- It’s difficult to detect the impact of various factors on the poverty of a particular household.
- Most countries don’t have the resources to conduct comprehensive & accurate surveys which results in many factors & households being overlooked.
- Most poverty predictions are done using financial data which is not accurate for a variety of reasons.
- A cheaper method is needed that can use other kinds of data such as aerial surveys etc.
What it does
- Visualizes factors affecting poverty in detail
- Financial data of the population is NOT required to predict the poverty levels of every household.
- Poverty prediction can be done from data taken from other sources such as aerial surveys, education surveys etc.
- Reduces cost & manpower reqd. for conducting analysis.
- Easy analysis of all the factors impacting poverty.
How we built it
We performed the data exploration, feature engineering & ML modelling all using Python on the extremely useful Jupyter Notebook
Challenges & Accomplisments
- The data had a lot of factors & we had to wrap our head around its significance and impact on our target.
- We were able to make an ML model with good results.
- We learned to use real world data to solve challenges for social good.
What's next for Household Poverty Predictor
- We plan to try other alogrithms to increase the F1 score
- We plan to tune some hyperparameters later on