Problem Statement

The primary challenges identified are deficient data labeling and the inability to predict default due to insufficient data. These issues hinder you from making accurate predictions and understanding income patterns effectively.

Objectives

-       Conduct an in-depth analysis of the current data labeling process to identify points and areas for improvement.

-       Utilize machine learning algorithms to identify patterns and accurately classify data.

-       Develop an automated or semi- automated data labeling pipeline to minimize human errors and ensure consistency

-       Determine Business Rules for Affordability

Current Logistic Regression

As the lack of sufficient data limits traditional default prediction, propose an approach allows us to identify patterns and behaviors related to income and spending pattern in order to determine affordability.

Project Scope

The project will include the following objectives:

-       Data analysis and preprocessing to identify relevant features for income categorization. 

-       Development and implementation of data labeling optimization techniques.

-       Implementation of Machine learning methods for income categorization.

-       Documentation of results and recommendations.

-       Business Rules to determine loan affordability

Deliverables

Upon completion of the project, the following deliverables will be provided:

1. Detailed analysis of the current data labeling process and its optimization.

2.Implemented techniques for income, expense, spending pattern categorization.

3. Comprehensive documentation outlining the project methodology, findings, and recommendations.

4. Working Prototype of Implemented strategy

1. Staff Time: Dedicated staff will be available for guidance, answering questions, and offering feedback throughout the project.
2. Access to Tools and Technology: Learners will have access to advanced data labeling tools, machine learning software, and other necessary technologies.
3. Access to Data: Comprehensive datasets will be provided to ensure sufficient data for accurate predictions and analysis.
4. Training and Workshops: We will offer training sessions and workshops on data labeling, predictive modeling, and understanding income patterns.
5. Resource Materials: Learners will have access to documentation, tutorials, and research papers to aid their understanding and application of project requirements.
6. Regular Check-ins: Scheduled check-ins to monitor progress, address challenges, and ensure learners are on track.
7. Collaborative Environment: Opportunities for peer collaboration and group discussions to enhance learning and problem-solving.