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Datapirates - Employability Evaluator Model For Loan Eligibility - Fintech #33
Employability Evaluator Model For Loan Eligibility
Theme: Fintech
Project Name: Employability Evaluator Model For Loan Eligibility
Short Project Description: To help banks or students for higher education loan sanction considering achievements, work experience, educational background rather than just collateral
Azure Services Used- Machine Learning Studio, Azure Machine Learning, Data Science Virtual Machines, Azure Open Datasets
🔥 Your Pitch
Problem Statement
The majority of banks in India don’t do higher education loans easily. Students are evaluated on their economic and financial stature rather than their potential and skill by the respective banks in India.
Presence of many NBFC’S for providing loan solutions but they rely on banks for the processing who again might or might not get convinced with the student’s profile or property. According to present statistics, around 3.5-4 lakhs students opt for higher education abroad and this number will tend to increase to ~5 lakhs in 2019/2020. Roughly around 33% have by default, the backing of property or money to support their finances in the very first place and the majority just drop the plan or admit owning to the lack of financial support. Most of the countries have strict laws for international students and in-spite of performing well in the course have to return to India for not having an employment opportunity.
Methodology
The model will take into account the student’s academic achievements, skills, and financial parameters to evaluate his/her probability to get employable after master's completion. The weightage shall be given to student’s academic credentials and financial parameters as well as the university score/grading for which admission has been received.
Input Features
Undergrad CGPA
Undergrad college Tier
No. of research papers published
Number of internships during undergrad
Whether Job after Under graduation
Job Experience in years after undergraduate
World ranking of the university(Masters)
Data Science Model
Developing a Data Science classification model.
The model that evaluates a student’s profile, skills academic background.
STAGED PROCESS :
STAGE 1: Data Science Model Evaluation.
STAGE 2: Evaluating secondary parameters like
Collateral/Property.
Family Background details etc. as appropriate by the banking team.
Cases Included
Student Getting a job within 1 year of Masters Completion labeled as 1 else 0.
Following cases not considered as proper jobs (after Masters).Teaching Assistant at the University.
Research Assistant at the University.
Graduate Research Assistant at the University.
Developer jobs performed within the University.
University Research Intern.
Intern position in any company if pursued for more than 4 months.
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[x] You have followed the issue title format.
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Employability Evaluator Model For Loan Eligibility
Theme: Fintech
Project Name: Employability Evaluator Model For Loan Eligibility
Short Project Description: To help banks or students for higher education loan sanction considering achievements, work experience, educational background rather than just collateral
Team Name: Datapirates
Team Members: Yash Gandhi @yashgandhi-32
Repository Link(s): https://github.com/yashgandhi-32/studyloan-eligibility-model
Presentation Link: https://drive.google.com/file/d/13hgEO9CoQFjk9ABeKGtYHHUmrGCJf_LO/view?usp=sharing
Azure Services Used- Machine Learning Studio, Azure Machine Learning, Data Science Virtual Machines, Azure Open Datasets
🔥 Your Pitch
Problem Statement
The majority of banks in India don’t do higher education loans easily. Students are evaluated on their economic and financial stature rather than their potential and skill by the respective banks in India. Presence of many NBFC’S for providing loan solutions but they rely on banks for the processing who again might or might not get convinced with the student’s profile or property. According to present statistics, around 3.5-4 lakhs students opt for higher education abroad and this number will tend to increase to ~5 lakhs in 2019/2020. Roughly around 33% have by default, the backing of property or money to support their finances in the very first place and the majority just drop the plan or admit owning to the lack of financial support. Most of the countries have strict laws for international students and in-spite of performing well in the course have to return to India for not having an employment opportunity.
Methodology
The model will take into account the student’s academic achievements, skills, and financial parameters to evaluate his/her probability to get employable after master's completion. The weightage shall be given to student’s academic credentials and financial parameters as well as the university score/grading for which admission has been received.
Input Features
Data Science Model
Cases Included
Before you post the issue: