n this task you have to apply the data classification algorithms on the Vehicle Sales Data set from kaggle

$($the picture provides you with information about the data $)$

$($The dataset contains details such as the year, make, model, trim, body type, transmission type, VIN $($Vehicle Identification Number$),$ state of registration, condition rating, odometer reading, exterior and interior colors, seller information, Manheim Market Report $($MMR$)$ values, selling prices, and sale dates.

Understanding the dataset, extracting patterns and analyzing it can provide valuable insights for car price prediction. The $$Price$$ column is the target column.$)$

In this task you are expected to:

$1-$ Use different regression algorithms $($Linear regression, SVR$,$ Decision Tree Regressor, Random

Forest Regressor, KNN or any other Regressor you want$)$ to solve such problem, evaluate each

using $3-$fold cross validation, and make a comparison between the performance results of each.

$2-$ Read thoroughly about each Regressor$$s parameters and try different values for each parameter.

$3-$ Select the best model that gives the best performance result and apply $($Grid Search or

Randomized search$)$ to fine tune the hyper parameters of this best model.

$4-$ Evaluate and test your best tuned model using different metrics, in addition to draw graphs

related to the regression evaluation results.

$5-$ Report all the code and the results in a jupyter notebook including the results for different

experiments.

$6-$ Add any extra material that you feel will enrich your project. $($any new Ideas or using new

regressors$).$

please provide me with the full code to achive this task $,$ not just the steps to slove it$.$