In order to interact with the website, please run "if name == "main", and then wait for the link to pop up. After it shows up, click on the link and wait for the application to appear in your browser.


We wanted to make an application that raises awareness for wildfires in our environment. We want to be able to create an application that helps give safety precautions, prevention, and detection -- all in one. Our team has created a predictive model in python calculating the possibility of future random forest fires occurring in your area based on past events. We trained and refined a model to accomplish our results. Classifiers such as location (within a certain distance relative to latitude and longitude) and date (how recent the surrounding fires were) were implemented.

Challenges we ran into

We ran into some challenges creating a fully functioning website due to limitations in knowledge of website development along with machine learning. Since we are beginners in Machine Learning, a majority of our time was dedicated to learning about the mechanisms of ML/AI.

Accomplishments that we're proud of

We are proud of being able to almost complete our project, and create a partially functioning ML model for our project. We were able to train our dataset in order to predict the susceptibility of forest fires according to location and date.

What we learned

We learned the versatility of ML to the improvement of our lives, society, environment, and more. We learned more about the interaction between the back-end and the front-end. We learned about training datasets to be able to make predictions.

What's next for "the forest fire predictor"

We want to be able to create a fully functioning website in order for a user to be able to utilize its benefits, and improve its accuracy.

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