A carregar...
Publicação
Text Mining Techniques for Car Price Prediction
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 2.81 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
Modern data sources routinely contain information both in unstructured and structured forms,
combining text with the usual numerical and categorical data. For instance, in websites dedicated for
selling and buying cars the listings typically include a textual description of the car. Others also include
a detailed list of numerical or categorical attributes, such as the total number of kilometers the car
has, or it´s model.
In this work project we apply text mining techniques to create predictors for car price regression from
unstructured data, the textual description in car listings. Two different types of predictors were
studied, the tf-idf features obtained from the n-gram count matrix, or the singular vectors derived from
the decomposition of the tf-idf matrix.
In this work we also examine the performance of reducing the vocabulary dimension by applying
stemming, lemmatization or not applying either of those. We also compare the effects of creating the
initial n-gram count matrix with only unigrams, unigrams and bigrams or only bigrams.
Our regression experiment shows that Support Vector Regression performs best at car price prediction
using text data as predictors with R2 = 0.77, MSE = 0.19 and MAE = 0.32. These results can be seen as
respectable given the complex nature of the task.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Text Mining Regression Analysis Car Price Prediction