# Multiple Linear Regression model using Python: Machine Learning Multiple Linear Regression model using Python: Machine Learning. Learning how to build a basic multiple linear regression model in machine learning using Jupyter notebook in python

Linear regression performs a regression task on a target variable based on independent variables in a given data. It is a machine learning algorithm and is often used to find the relationship between the target and independent variables.

TheSimple Linear Regressionmodel is to predict the target variable using one independent variable.

When one variable/column in a dataset is not sufficient to create a good model and make more accurate predictions, we’ll use a multiple linear regression model instead of a simple linear regression model.

The line equation for the multiple linear regression model is:

`y = β0 + β1X1 + β2X2 + β3X3 + .... + βpXp + e`

Before proceeding further on building the model using python, we need to consider some things:

1. Adding more variables isn’t always helpful because the model may ‘over-fit,’ and it’ll be too complicated. The trained model doesn’t generalize with the new data. It only works on the trained data.
2. All the variables/columns in the dataset may not be independent. This condition is called `**multicollinearity**`, where there is an association between predictor variables.
3. We have to select the appropriate variables to build the best model. This process of selecting variables is called `**Feature selection**`.

We’ll discuss points 2 & 3 using python code.

Now, let’s dive into the `Jupyter notebook` and see how we can build the Python model.

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