Automatic text summarization with a pre-trained encoder and a transformer decoder (BERT). Provides a web interface for the models using Django
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Updated
Jul 23, 2023 - Python
Automatic text summarization with a pre-trained encoder and a transformer decoder (BERT). Provides a web interface for the models using Django
ML Python - Logistic Regression (GLM)
Production-ready customer churn prediction API built with FastAPI, scikit-learn, and Docker.
An ML API to compute similarity scores between meta information about sentence examples.
An ML API to compute similarity scores between shingled sentence examples.
Production-style ML inference API for credit card fraud detection using FastAPI and XGBoost
ML-API Built using FastAPI for predicting food images and recommending food based on user dietary preferences
Cost-sensitive fraud detection REST API using XGBoost and FastAPI, optimized for financial loss minimization and deployed on Render.
Explainable ML API for loan risk classification using FastAPI, SHAP, and Docker.
Personal Knowledge Base RAG API – FastAPI-based RAG system for querying TXT/PDF documents using embeddings, vector search (FAISS/Qdrant), optional reranking, and Groq LLM inference.
A production-ready Machine Learning API built with FastAPI that predicts cancer risk using a trained Random Forest model with input validation, probability scoring, and prediction logging.
An ML API to compute the Jaccard similarity based on shingled subtrees of the dependency grammar.
API Service for Palomade App that use tensorflow model (.h5) to predict maturity level of palm fruit.
An ML API to compute semantic similarity scores between sentence examples.
This is api for fetch machine learning predict data
This is an ML service that is used to suggest the user weather a server is to be scaled UP or DOWN.
Machine Learning API for predicting credit risk using Scikit-learn
Fraud Detection REST API project built with FastAPI and LightGBM Binary Classifier.
This project is a machine learning-driven API built with FastAPI to predict the survival probability of Titanic passengers. The model is trained with features like age, class, sex, fare, family relations, and port of embarkation.
A time regressor model (ensemble of LightBGM and CatBoost) trained on synthetic dataset to predict future footfall at temple.
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