Household Poverty Visualizer

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  F1 scores

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  Importance of educating women

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  Importance of education

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  Poeverty levels

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  Lack of amneties

Inspiration

• 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

Built With

• python