Critically Wild

Inspiration

Our mission is to raise awareness for wildlife and the urgent threats facing endangered species. Our platform provides a global map of endangered species, showing their current populations and predicting when they may go extinct based on real-time data.

What it does

Interactive Map: Explore species at risk worldwide, with detailed extinction timelines. Data-Driven Insights: We use multiple datasets combining climate change data, real-world population trends, and habitat destruction metrics to provide accurate predictions. Machine Learning Models: Through neural networks and extensive feature engineering, we analyze and forecast extinction risks, helping users understand the impact of human activities. Future Vision: A Step-by-Step Conservation Simulation We plan to evolve the platform into a real-world conservation simulation where users can take step-by-step actions to help save a species. This tool will guide individuals, organizations, and policymakers on the most effective ways to combat species decline, backed by advanced AI-driven predictions.

How we built it

We began by collecting and cleaning datasets on endangered species, climate patterns, and environmental threats. This step involved handling missing data, removing duplicates, and standardizing various formats so our machine learning model could make accurate predictions. Once the data was ready, we used Python to build a model that estimates the risk of extinction based on factors like habitat loss, climate shifts, and human impact.

On the backend, we set up a Python-based server to run the model and provide results through a REST API. The frontend, created with React, fetches these predictions and displays them on an interactive map built with Leaflet. When users click on a region, they can view local endangered species and see how likely each is to face extinction.

Throughout development, we tested and refined both the backend and frontend to ensure smooth data flow. While we had to scale down some initial ideas, our main focus was creating a clear, user-friendly experience that raises awareness about the urgent need for wildlife conservation.

Challenges we ran into

One of the toughest challenges we faced was integrating diverse datasets on endangered species and climate-related threats. Cleaning and structuring the data to ensure accuracy took more time than expected, but we were determined to maintain high data quality. Another challenge was building our predictive machine learning model. We had to fine-tune hyperparameters and experiment with various algorithms before we found one that produced meaningful extinction timelines. Additionally, designing a user-friendly, interactive map was no small feat. We wanted to ensure users could easily navigate the map while intuitively understanding the threat levels of different species, which required a lot of trial and error with color coding and UI design.

Accomplishments that we're proud of

We’re incredibly proud of how Critically Wild evolved throughout the project. One major accomplishment was successfully creating an interactive map that allows users to click on regions and see which species are most at risk. Seeing the data visualized with color-coded extinction timelines was incredibly rewarding. We’re especially proud of our work on the machine learning model. Despite the challenges in finding and preparing data, we were able to build a model that produces meaningful extinction predictions. While our model still needs more training, it was rewarding to see it generate insights after hours of hard work spent building and refining the dataset. Watching everything come together—data collection, machine learning, and visualization—was one of the most fulfilling moments of the entire project.

What we learned

From the very start, we had big dreams and high expectations for creating a big project with all the bounties. We envisioned a fully immersive simulation with multiple features and sophisticated algorithms to capture the full complexity of wildlife conservation. However, as we progressed through the project, we quickly realized that we had to scale down significantly. Time constraints, technical limitations, and the complexity of the datasets meant that we had to let go of several features that we were initially excited about. We had to prioritize the core functionalities that could be delivered within the available time frame.
This experience taught us the importance of managing expectations and focusing on achievable milestones. We learned that a clear, focused vision is more effective than trying to do everything at once. Additionally, we discovered the complexities of bridging the backend and frontend. Making sure the data from our machine learning model displayed accurately and efficiently on the map required more effort than we initially anticipated. It taught us the value of clear communication between team members working on different parts of the tech stack, as well as the importance of robust testing and incremental integration.
Ultimately, we learned that every project involves compromise. While we had to cut down on certain features, we also learned how to deliver a functional and impactful product with the resources at hand.

What's next for Critically Wild

Looking ahead, we have big dreams for Critically Wild! One of our next steps is to develop a simulation mode where users can experience real-life conservation scenarios. Imagine clicking on a critically endangered animal and receiving a quest that explains how specific conservation actions—like habitat restoration or policy changes—could improve its survival. We also want to launch the project in public spaces such as malls and schools to maximize its outreach potential. Expanding the dataset to include even more species and threats is another priority, as we aim to create the most comprehensive conservation tool possible. Lastly, we envision community involvement, where users can share their own conservation stories or actions, fostering a global community united for wildlife preservation.

Together, we can use data and technology to make a difference. Time is ticking—start clicking!