Inspiration
In this report, we utilize previous examples of using Principal Component Analysis (PCA) and clustering algorithms to create a multidimensional poverty index, identify trends among specific countries, and identify countries that will benefit most from funding
What it does
We analyze the data, find trends, give suggestion for policy changes
How we built it
Using Kaggle and Jupyter Notebook and a lot of data science skills :)
Challenges we ran into
Dealing with Nan values, handling bad component values, selecting correct and informative features
Accomplishments that we're proud of
The given Index successfully encapsulates poverty levels of country
What we learned
how to deal with timed data, interpolation and time series and working with PCA
What's next for Sap Challnge
apply these policies into a real-world application !
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