Autonomous AI Predictive Analytics Platform
react machine-learning predictive-analytics data-profiling fastapi drift-monitoring shap-explainability
-
Updated
Jun 17, 2026 - Python
#
shap-explainability
Here are 13 public repositories matching this topic...
Language: All
Filter by language
Sort: Most stars
Sort options
End-to-end customer churn prediction project using the Telco dataset. Includes EDA, data preprocessing, Logistic Regression / Random Forest / XGBoost model comparison, SHAP explainability, and a production-ready prediction pipeline.
python machine-learning random-forest scikit-learn xgboost data-analysis logistic-regression customer-churn shap-explainability
-
Updated
Jan 6, 2026 - Jupyter Notebook
This project employs Logistic Regression for binary classification, to predict whether a borrower is capable of repaying a loan based on various financial and demographic factors.
-
Updated
Apr 24, 2026 - Jupyter Notebook
MultiModal Disaster Response System — BERT + ResNet-50 fusion for real-time disaster intelligence
nlp ai computer-vision deep-learning humanitarian bert resnet-50 multimodal multimodal-deep-learning shap-explainability
-
Updated
Mar 7, 2026 - Python
ML-powered crop yield prediction for Maharashtra — Gradient Boosting (R2=0.977), SHAP explainability, MLflow experiment tracking, LLM-powered AI explanations, and interactive Streamlit dashboard.
python data-science machine-learning agriculture scikit-learn india feature-engineering maharashtra gradient-boosting explainable-ai mlflow ml-pipeline crop-yield-prediction streamlit shap-explainability
-
Updated
May 22, 2026 - Python
Spatio-temporal graph deep learning for IPL T20 match outcome prediction. GAT player-interaction graph + BiLSTM + cross-attention Transformer. Ball-by-ball win probability, run forecasting & Player Impact Score with SHAP explainability.
python iot data-science deep-learning pytorch cricket data-analytics ipl spatio-temporal bilstm sports-analytics match-prediction optuna graph-attention-network t20 cross-attention shap-explainability player-impact-score
-
Updated
Apr 21, 2026 - Jupyter Notebook
Real-time PPG-based atrial fibrillation detection using Random Forest with feature-level explainability and interactive visualization.
-
Updated
Apr 9, 2026 - Jupyter Notebook
LangGraph-based agent pipeline for code evaluation using static analysis, FAISS-powered RAG, and LLMs with SHAP-style explainability.
-
Updated
May 1, 2026 - Python
The AI Loan Analyst is a sophisticated Streamlit-based web application designed to automate and enhance the loan analysis process for financial institutions. It combines data science, machine learning, and financial modeling to provide a complete loan portfolio management solution.
python data-science machine-learning exploratory-data-analysis machine-learning-algorithms eda data-analytics xgboost lightgbm data-cleaning roi-analysis streamlit ai-analysis loss-given-default shap-explainability
-
Updated
Mar 11, 2026 - Python
I built this application to allow users to input various clinical parameters and receive an instant prediction of whether a patient is likely to have CKD. The app is hosted on Streamlit community cloud for public access.
-
Updated
Aug 17, 2025 - Jupyter Notebook
Built a machine learning model to predict telecom customer churn using classification techniques and SHAP explainability. Optimized performance through tuning and translated results into actionable customer retention insights.
python numpy scikit-learn cross-validation pandas xgboost logistic-regression feature-engineering predictive-modeling data-cleaning hyperparameter-tuning model-evaluation data-storytelling shap-explainability threshold-optimization
-
Updated
Apr 21, 2026 - Jupyter Notebook
ML-based beta thalassemia carrier screening from CBC parameters for Sri Lankan primary care. SVM + SHAP explainability, MOH referral letters, couple screening, voice input, and analytics dashboard.
react nodejs python machine-learning mongodb svm scikit-learn clinical-decision-support fast-api thalassemia primary-health-care shap-explainability
-
Updated
May 15, 2026 - JavaScript
FraudDetectAI is an advanced credit card fraud detection system built with XGBoost and Hybrid SMOTE Sampling (Oversampling + Undersampling). This project tackles highly imbalanced datasets, ensuring strong fraud detection accuracy while minimizing overfitting risks.
data-science credit-card-fraud cybersecurity feature-engineering hyperparameter-tuning imbalanced-data smote fraud-detection classification-models optuna xgboost-classifier financial-security shap-explainability overfitting-analysis
-
Updated
Jun 5, 2026 - Jupyter Notebook
Improve this page
Add a description, image, and links to the shap-explainability topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the shap-explainability topic, visit your repo's landing page and select "manage topics."