Sentiment Based Product Recommendation System

App demo using streamlit

Check out https://saurabhdugdhe-sentimentbasedproductrecomme-streamlit-app-ngfywy.streamlit.app/

Table of Contents

• Problem Statement
• Data
• Technologies Used
• Contact

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

Data

1. sample30.csv - This dataset consists of 30,000 reviews for more than 200 different products. The reviews and ratings are given by more than 20,000 users. Please refer to the following attribute description file to get the details about the columns of the Review Dataset.
2. Data+Attribute+Description.txt - Data dictionary for the dataset sample30.csv

Technologies Used

• Python (3.9.15)
• Numpy (1.21.5)
• Pandas (1.4.4)
• Seaborn (0.12.1)
• matplotlib (3.5.0)
• nltk (3.7)
• spacy (3.3.1)
• regex (2022.7.9)
• wordcloud (1.8.2.2)
• scikit-learn (1.0.2)
• imbalanced-learn (0.9.0)
• xgboost (1.5.0)
• flask (1.1.2)
• cloudpickle (2.0.0)

Contact

Created by @saurabhdugdhe