Popular GitHub Repositories for Cassandra

PipelineAI

MorphL Community Edition uses big data and machine learning to predict user behaviors in digital products and services with the end goal of increasing KPIs (click-through rates, conversion rates, etc.) through personalization

Learn to create & deploy a deep learning algorithm into a production REST API microservice using Python, Keras, FastAPI, & NoSQL.

Chat with your PDF files using LlamaIndex, Astra DB (Apache Cassandra), and Gradient's open-source models, including LLama2 and Streamlit, all designed for seamless interaction with PDF files.

Credit card fraud detection using kafka,scikit,flask,cassandra

这是一个使用Spark、Hadoop和Cassandra对一部分网页进行索引的项目。

An example of Twitter realtime analysis with Kubernetes, Flink, Kafka, Kafka Connect, Cassandra, Elasticsearch/Kibana, Docker, Sentiment Analysis, Xgboost and Websockets

Explore how to build a Q&A system on PDF File's using AstraDB's Vector DB with Langchain and OpenAI API's

Data Engineering Projects

This is a data pipeline for predicting stock prices using Apache Spark, Apache Cassandra, and machine learning techniques. It collects and preprocesses stock data from Alpha Vantage API, engineers features, trains models, and performs data analysis and predictions.

A Reinforcement learning based program to dynamic display of ad-banners based on the click event by customers. This uses Thompson sampling technique hence ensuring better click through rate.

Comparative Analysis of Unsupervised Learning Methods for Real-time Anomaly Detection in Industrial Control Systems (ICS)

A real-time web app that classifies the fraud from credit-card's transaction data

Udacity's Data Engineering Nanodegree project: Data Modeling with Cassandra

This repository is created for the web development project of Custom PDF ChatBot by METIS, IITGN.

Modern, type-safe Python ORM with dataclass support. Works with PostgreSQL, SQLite, MongoDB, and Cassandra.

LlamaIndex RAG using Cassandra DB powered vectorstore with a knowledge graph

This is an end-to-end ML project, which aims at developing a classification model for the problem of classifying a given customer profile into either of the risk category (Good or Bad). The final classifier used for this project is XGBoost classifier. Deployed in Heroku.

Q&A chatbot, employing LlamaIndex, ASTRA DB (Apache Cassandra), and Gradient's open-source models like LLama2, Gradio, all designed for seamless interaction with YouTube videos

This project combines interactive storytelling with artificial intelligence to create an exciting adventure for players. In this game, you will make choices that impact the outcome of the story.