This is where artificial intelligence comes into the picture. Artificial intelligence applications have helped E-commerce in managing the inventory. Moreover, the inventory management system will get better over the time. AI systems build a correlation between the current demand and the future demand.
  6. Cybersecurity
    Artificial intelligence has also improved the cybersecurity of the E-commerce websites. It can prevent or detect any fraudulent activities. E-Commerce has to deal with a lot of transactions on daily basis. Cybercriminals and hackers can hack the user account to gain unauthenticated access. This can lead to the exposure of private data and online fraud. The reputation of the business also gets a big blow. To prevent this, Artificial intelligence and machine learning algorithms are developed that can mitigate the chances of fraud activities over the website
  7. Better Decision Making
    E-commerce can make better decisions with the application of Artificial intelligence. Data analysts have to handle a lot of data every day. This data is too huge for them to handle. Moreover, analyzing the data also becomes a difficult task. Artificial intelligence has fastened the decision-making process of E-Commerce. AI algorithms can easily identify the complex patterns in the data by predicting user behaviour and their purchasing pattern.
  8. After Sales Service
    Selling the product is not enough. Businesses have to aid the customer in the complete buying cycle. After sales service is an integral part of after-sales service. Artificial intelligence applications can automate the feedback form, replacements and handling any other ambiguity in the product. By solving the buyer’s issues, the brand value of the website gets improved.
  9. CRM
    In the past, Customer Relationship Management (CRM) relied on the people to collect a huge amount of data in order to collect the data and serve the clients. But today, artificial intelligence can predict which customers are most likely to make a purchase and how can we better engage with them. Artificial intelligence applications can help in identifying the trends and plan the actions according to the latest trends. With the help of machine learning algorithms, advanced CRM can learn and improve over time.
  10. Sales Improvement
    Artificial intelligence applications can generate and predict the accurate forecast of the E-commerce business. The study of historical data, data analytics, and latest trends can help in optimizing the resource allocation, build a healthy pipeline and analyze the team performance. The managers can get a better insight into the latest trends in sales. They can analyze the trends and can improve the sales by making strategies well before time.

If you want to learn Artificial Intelligence, know the Artificial Intelligence course of Learnbay in here.

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