Price Prediction With Regression Analysis in R

Predictive data analytics uses recorded data to predict future events through various statistical methods. Since R language centers heavily on statistics, it provides us with the right tools to create a predictive model, and offers extensive support for data visualization. It offers libraries that help apply statistical techniques like regression, time series analysis, etc., on any dataset.

The dataset that we’ll use in this project is about the specifications and prices of vehicles and is freely available on Kaggle. The vehicle’s specifications are related to its price in one way or another. The nature of the relationship can be identified and modeled via the regression model. A regression model describes and estimates the relationships among the variables and is used in the predictive analysis for a complex dataset like ours. Lasso regression is a type of regression model that makes feature selection on its own by shrinking the coefficients of insignificant variables that do not contribute to the model’s performance to zero, which makes the models easy to train.

In this project, we’ll visualize and explore the dataset and then apply regression models to the data. In the end, we’ll compare the performance of different regression models. We’ll also learn how to train, test, and evaluate models in R.