Spam Detection

Inspiration

Health and Wellness are very important, but Spam messages can make people distressed, so it is important to have some way to intelligently classify spam messages.

What it does

The web application allows the user to enter a message they have received then uses a machine learning algorithm to detect whether a given message is spam or not and displays the result to the user.

How we built it

I built the web application using Flask and HTML, with the backend in Python. I created the machine learning model using sklearn, which is a Python library for machine learning.

Challenges we ran into

The main challenge was gathering the data and training the model. I used Kaggle as a resource to find datasets that contain comprehensive data, and tried different models as well.

Accomplishments that we're proud of

I am proud of the fact that the web application works and classifies messages correctly if the messages are similar to the input data it was trained on.

What we learned

I learned how to use Python libraries to create machine learning models then add those insights visually to the user in order to positively impact users since they would have an easy way to check if a message is spam.

What's next for Spam Detection

The next step is to continue improving the model by expanding the dataset, which will allow for higher accuracy classifying messages that are unique from the training dataset.

Built With

• flask
• pandas
• python
• scikit