Problem Statement
The e-commerce business is quite popular today. Here, you do not need to take orders by going to each customer. A company launches its website to sell the items to the end consumer, and customers can order the products that they require from the same website. Famous examples of such e-commerce companies are Amazon, Flipkart, Myntra, Paytm and Snapdeal.
Suppose you are working as a Machine Learning Engineer in an e-commerce company named 'Ebuss'. Ebuss has captured a huge market share in many fields, and it sells the products in various categories such as household essentials, books, personal care products, medicines, cosmetic items, beauty products, electrical appliances, kitchen and dining products and health care products.
With the advancement in technology, it is imperative for Ebuss to grow quickly in the e-commerce market to become a major leader in the market because it has to compete with the likes of Amazon, Flipkart, etc., which are already market leaders.
As a senior ML Engineer, you are asked to build a model that will improve the recommendations given to the users given their past reviews and ratings.
In order to do this, you planned to build a sentiment-based product recommendation system, which includes the following tasks.
Data sourcing and sentiment analysis
Building a recommendation system
Improving the recommendations using the sentiment analysis model
Deploying the end-to-end project with a user interface
Prediction
• Performed Sentiment Analysis on Amazon Product Reviews using the User Experiences and ratings for predicting their helpfulness.
• Built Recommender System-Item Based Collaborative Filtering Model to find 2 most similar items to an Amazon product.
• Implemented NLP model using SVM , Naïve Bayes and Logistic Regression and Recommender system using K nearest neighbors.