Feature extraction
<p>Suppose I have been given data sets with headers : 
id, query, product_title, product_description, brand, color, relevance.</p>

<p>Only id and relevance is in numeric format while all others consists of words and numbers. Relevance is the relevancy or ranking of a product with respect to a given query. For eg - query = "abc" and product_title = "product_x" --> relevance = "2.3"</p>

<p>In training sets, all these fields are filled but in test set, relevance is not given and I have to find out by using some machine learning algorithms. I am having problem in determining which features should I use in such a problem ? for example, I should use TF-IDF here. What other features can I obtain from such data sets ? </p>

<p>Moreover, if you can refer to me any book/ resources specifically for 'feature extraction' topic that will be great. I always feel troubled in this phase. Thanks in advance. </p>

<p>I think there is no book that will give the answers you need, as feature extraction is the phase that relates directly to the problem being solved and the existing data,the only tip you will find is to create features that describe the data you have. In the past i worked in a problem similar to yours and some features i used were:</p>

<ul>
<li>Number of query words in product title.</li>
<li>Number of query words in product description.</li>
<li>n-igram counts</li>
<li>tf-idf</li>
<li>Cosine similarity</li>
</ul>

<p>All this after some preprocessing like taking all text to upper(or lower) case, stemming, standard dictionary normalization.</p>

<p>Again, this depends on the problmen and the data and you will not find the direct answer, its like posting a question: "i need to develop a product selling system, how do i do it? Is there any book?" . You will find books on programming and software engineering, but you will not find a book on developing your specific system,you'll have to use general knowledge and creativity to craft your solution.</p>

python machine-learning