E-COMMERCE PRICE PREDICTION MODEL - Leonata Pratama, Jofiandy
There are many machine learning models that are used to predict the occurrence of an event. One of the use cases in the machine learning model is predicting price of an item. There are several models implemented to build a machine learning model to predict price both for linear model and non-linear model. To choose which type of model that is going to be used for price prediction will be based on the dataset structure and also the outcome that is expected. For non-linear dataset, it is recommended to use non-linear machine learning model. In this experiment, comparison will be conducted between decision tree regression model and random forest model to predict the price of wearable item in Shopee e-commerce. In this process, not only does the model is being experimented, but also the improvement of each model is also explored. In the end, the performance of the model is being evaluated with the value of ratio between mean absolute error with dataset median and also the value of ration between root mean square error with dataset median. In the implementation process of building the model, several steps are taken which consist of dividing dataset with k-fold cross-validation and also doing oversampling on the dataset. After implementing the model, the evaluation process is conducted. Based on the experiment, it is known that with hyperparameter tuning for random forest, it gives a much better outcome from the value of ratio between mean absolute error with median which is 0.567 and value of ratio between root mean square error with median which is 1.053 for overall dataset. On the other hand, for the decision tree regression that also being hyperparameter tuned, the value of ratio between mean absolute error with median which is 0.901 and value of ratio between root mean square error with median which is 1.426