EmoFinance.

Would you want to invest when you’re having a bad day? What if you are making the biggest mistake of your life? It can be the difference between a millionaire and a billionaire.

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Inspiration: We were inspired by the fact that people face intense losses due to the various emotions they feel. Many people don't realize the strong connection between finance and emotions. For example, one who is sad from losing a big chunk of their stock wants to get it all back or someone who is overly excited overall may make an impulsive decision.

What it does: It aims to take in text that people enter and identify how they feel through our AI model. The importance of this is to identify how they're feeling. Most of this can have future implications with other financial websites to allow people and give them financial advice. For example, if they're in a good mood, they can invest in some companies. Good emotions can be related to good decisions which is key in finance and investing.

How we built it: We built it with JavaScript, HTML, CSS, and Python. JavaScript, CSS, and HTML were used to make the website. Python we used various Libraries such as Pandas, and SkLearn, as well as Tokenization and Confusion Matrix for our AI model in Google Collab.

Challenges we ran into: We spent too much time on the model. It was super hard. It's an AI model that we fed data from Kaggle. We didn't have time to link it with the website. However, we believe it has a lot of potential to be used.

Accomplishments that we're proud of: We are proud of the model. The accuracy rate is 95%. Once we connect the website to the AI model, we believe it can potentially be used to take in user text identify how they're feeling, and prompt them to make decisions based on that.

What we learned: We learned about the connection between emotions and finance. We also learned about the AI model since we used other resources to be able to write some lines. It definitely takes time to train the model.

What's next for EmoFinance: We will connect the website to the model so users can enter in text and then the computer will output the related emotion. Based on that, it will give key advice regarding whether to invest or not or some other financial decision.

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