Costa Rican Household Poverty Level Prediction

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The Problem

Here's the backstory: Many social programs have a hard time making sure the right people are given enough aid. It’s especially tricky when a program focuses on the poorest segment of the population. The world’s poorest typically can’t provide the necessary income and expense records to prove that they qualify.

In Latin America, one popular method uses an algorithm to verify income qualification. It’s called the Proxy Means Test (or PMT). With PMT, agencies use a model that considers a family’s observable household attributes like the material of their walls and ceiling, or the assets found in the home to classify them and predict their level of need.

While this is an improvement, accuracy remains a problem as the region’s population grows and poverty declines. To improve on PMT, the IDB (the largest source of development financing for Latin America and the Caribbean) has given this dataset and challenge to improve predcition of Poverty level so that the people who actually need the aid get it and everyone can work towards greater good.

Beyond Costa Rica, many countries face this same problem of inaccurately assessing social need.
Source : Kaggle

Our Solution with MLOPS

We aim to help the IDB(Inter American development bank) to identify poverty levels and make sure aid is provided to those in need and make a good impact and help the social cause of uplifting the underprivileged in Latin America

With MLOPS we created a pipline, which ingests data from a server and then cleans the data and transforms it and trains the model,saves the model and serves the model as an API

We can call the API served by MLOPS to get solution for our problem in real time, feed the inputs and get the result.This helps us deliver a good solution for the social problem

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Set project name and set project path and artifacts path

Ingestion of data from a remote Server and saving the data.Transform/Clean the data and save the data

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Create the ML Model and save the model.Serve the model.

Call the API and showcase the great impact and usecase the project can provide.

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