# Some Advantages and Applications Of Regression

Applications of Regression is the backbone of supervised learning and is mostly used to predict the business problems in which the data set is of numerical form. The data scientists use regression algorithms for dealing with business-related data. The regression problems can be used to predict the future prices of the items, future marks of the students, and future budget of the company’s project for the next year. The regression algorithms cannot deal with the categorical data. So we cannot use the regression algorithms for classification purposes. But there is one unique type of regression called logistic regression, which is used for classification. Click here for more details Data Science Training in Pune

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## Advantages of Linear Regression:

1. Linear Regression gives the best results when the data set is linearly separable. We can use it to find out the type of the variable as well.

2. Linear Regression can be implemented and trained very easily and efficiently.

3. Linear Regression is prone to overfitting, but this problem can be solved using dimensionality reduction techniques.

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## Disadvantages of Linear Regression:

1. Sometimes, the linear regression model assumes a straight-line relationship between the

dependent and independent variable, but in real-world problems, data is very linearly separable in very rare cases. So, this can cause problems in the future during implementation. It would be best if you were sure about the nature of the dataset. In other words, you should first find out that the dataset is linearly separable or not.

2. In some cases, if the number of observations is less than the number of features, we must not apply linear regression. Otherwise, it can cause noise or overfitting while building the model.

3. Prone to outliers: Linear regression can cause problems if there are outliers in the data set. So, it is necessary to remove them first before applying the model to get the best results.

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### Tools and Techniques used for Linear Regression:

For building linear regression models practically, some statistics tools and built-in libraries of

Python (Jupyter Notebook) are mostly used.

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### Some Real-Life Applications of Linear Regression:

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Nowadays, linear regression is used as a significant tool for predictions in many fields. Some of them are as follows:

In Weather Forecasting, multi-linear models are being used to determine the weather of upcoming days. Linear regression models predict the rainfall probability by using the previous data of weather as an input.

We can predict the productivity of crops by using linear regression models. Linear regression models take the previous records of productivity to estimate the future production of crops.

In Finance and Economics, Linear regression is most excessively used for some essential predictions. For example, for predicting current trends of market or stock exchange, for prediction of investments and required assets, for prediction of exports and imports, and demand and supply estimations, etc.

Linear regression is commonly used for essential predictions in research, astronomy, biology, and medical science for data analysis data science training in hyderabad.

We have discussed some significant regression applications in our daily life. For more articles related to data science, please keep visiting our platform. Click here for more details Data Science Course in Chennai