The problem ISDB-Startups (Internet Startups Database) solves

Entrepreneurs and Investors face lot of dilemma before starting a startup. As we all know that starting a venture is not an easy task, there are lot of challenges associated with it. To help our investors to overcome this dilemma we have designed this Platform. We have used various ml model so that they can know beforehand whether the startup they are starting would be beneficial for them or not.

features

• View the history, reviews and growth of the startups on the go.
• Create your own startup.
• Predict success and failiure of an startup using ml.
• Predict if the startup can get a loan using ml.
• Predict how much profit startup will make using ml.
• Add reviews to a startup and get reviews sentiments using ml.
• Invest on the startups using razorpay apis.
• Get rewared by investing on a startup.

So we introduced an authentication based login and signup portal for keeping it safe and we have introduced various ML model so that they can know beforehand whether the startup they are starting would be a fail or a success.
We have used natural language processing technique to check whether the reviews are positive or negative to help the investor know what others are thinking about the startup.

How I built it

I wanted to start my own startup but could'nt find suitable place hence i had this idea of building a site to raise fund without investors

Accomplishments that I'm proud of

• implemented razorpay api
• made flask apis out of pickle models
• used tailwind css in entire website

What I learned

• make apis from ml models
• using tailwind css and chakra UI

What's next for ISDB-Startups (Internet Startups Database)

• We are thinking of adding adervtisements in our platform to generate earnings from our idea.
• To help people raise funds and make them unicorn startups.
• onboarding new startups on to our platform.

Challenges we ran into

• Creating success predictor and loan predictor models.
• Using razorpay api in our application.
• Deciding which model is best for classification as per our need.