NameBerry

Inspiration

I was inspired by watching videos of cute babies on the internet and wondering how their parents choose their names. I decided to create an application that can help future parents find the best name for their newborns.

What it does

This application takes some parameters from the user, such as gender, origin, meaning, and popularity, and suggests a suitable name for their baby. The user can also see the meaning and origin of each name and how popular it is in the US.

How I built it

I built a machine learning model that can generate baby names based on the given parameters. I used the US Baby Names dataset for training my model. For the front-end, I used HTML, Bootstrap, and JavaScript to create a simple and user-friendly interface. For the back-end, I used Flask to connect the front-end with the model and handle the requests and responses.

Challenges I ran into

The main challenge I faced was finding the right features to train my model and make it generate realistic and diverse names. I also had to clean and preprocess the dataset to remove any duplicates or errors.

What I learned

This was my first time using Flask as a back-end framework, so I learned a lot about how to set up a web server and handle HTTP requests. I also learned how to use Bootstrap to design responsive web pages and how to use JavaScript to add interactivity and functionality.

Built With

• bootstrap
• css
• flask
• html
• javascript
• numpy
• pandas
• python
• scikit-learn