Difference between revisions of "Group22 Proposal"
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==<font color = "#367c45">Motivation</font>== | ==<font color = "#367c45">Motivation</font>== | ||
| − | + | The main core objectives of this paper are to understand how Kiva’s use of its discourses evokes certain expectations for participants lending money through their microfinance program as to what the impact their money will have on borrowers. And explore, visualize and connect the available data sets in order to gain in-depth knowledge, thus identify the welfare characteristics of each region. | |
| − | + | We would like to know the level of poverty of each borrower in order to set investment priorities, help inform lenders, and understand Kiva’s target communities. | |
| − | + | • Explore loan difference among the world and detect the different patterns and poverty causes among different countries. | |
| − | + | • Understand how online platform effect people’s real life. | |
| − | + | • Detect the development potentials of Kiva, and help to provide the world with a better future with less impoverished people. | |
| − | + | <br> | |
==<font color = "#367c45">Challenge</font>== | ==<font color = "#367c45">Challenge</font>== | ||
Revision as of 09:41, 13 August 2018
SMALL LOAN, BIG DIFFERENCE
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Introduction
Kiva is an international non-profit, founded in 2005 and based in San Francisco, with a mission to connect people through lending to alleviate poverty. By lending as little as $25 on Kiva, anyone can help a borrower start or grow a business, go to school, access clean energy or realize their potential. For some, it’s a matter of survival, for others it’s the fuel for a life-long ambition. And Kiva lenders have provided over $1 billion dollars in loans to over 2 million people in 87 countries.
Motivation
The main core objectives of this paper are to understand how Kiva’s use of its discourses evokes certain expectations for participants lending money through their microfinance program as to what the impact their money will have on borrowers. And explore, visualize and connect the available data sets in order to gain in-depth knowledge, thus identify the welfare characteristics of each region.
We would like to know the level of poverty of each borrower in order to set investment priorities, help inform lenders, and understand Kiva’s target communities.
• Explore loan difference among the world and detect the different patterns and poverty causes among different countries.
• Understand how online platform effect people’s real life.
• Detect the development potentials of Kiva, and help to provide the world with a better future with less impoverished people.
Challenge
- Data cleaning. Many variables have large amount missing value. Currencies with different measurements.
- Correlation between MPI and number of orders and money amounts. Not all orders from poor countries.
Mission
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STEP |
MISSION |
BY |
WEEK |
STATUS |
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1 |
brainstorm |
All |
4 |
Done |
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2 |
select topic&demo |
All |
5&6 |
Done |
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3 |
consultation |
GAO,JIAOYANG & MU,FUYAO |
7 |
Done |
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4 |
edit wiki page |
YU,ZHECHENG |
8 |
Done |
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Demo
