I will do ml model explainability, shap analysis and bias audit

Does your ML model make decisions nobody can explain? In 

finance, healthcare, and HR regulators now require you 

to justify every prediction your model makes. I will 

analyse your model using SHAP, LIME, and fairness 

auditing tools and deliver a clear report showing exactly 

why your model makes each decision.

WHAT I DELIVER:

MODEL EXPLAINABILITY:

SHAP values global and local feature importance

LIME explanations for individual predictions

Feature contribution waterfall and summary plots

Decision boundary visualisation

Works with any model XGBoost, Random Forest,

 Neural Networks, Logistic Regression, LightGBM

BIAS & FAIRNESS AUDIT:

Demographic bias detection across protected groups

Fairlearn and IBM AI Fairness 360 analysis

Disparate impact and equal opportunity metrics

Recommendations to reduce bias without hurting accuracy

DELIVERED AS:

Full Python code (Jupyter Notebook)

PDF report with charts and plain-English explanations

Executive summary suitable for non-technical stakeholders

PERFECT FOR:

Fintech explain credit scoring decisions

HR tech audit hiring or performance models

Healthcare justify diagnostic AI predictions

Any company deploying ML.