link : https://www.youtube.com/watch?v=PqscHXQ4oxM
The Solution250 is a school project developed to provide a simple and efficient digital solution for businesses offering products or services with customer feedback. The traditional process of analyzing and drawing conclusions from scattered feedback can be time-consuming and resource-intensive. Leveraging machine learning algorithms, this project aims to extract summarized and quantified insights directly from raw data.
Project Name: 🌟 Solution250 🌟
- Description
- Table of Contents
- Features
- Installation
- Usage
- Technologies Used
- Contributing
- License
🌟 Simplified feedback analysis: The project employs machine learning algorithms to extract valuable insights from customer reviews, simplifying the process of drawing conclusions.
🌟 Free play for visitors: Visitors to the website can access the content for free, but with restricted functionality. They can submit reviews and receive polarity ratings in the form of stars (1 to 5).
🌟 Comprehensive dashboard: For businesses seeking in-depth feedback analysis, the project offers an upgraded plan. This plan provides complete feedback for a specific product and generates a comprehensive dashboard. It even supports popular platforms like Amazon, allowing businesses to input an Amazon product link and receive a detailed product overview.
To install and run the Feedback Analyzer locally, please follow these steps:
Clone the repository: git clone https://github.com/jeandtx/solution250.git Navigate to the project directory: cd solution250 Install the dependencies: npm install Configure any necessary environment variables. Launch the application: npm run dev
Once the application is running, users can access the website to utilize the features. Visitors can submit reviews and receive polarity ratings. Businesses can opt for the upgraded plan to gain access to the comprehensive dashboard for their specific product.
🔧 Machine Learning: The project utilizes machine learning algorithms to analyze and extract insights from customer feedback data.
🔧 Web Scraping: The implementation includes web scraping techniques to retrieve relevant data from external sources, such as Amazon product pages.
🔧 Frontend: The frontend is built using HTML, CSS, and JavaScript to provide an intuitive user interface.
🔧 Backend: The backend is powered by Node.js and utilizes various libraries and frameworks to handle data processing and generate the required insights.
We welcome contributions from the open-source community to enhance the Feedback Analyzer project. If you wish to contribute, please follow these steps:
Fork the repository.
- Create a new branch.
- Make your changes and commit them.
- Push your changes to your forked repository.
- Submit a pull request detailing your changes.
The Feedback Analyzer project is licensed under the MIT License. You can find more information in the LICENSE file.