AI News, Can Machine Learning predict Poverty?

Can Machine Learning predict Poverty?

World Bank hosted its poverty prediction competition on the competition hosting website drivendata.org.

Some information we can derive right by looking at the dataset is: One way to dive deeper into data (quickly) is to use the new package Pandas-Profiling (which can be downloaded from GitHub here). This package does a lot of primary analysis and saves them as pretty HTML files one can view on their browser.

Some more conclusions we can draw are: If one looks at the datatypes of the objects, they can see that the data is a mix of categorical (attributes which can take one out of a constant number of enumerable values) and numerical values (both floats and integers).

Another important property of dataset is the imbalance between +ve and -ve classes (non-poor people vastly outnumber poor people).

To train models on such skewed data, we tried different approaches using an imbalanced-learn library in Python: The dataset was preprocessed as follows: We now talk about multiple approaches that we tried.

However, unlike text,  this dataset has no concept of sequence, so we decided to use a Neural Network common in text classification, but doesn’t take order into account.

Country B where the highest accuracy we ever received (even better than our best performing model) was using Self Normalized Deep Neural Network, the results don’t translate on the leaderboard where we keep getting low scores (high logloss).

We wrote a data pipeline for trying out different tricks we have mentioned (successful/unsuccessful) at the start of the segment and a pipeline to Grid Search over different hyperparameters and try a 5-fold Cross-Validation.

The tricks which worked above combined with Grid Search gave massive boosts to our scores and we could beat 0.2 logloss and then 0.9 logloss score too.

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