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I will add explainable ai shap or grad cam to your machine learning model
Mansoor Ali
About this gig
Here's the full gig description for Gig 3:
Is your machine learning model a "black box"? I'll make it interpretable.
I add explainability layers to your existing ML/DL models so you or your stakeholders, clients, or regulators can understand why your model makes the predictions it does. This is critical for healthcare, finance, security, and any high-stakes application where "trust me, it works" isn't good enough.
What I offer:
SHAP waterfall & summary plots (for tabular/structured data models)
Grad-CAM heatmap overlays (for image/CNN models)
Feature importance breakdown with written interpretation
Clear visual report explaining model behavior
Proven results:
I've applied this exact approach across multiple deployed projects including a malware classifier (95.89% accuracy) with SHAP waterfall plots and MITRE ATT&CK mapping, and a chest X-ray detector with Grad-CAM visual explanations. Both are live and publicly demoed.
Why work with me:
- Real applied XAI experience, not just theory
- Clear visuals + written explanations non-technical stakeholders can understand
- Fast turnaround
Tools I use: SHAP, Grad-CAM, TensorFlow, Scikit-learn, Python
Get to know Mansoor Ali
Mansoor Ali
- FromPakistan
- Member sinceJul 2025
- Avg. response time1 hour
Languages
English, Urdu, Hindi
Hi! I'm Mansoor Ali, a BS Artificial Intelligence student at UMT and a passionate Flutter Developer & Machine Learning Engineer. I specialize in building modern, responsive, and user-friendly mobile applications using Flutter, Firebase, REST APIs, and clean UI design. I also develop AI and Machine Learning solutions with Python, Scikit-learn, and TensorFlow. I have completed a Flutter development internship and built multiple real-world projects. My goal is to deliver high-quality, scalable, and reliable solutions that exceed client expectations.
My Portfolio
Other AI Development Services I Offer
FAQ
What's the difference between SHAP and Grad-CAM?
SHAP explains predictions from tabular/structured data models (like classifiers using numeric or categorical features) by showing which features drove each decision. Grad-CAM is for image-based CNN models it highlights which regions of an image the model focused on. I'll recommend the right one ba
What do I need to send you to get started?
Your trained model file, along with the code/framework it was built in (TensorFlow, PyTorch, Scikit-learn, etc.) and a sample of the data it was trained or tested on.
Will this work with any model, or only certain types?
SHAP works well with most tabular models (Random Forest, XGBoost, logistic regression, etc.). Grad-CAM is specific to CNN-based image models. If you're unsure which applies to your model, message me and I'll confirm before you order.
Can you explain the results in plain language, not just technical plots?
Yes Standard and Premium packages include a written interpretation alongside the visuals, so both technical and non-technical stakeholders can understand what's driving your model's decisions.
Can you apply this to a model I haven't built yet?
This gig is for adding explainability to an existing trained model. If you need a model built from scratch first, check out my other gig for custom image classification models, or message me and I can guide you on the right combination of services.
