<ul><li>The dataset consists of 405,002 rows and 12 columns.</li><li>Data processing techniques were implemented to improve the quality of the dataset.</li><li>A number of features were engineered or simplified to improve the model's interpretability.</li><li>The feature space was reduced using principal component analysis (PCA) and Scikit Learn selection to improve model performance and interpretability.</li><li>Four models were considered: Linear Regression, Random Forest Model, Gradient Boosting Regressor, and Averager/Voting Regressor.</li><li>The performance of each model was compared; the Voting Regressor was found to be the most suitable for this application.</li><li>The SHAP (SHapley Additive exPlanation) algorithm was used to provide global and local explanations of the models.</li><li>The feature importance analysis was executed for each model.</li><li>The model-predicted values were plotted against the actual values for each model.</li><li>The results show that the model is capable of providing accurate car prices within seconds. </li></ul>

Revving Up Insights: Predicting Car Prices with Regression Models and Model Interpretability

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