![]() ![]() The full data set contains 423,857 rows of data with 25 attributes. We are going to use this data for our project. Kaggle has provided a data set for second hand vehicles’ prices. Model Comparison and Selection (Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, Extreme Gradient Boosting).Data Preparation (Missing value and category data). ![]() Below is the list of what we will be covering. The aim of this article is to go through steps for doing regression analysis where we will compare several models and run them with cross-validation. It contains several features where we need to prepare and we are also facing none normal distribution. In this vehicle price prediction, it is a good practice for the regression model. However, in regression, the output that we want is the value, like what will be the price of the house. For example, in sentiment analysis, we want to know whether a review belongs to a good or a bad sentiment. The different of the two is that classification predict the output (or y) as either yes or no, up or down, or some other categories. In machine learning, there are classification and regression model. ![]()
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