Q4. You are building a product classification system for an online electronics store. The system should classify an incoming stream of millions of products to one of the 3000+ leaf level product types in the taxonomy such as laptops, smart TVs, wireless headphones, car speakers, among others. The system should be very precise because it's important to assign products to the right category to facilitate the customer shopping experience. Each instance in yourdataset has product title, description and image fields. See example below: OontZ Angle 3 (3rd Gen) - Bluetooth Portable Speaker, Louder Volume, Crystal Clear Stereo Sound, Rich Bass, 100 Ft Wireless Range, Microphone, IPX5, Bluetooth Speakers by Cambridge Sound Works (Black) by Cambridge Soundworks ***** 51,181 ratings I s05 answered questions Amaron's Choice for speakers biuetooth wireless Price: $24.99 vprime FREE Returns Coupen OSave an extra $5.00 when you apply this coupon. Details 810 Get $70 off instantly: Pay $0.00 52499 upon approval for the Amazon Prime Rewards Visa Card. No annual fee Color: Black OontZ $24.9 $299 $29.9 A S2 pime $2199 rine HIGHER QUALITY CRYSTAL CLEAR STEREO SOUND - The Oontz Angle 3 (3rd Gen) is Designed and Engineered by Cambridge Sound Works in the USA for greater clarity sound, accurate mids and clear highs from dual precision acoustic stereo drivers; the bass output is enhanced by our proprietary passive bass radiator; unique triangular design and downward facing bass radiator further enhance the sound quality; the Higher Quality Crystal Clear Sound & Features distance it from the competition LOUDER VOLUME - Surprisingly loud, the Volume Booster 10+ watt power AMP pumps out more volume and plays your music with no distortion, even at maximum volume; the louder volume makes the Oontz Angle 3 (SRd Gen) Portable Bluetooth ----- -------.-------- -n------ a. [5 pts] What features would you use for your machine learning-based classifier? b. [5 pts] Assume that you only have access to product titles in your dataset (i.e., you have less data to play with) instead of product titles, description and images. How will this affect feature engineering and the NLP pipeline for your classifier? c. [10 pts] Obtaining training data is paramount for a large-scale classification system. You have a limited budget and can't hire an army of analysts to manually label every single instance. Discuss some strategies for obtaining training data for the classifier. d. [5 pts] How would you handle products that are misclassified?