Projects

Developed a machine-learning framework combining supervised and unsupervised methods to detect fraudulent Ethereum accounts with >85% accuracy and <5% false positives, deployed as an interactive Streamlit app.

Built a comprehensive real-time analytics platform processing 10M+ events per day using Kafka, Spark Streaming, and ClickHouse for sub-second query performance.

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.

This repository contains the code and analysis for my data analysis project on stock price analysis and forecasting for my Internal attachment at Jomo Kenyatta University of Agriculture and Technology. The project analyzes historical stock price data, visualizes trends, and develops a forecasting model using Python and data science techniques.

The project focuses on the development and deployment of an ELT (Extract, Load, Transform) pipeline utilizing industry-standard tools such as dbt (data build tool), Snowflake, and Airflow. The pipeline is designed to handle the transformation and loading of data from source tables to final data marts, ensuring efficient data processing.

This Shiny web application analyzes product transactions to discover frequently purchased product pairs and visualize the relationships between them. The app uses association rule mining (Apriori algorithm) to identify frequent itemsets, and it applies community detection to find clusters of related products.

This project demonstrates how to use Apache Kafka Streams to detect fraudulent activities by analyzing IP logs in real-time. By processing the streaming data, the system flags potential fraud by identifying suspicious patterns, such as repeated login attempts or access from unusual IP addresses.

The Retail Recommender System is a Shiny-based web application that provides recommendations for cross-sell opportunities using association rule mining. Built with R, it analyzes customer transaction data, extracts purchasing patterns, and generates rules for cross-sell recommendations.

A Jupyter Notebook repository by Sabareh.

The repo contains a Hillwinds data-engineer take‑home exercise defined in TAKE_HOME_ASSESSMENT (1).md

A code repository by Sabareh.

This is a repository containing the course work that I have done in the course "Technical Writing: How to Write Software Documentation" on Udemy offered by Jordan Stanchev

R machine learning application that performs forecasting on pharmaceutical medicine sales data using information obtained form NHS (UK) General Practitioner (GP) datasets.

A TypeScript repository by Sabareh.

Data Engineering Project using Sales Data in Hadoop using Cloudera

A Python repository by Sabareh.

A Python repository by Sabareh.

A collaborative project on the function printf.

A code repository by Sabareh.

A code repository by Sabareh.

A code repository by Sabareh.

A code repository by Sabareh.

A code repository by Sabareh.

A JavaScript repository by Sabareh.

I'm now a ALX Student, this is my first repository as a full-stack engineer

This is my home 🏡 on the internet. I am trying my best to document my data science journey.

my website

A Jupyter Notebook repository by Sabareh.

A code repository by Sabareh.

A Python repository by Sabareh.

A Python repository by Sabareh.