Problem Statement:

Excallidraw Drawings for the Repo: https://excalidraw.com/#json=mJ0zu5YX0Rr9m3LSsi4eI,DXbP2k-uVFWDfPwbjT4VTw

Business Context:

The project aims to develop a machine learning system that predicts individual income levels based on demographic and employment data.

The prediction boundary is set at $50,000 annually (binary classification problem).

The solution will help in understanding socio-economic factors affecting income levels.

Enable data-driven decision making for policy makers and financial institutions.

Identify key socio-economic factors influencing income disparities.

Support targeted intervention programs for economic development

Key Stakeholders

Policy Makers: For evidence-based policy development

Financial Institutions: For risk assessment and product development

Social Services: For resource allocation and program planning

Research Organizations: For socio-economic studies

Dataset Details:

Let's visualize the data structure and features:

classDiagram
    class Features {
        Demographic_Features
        Employment_Features
        Financial_Features
        Other_Features
    }
    
    class Demographic_Features {
        age: numeric
        education: categorical
        education-num: numeric
        race: categorical
        sex: categorical
        country: categorical
    }
    
    class Employment_Features {
        workclass: categorical
        occupation: categorical
        hours-per-week: numeric
        relationship: categorical
        marital-status: categorical
    }
    
    class Financial_Features {
        fnlwgt: numeric
        capital-gain: numeric
        capital-loss: numeric
    }
    
    Features --> Demographic_Features
    Features --> Employment_Features
    Features --> Financial_Features

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