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Sherlocked-Blaire/Financial-Inclusion-in-Africa

Domaine:

socioeconomic

Type de record:

project
Créateur:
She
Hôte:
The objective of this project is to create a machine learning model to predict which individuals are most likely to have or use a bank account # FINANCIAL INCLUSION IN AFRICA ## Background of study Financial Inclusion remains one of the main obstacles to economic and human development in Africa. For example, across Kenya, Rwanda, Tanzania, and Uganda only 9.1 million adults (or 13.9% of the adult population) have access to or use a commercial bank account. Traditionally, access to bank accounts has been regarded as an indicator of financial inclusion. Despite the proliferation of mobile money in Africa, and the growth of innovative fintech solutions, banks still play a pivotal role in facilitating access to financial services. Access to bank accounts enable households to save and facilitate payments while also helping businesses build up their credit-worthiness and improve their access to other finance services. Therefore, access to bank accounts is an essential contributor to long-term economic growth. ## Aim The objective of this project is to create a machine learning model to predict which individuals are most likely to have or use a bank account. The models and solutions developed can provide an indication of the state of financial inclusion in Kenya, Rwanda, Tanzania and Uganda, while providing insights into some of the key demographic factors that might drive individuals’ financial outcomes. ## Data The data was collected from a Zindi competition.Here is the link to the data -Financial Inclusion in Afri… ### About the data Financal Inclusion Survey Data The main dataset contains demographic information and what financial services are used by approximately 33,610 individuals across East Africa. This data was extracted from various Finscope surveys ranging from 2016 to 2018, and more information about these surveys can be found here: FinAccess Kenya 2018 Finscope Rwanda 2016 Finscope Tanzania 2017 Finscope Uganda 2018 The data have been split between training and test sets. The test set contains all information about each individual except for whether the res …

Visit

github.com

Tasks

text classification

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