![]() ![]() Most of the time, we use multiple linear regression instead of a simple linear regression model because the target variable is always dependent on more than one variable. We built a basic multiple linear regression model in machine learning manually and using an automatic RFE approach. Since the R² values for both the train and test data are almost equal, the model we built is the best-fitted model. The R² value for the test data = 0.6481740917926483, which is pretty similar to the train data. We’ll import the necessary libraries to read the data and convert it into a pandas dataframe. The target variable/column in the dataset is Price. We read the data into our system and understand if the data has any anomalies.įor the remainder of the article, we are using the dataset, which can be downloaded from here. Now, let’s dive into the Jupyter notebook and see how we can build the Python model. We’ll discuss points 2 & 3 using python code. ![]() This process of selecting variables is called Feature selection. We have to select the appropriate variables to build the best model.This condition is called multicollinearity, where there is an association between predictor variables. All the variables/columns in the dataset may not be independent.The trained model doesn’t generalize with the new data. Adding more variables isn’t always helpful because the model may ‘over-fit,’ and it’ll be too complicated.+ βpXp + eīefore proceeding further on building the model using python, we need to consider some things: The line equation for the multiple linear regression model is: 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 Simple Linear Regression model is to predict the target variable using one independent variable. It is a machine learning algorithm and is often used to find the relationship between the target and independent variables. Linear regression performs a regression task on a target variable based on independent variables in a given data. ![]()
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