Pandas

Here is a design for the web application

Inspiration

We wanted to develop a machine learning model to predict which areas in Kenya are most at risk of deforestation, based on various environmental, social, and economic factors. This will help policymakers and environmental organizations prioritize their conservation efforts and target interventions to areas that are most at risk.

What it does

Based on data collected based on social, environmental, and social factors, the model predicted areas in the country that would be the most affected by deforestation for the next decade which will help policymakers concentrate their efforts on areas with a high risk of deforestation keeping in mind that resources are limited thus ensuring efficiency in distributing resources countrywide.

How we built it

We collected data such as the rate of urbanization in various counties, the population distribution, climatic factors in various countries, the rainfall distribution in the counties, the tree coverage, and finally the rate of deforestation in the last decade. Afterward, we cleaned the data and fitted it into a linear regression algorithm which then used this data to predict the rate of deforestation for the next decade

Challenges we ran into

One of the challenges was all the data we collected wasn't already present in one dataset and was distributed over various sites on the internet and so the data collection on the various factors affecting deforestation was tiresome but other than that, it was smooth sailing from there on.

Accomplishments that we're proud of

As a team, we learned from each other as individual team members had unique skill sets. The project involved using python programming language, creating a demo application for our soon-to-be web application, and data cleaning all of which we accomplished as a group due to our unique skill sets.

What we learned

Each individual team member learned from the other, be it web application development, data analysis, and machine learning. It was nice learning from other students from different schools and overall, it was a really exhilarating experience