Inspiration: since we are in the information technology sector, the ratio of men: women in the sector shows that we need more women in this field. Limitation access of information technology is one of the reason why some girls and women are not in the Tech world.
What it does it classifies based on the socio economic issues if men and women have access to information technology. This then helps in policy making. Since we are now moving to times where everything is online, job application, information and current affairs. It is good to know how many still don't have access to IT so we can improve our policies by 2030
How we built it: we used data from STATS SA general household survey, did data cleaning and preprocessing. Feature engineered a column/ variable named access to technology using different variables from the dataset. We used a random tree classifier since this is a classification model. We had to oversample our target variable using SMOTE because it was not balanced then after used cross validation tool to test if the model is not overfitting.
Challenges we ran into: looking for data and balancing the target variable. We had to build a lot of models to make sure it does not over nor under fit.
Accomplishments that we're proud of: working together as a team and coming up with the best concept for the project. Improved model building skills
What we learned: mostly ways of balancing the dataset and how broad information technology is
What's next for SDG goal 5: Gender based analysis of IT access
Having to come up with a user interface deployment so stakeholders are able to put in data and see what the current status in terms of access to information technology.
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