You will get comprehensive Explainability report of AI model
Project details
This project delivers Explainable Artificial Intelligence (XAI) solutions to help clients understand, validate, and trust their machine learning models. The service provides clear insights into how models make predictions and which factors drive their decisions.
The project identifies the most influential features used by the model during data processing and quantifies the relative importance of each feature. It also explains how individual variables contribute to specific predictions, enabling clients to interpret model outputs with confidence.
Depending on client requirements, the solution may include feature importance analysis, SHAP-based explanations, local and global interpretability assessments, visualizations, and detailed reports. These insights can support model validation, regulatory compliance, bias detection, performance improvement, and stakeholder communication.
The final deliverables are designed to make complex AI models more transparent, interpretable, and actionable for both technical and non-technical audiences.
The project identifies the most influential features used by the model during data processing and quantifies the relative importance of each feature. It also explains how individual variables contribute to specific predictions, enabling clients to interpret model outputs with confidence.
Depending on client requirements, the solution may include feature importance analysis, SHAP-based explanations, local and global interpretability assessments, visualizations, and detailed reports. These insights can support model validation, regulatory compliance, bias detection, performance improvement, and stakeholder communication.
The final deliverables are designed to make complex AI models more transparent, interpretable, and actionable for both technical and non-technical audiences.
Machine Learning Tools
BERT, NLTK, NumPy, Open Neural Network Exchange, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | Unlimited | Unlimited | 0 |
Number of Model Variations | 2 | 5 | 5 |
Number of Scenarios | 2 | 5 | 7 |
Number of Graphs/Charts | 5 | 10 | 12 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
6 reviews
(5)
(1)
(0)
(0)
(0)
This project doesn't have any reviews.
MS
Mohab S.
Jan 31, 2023
Software Engineering Research
CD
Chris D.
Jan 12, 2023
Psychology / Sociology / Social Sciences Writer for Website (MA or PhD Preferred)
Fast turnaround, followed the instructions, was diligent and made a big effort to meet my expectations. Thanks Juniard for your work!
DC
Dexter C.
Oct 13, 2022
Leadership & Crisis Literature Review
DC
Dexter C.
Jun 2, 2022
Two Literature Reviews
BL
Bliss Tamar L.
Sep 7, 2021
Confirmatory Factor Analysis
Junaid did really good job. He is scrupulous. Besides he is very responsive and good at cooperating. I will ask him help in my projects in the future as well.
About Junaid
NLP, Sentiment and Text Analyst
Larkana, Pakistan - 12:13 am local time
My core expertise includes:
Web scraping and data collection pipelines
Handling and wrangling text data
Feature extraction
Sentiment analysis
Topic modeling
Text classification
Named Entity Recognition (NER)
Social media and customer feedback analysis
Training and Fine tunning Machine Learning and Deep Learning
Transformer-based models (BERT, RoBERTa, DistilBERT)
Fine-tuning models on Hugging Face
Classification, prediction, and recommendation systems
I combine strong academic research expertise using NLP method which can aid value to industry. So, whether you need NLP pipelines, model fine-tuning, sentiment analysis and feature extraction, I can help turn your data into meaningful business value.
Tools & Technologies: Python, TensorFlow, PyTorch, Hugging Face, Scikit-learn, Pandas, NumPy, NLP Libraries, and Data Visualization.
Let's collaborate to build intelligent, transparent, and impactful AI solutions.
Steps for completing your project
After purchasing the project, send requirements so Junaid can start the project.
Delivery time starts when Junaid receives requirements from you.
Junaid works on your project following the steps below.
Revisions may occur after the delivery date.
Explanation Goal
The first step is to understand the explanation goal
Model architecture
The second the step is understand the key architecture of model such as CNN, LSTM, BERT and others and parameter of model