Recession Watch: Assessing Economic Outlook

Initial State Of Website
Website After User-Input
Backend: Usage of NLP Model
Total Sentiment Analysis
Aggregated Sentiment Dictionary
Web Scraping Code

Inspiration

Our inspiration for this project stemmed from a personal experience of feeling overwhelmed and unsure about the state of the financial market. As college students with limited time and resources, we found it challenging to stay up-to-date with the latest news and opinions regarding the stock market. We realized that we weren't alone in this struggle and decided to leverage our knowledge of machine learning and natural language processing to create a solution. Our goal was to develop a tool that would alleviate the stress and uncertainty around financial stability by providing clear and concise information from expert sources. We were excited to take on this challenge and motivated by the opportunity to make a difference. During the hackathon, we were impressed by the supportive and collaborative environment, which helped us to turn our idea into a reality.

What it does

The Problem of stock instability could affect many students' or adults' lives as they will always be occupied by whether their money is safe or not. Our program takes this problem away and eases your stress. By using our web scrapping program and Machine learning models we can scan many recent articles by many credible different banks on their expert opinion of the stock market. Moreover, The user also has a hand in who they trust to place their money. Using our friendly front-end User Interface, the user can select the top firms that he believes will provide them with the most substantial data. And our program will do the rest.

How we built it

To tackle this project, we began by brainstorming various approaches and ideas to solve the problem at hand. We quickly came to a consensus that a dynamic website, where users could interact with the backend, would be the most effective solution. We then divided our team into different roles to maximize efficiency. One team member focused on researching and compiling 15 up-to-date articles on the most prominent banks, while another focused on developing the backend with machine learning. Meanwhile, the third team member worked on the front end of the website using HTML and CSS and also facilitated the connection between the back end and front ends. Through a collaborative effort and with each team member contributing their unique skills, we were able to create a functional and user-friendly platform that meets the needs of our target audience.

Challenges we ran into

One of the most significant challenges we faced during the project was integrating the front end and back end of our application. Although each team member worked diligently on their tasks, we faced difficulty merging the different parts of the project together. As a result, we encountered obstacles in connecting our code with tools such as Flask. Additionally, it was our first time using many of the tools we employed in this project, which made the learning curve steeper and impeded our progress. In hindsight, we could have improved this process by dedicating more time to learning each other's code throughout the project, which would have made the merging process more efficient. Despite this challenge, we persevered, and with persistence and collaboration, we were able to overcome these obstacles and deliver a successful project.

Accomplishments that we're proud of

We are incredibly proud of several accomplishments that we achieved during the project. One of our biggest accomplishments was working with packages and libraries that were new to us. Despite the learning curve, we were able to master these tools and leverage them effectively to create our project.

What we learned

We learned how to work efficiently in a small team and divide tasks to maximize productivity. We also gained experience in working with new packages and libraries and expanding our knowledge in machine learning and natural language processing. We learned the importance of effective communication and collaboration in completing complex projects, especially when integrating separate components. These lessons will be valuable in our future projects as we continue to grow and develop as developers.

What's next for Recession Watch: Assessing Economic Outlook

In the future, we plan to expand Recession Watch by adding more financial institutions, improving our machine learning algorithms, creating a mobile app, and providing real-time updates for users. We could also enhance the machine learning and natural language processing algorithms to improve the accuracy and relevance of the information provided to users. We believe that there is a lot of potential for growth and development, and we are excited to explore new possibilities for the project.