Regression Analysis is a statistical method that helps to understand how the value of the dependent variable is changing corresponding to an independent variable when other independent variables are kept fixed. It predicts continuous or real values such as temperature, age, salary, price, etc.
Examples of regression can be:
- Prediction of rain using temperature and other factors
- Determining Market trends
- Prediction of road accidents due to rash driving
- Predicting the impact of SAT/GRE scores on college admissions
- Prediction of the sales based on input parameters, etc.
Some Terminologies Related to the Regression Analysis:
- Dependent Variable(Target Variable): The main factor which we want to predict or understand is called the dependent variable.
- Independent Variable: The factors which affect the dependent variables or which are used to predict the values of the dependent variables.
- Outliers: Outlier is an observation which contains either very low value or very high value in comparison to other observed values. An outlier may hamper the result, so it should be avoided.
- Multicollinearity: If the independent variables are highly correlated with each other than other variables, then such condition is called Multicollinearity.
- Underfitting and Overfitting: If our algorithm works well with the training dataset but not well with test dataset, then such problem is called Overfitting. And if our algorithm does not perform well even with training dataset, then such problem is called underfitting.
Why do we use Regression Analysis?
Regression analysis is one of the most basic tools in the area of machine learning used for prediction. Using regression you fit a function on the available data and try to predict the outcome for the future or hold-out datapoints.
There are various scenarios in the real world where we need some future predictions such as weather condition, sales prediction, marketing trends, etc., for such case we need some technology which can make predictions more accurately.
So for such case we need Regression analysis which is a statistical method and used in machine learning and data science.
Besides these, some other reasons to use regression analysis are:
- Regression estimates the relationship between the target and the independent variable.
- It is used to find the trends in data.
- It helps to predict real/continuous values.
- By performing the regression, we can confidently determine the most important factor, the least important factor, and how each factor is affecting the other factors.
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