You will get an Explainable AI report for your ML model using SHAP
Project details
Is your ML model a black box? Do you need to explain its predictions to stakeholders, regulators, or clients? I will create a detailed Explainable AI report for your machine learning model using SHAP analysis and Python.
What you will get:
• SHAP feature importance analysis showing which factors drive your model's predictions
• Individual prediction explanations for any applicant or data point
• Beautiful visualizations including waterfall plots, summary plots, and bar charts
• Fairness audit across demographic groups (optional)
• Clean, well-documented Python code
• PDF or HTML report ready to share with stakeholders
Why choose me:
I have hands-on experience building and deploying a real Explainable AI Loan Default Prediction system trained on 255,347 real loan records using XGBoost and SHAP. My live app is deployed on Streamlit Cloud this is not theory, this is real delivered work.I work with any sklearn-compatible ML model including XGBoost, Random Forest, Logistic Regression, and more.
Let's make your AI transparent and trustworthy!
What you will get:
• SHAP feature importance analysis showing which factors drive your model's predictions
• Individual prediction explanations for any applicant or data point
• Beautiful visualizations including waterfall plots, summary plots, and bar charts
• Fairness audit across demographic groups (optional)
• Clean, well-documented Python code
• PDF or HTML report ready to share with stakeholders
Why choose me:
I have hands-on experience building and deploying a real Explainable AI Loan Default Prediction system trained on 255,347 real loan records using XGBoost and SHAP. My live app is deployed on Streamlit Cloud this is not theory, this is real delivered work.I work with any sklearn-compatible ML model including XGBoost, Random Forest, Logistic Regression, and more.
Let's make your AI transparent and trustworthy!
AI Algorithms
Feedforward Neural Network, Linear Discriminant Analysis, Regression AnalysisAI Applications
AI-Enhanced Classification, Anomaly Detection, Sentiment Analysis, Time Series AnalysisAI Development Language
PythonAI Tools
Gradio, Hugging Face, StreamlitAI Models
Naive Bayes ClassifierWhat's included
| Service Tiers |
Starter
$25
|
Standard
$60
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | ||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | ||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | |||
Source Code |
Frequently asked questions
About Abeer
AI & Machine Learning Developer | Python | XGBoost | Data Science
Karachi, Pakistan - 6:11 pm local time
What I can do for you:
- Machine Learning model development (XGBoost, Scikit-learn)
- Explainable AI (SHAP analysis & interpretability)
- AI Chatbot development using LLMs & prompt engineering
- AI automation workflows (n8n, Zapier, Make)
- Data analysis, cleaning & visualization
- Python scripting & automation
- Generative AI integration (ChatGPT API, Claude API)
- Computer Vision basics (OpenCV)
- Data entry & Excel processing
My work speaks for itself I independently built and deployed a live Explainable AI Loan Default Prediction system trained on 255,347 real loan records, with fairness auditing across demographic groups.
Live: loan-default-abeer.streamlit.app
I am new to Upwork but not to delivering results. Fast, detail-oriented, and committed to quality work with clear communication. I welcome entry-level projects and will give every task my full effort.
Let's work together!
Steps for completing your project
After purchasing the project, send requirements so Abeer can start the project.
Delivery time starts when Abeer receives requirements from you.
Abeer works on your project following the steps below.
Revisions may occur after the delivery date.
Model & Data Review
Review your model and dataset, understand the prediction task and prepare for SHAP analysis
SHAP Analysis
Run SHAP explainability analysis, generate feature importance plots and individual prediction explanations