This example demonstrates the prediction of the detail views of cars on a website from the information contained in the other columns of the data 'Items_Cars_Data.csv'. The file 'Data_description.csv' describes the columns. The entire chain of model development was covered: data loading, derivation of new features, exploratory data analysis, preparation of data for training, model building, cross-validation, tuning of hyperparameters, analysis of learning curves, evaluation on the hold-out set (test data), and feature importance analysis. The algorithms include Linear Regression, Support Vector Regression (SVR), Artificial Neural Network of Multi-layer Perceptron (MLP) and eXtreme Gradient Boosting (XGBoost). Transforming skewed data with log transformation efficiently improved the accuracy of the model predictions.
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This example demonstrates the prediction of the detail views of cars on a website from the information contained in the other columns of the data 'Items_Cars_Data.csv'. The file 'Data_description.csv' describes the columns.
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Randa-lab/Predicting-the-online-views-of-products-ML-Regression-
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This example demonstrates the prediction of the detail views of cars on a website from the information contained in the other columns of the data 'Items_Cars_Data.csv'. The file 'Data_description.csv' describes the columns.
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