Build a NLP-python based binary classifier, that differentiates between "good product" and "bad product" query.

1) Prepare dataset for good product vs bad product query if required. ( One can do it quickly using [login to view URL] )

2) Examples of good product queries

- "nail polish to go with navy dress"

- "computer science book"

- "dog seat cover"

- "dog seat cover for ford f150"

- "iphone 7 yellow case"

- "multi purpose hammer"

- "hammer with rubber head"

- "Samsung phone's best cover"

- "General purpose hammer"

- "non toxic baby soap"

3) Examples of bad product queries:

- "battery life of iphone 7"

- "nail polish may kill you"

- "social work"

- "table dimension"

- "rock music"

- "computer science"

- "hammer broke iphone 7"

- "nokia phone stronger than hammer"

- "iphone 7 damaged by new case"

- "Samsung phone looks ugly with cover"

Criteria for success: Will randomly query suggestions from [login to view URL] and check how the algorithm is working.

Note 1: Attached files, I got from [login to view URL], by using some seed words like: "battery life", "nail polish", "rock music", "social work", "dog seat cover", "iphone 7" and "hammer" . I have a paid account for answerthepublic.com. Do let me know if you want more data for seed queries.

Note 2: I tried to remove few bad queries by using some stop-words like "you", "my", "won't", etc., And also using bit of nltk based POS tagging and considering only queries ending with nouns. But had minimal success. Looking for a superior solution with high precision [ Its ok if you loose few good queries, but classifying bad query as good query has to be very minimal ]

Note 3: Feel free to use any of the nlp libraries like spacy, nltk etc.,

Additional task: Convert the query to product if possible

Few Examples:

"social work" to "social work book"

"rock music" to "rock music cd"

"computer science" to "computer science book"

Квалификация: Естественный язык, Python

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О работодателе:
( 5 отзыв(-а, -ов) ) Visakhapatnam, India

ID проекта: #20070843

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