Inspiration
In the modern world, shopping online is becoming more popular because the internet made people's life more flexible. Specifically for furniture, generally people commute to stores with memorized dimensions and other characteristics. In modern times, the internet made shopping a better experience. However, the question remains: what more can we do to make the experience much more feasible for the customer? In this project, we will implement a feature that will assist people in looking for furniture using their own furniture images to web scrape for similar products.
What it does
After uploading an image of the furniture's picture, this feature should return products of a similar size, shape, and other closely resembling products for the customer.
How we built it
The project was built using NodeJS for the backend and react for the front end.
Challenges we ran into
The fundamental challenge in this project was creating an application that personalizes and/or optimizes the e-commerce shopping experience of a consumer. We had issues with the API transferring data to the backend -- main issue. Other issues were source control and merge conflicts; however we were able to overcome the issues.
Accomplishments that we're proud of
The most significant accomplishments we all are proud of were working as a team and creating an eCommerce website. Dividing this enormous project into four compartmentalized tasks made it feasible to finish the feature. Also, building a beautiful UI interface.
What we learned
Many of us had prior knowledge of the MERN model front-end and back-end web development using React.js and deep experience with node.js. Most hours were spent testing different backend components with new versions of the front end. The obstacles we encountered while debugging and testing various new methods helped broaden our understanding of the software development lifecycle.
What's next for E-commerce Website
The next step for our project is to add a Machine Learning Model to identify more optimal matches and implement a web scrapper that can search central repositories of high-volume websites and return best-valued items.
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