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Linear Regression in Machine Learning and Data Science

Predicting useful insights from the data set is one of the primary purposes of using machine learning in data science. While talking about supervised machine learning, we come across fundamental algorithms to predict the numerical values from the data set, which is regression. The machine learning and data science problems in which data values are numerical and the data scientists have to predict numerical value; regression is a handy algorithm in these cases. Let us consider an example for explaining the linear regression. Suppose we have a data set in which we have to predict the house price from the house data set’s given attributes. The given attributes can be the number of rooms, location, number of stories in the house, and house price, the dependent variable. After training the data set using the regression model, the new data’s house price can be easily determined. But regression can not handle the categorical data and cannot solve the classification problems. But there is a type of linear regression called logistic regression, which is used for classification problems. Looking forward to becoming a Data Scientist? Check out the Data Science Course Fees In Pune

 What is Linear Regression:     

First of all, we need to know what linear regression is. Linear regression is a predictive technique used to explore the relationship between the dependent and independent variables. Become a Data Scientist with 360DigiTMG Data Science Online Courses In Bangalore

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Linear Regression

The Generic Formula of Linear Regression:

We can express the relationship between the dependent and the independent variable, i.e., ‘x’ and ‘y’ variable in the form of an equation as follows:

y = mx + b

where ‘b’ represents the intercept, and ‘m’ represents the slope of the linear line. Using the dataset containing the x and y variable, we can get the best intercept and slope values to find out an authentic linear line with less error. Also, check this Data Science Certification to start a career in Data Science.

Types of Linear Regression:

We can categorize Linear Regression into two types as follows:

  1. Simple Linear Regression
  2. Multiple Linear Regression

Simple Linear Regression:  

In this type, we express the relationship between single independent and dependent variables in the form of a resulting straight line. It can be expressed in the form of a simple line equation as follows

Y=β0 + β1X +

Where Y is the dependent variable, X is the Independent variable, β0, β1 are the coefficients, and ∈ is the noise or error in the dataset. Looking forward to becoming a Data Scientist? Check out the Data Science Certification Course In Chennai and get certified today.

Multiple Linear Regression:

In this type of regression, we can determine the relationship between two or more independent and dependent variables. As a result, we find out two or more linear lines. Sometimes, the dependent variable can be continuous, and in some cases, it can be categorical. 

The equation of multiple regression is as follows:

Y=β0 + β1X1 2X2+……. pXp +

Where Y is the dependent variable, X1, X2,…Xp are independent variables or predictors, β0, β1, β2….. βp are the coefficients and ∈ is the error or noise in the dataset.

We have discussed some basic formulas and working of regression in data sciences and machine learning. For more articles related to data science, please keep visiting our blog.

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