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

Simplified ETL process in Hadoop using Apache Spark. Has complete ETL pipeline for datalake. SparkSession extensions, DataFrame validation, Column extensions, SQL functions, and DataFrame transformations

Data Engineering Project with Hadoop HDFS and Kafka

Python automation in linux

This project builds a scalable log analytics pipeline use Lambda architecture for real-time and batch processing of NASA server logs.

Machine Learning for Forest Fire Prediction using Hadoop ecosystems and Spark Tools (Pyspark)

Python scripts to assist setting up Hadoop v1 in Linux and starting a NameNode, DataNodes and Client.

Map/Reduce project with Hadoop

Real-Time Meetup RSPV Data Processing using Kafka and Spark

Freelancer Project - Batch processing data pipeline and machine learning application.

Linux-Automation

MapReduce Python Example

This repository contains the necessary scripts for oil production flow prediction models that make use of spark's MLlib

Market Basket Analysis using Hadoop MapReduce in Python

Airline On-time performance Using Hive

This project presents a comprehensive data pipeline designed to predict customer churn using historical customer data. By leveraging Hadoop and PySpark, this pipeline efficiently processes large datasets, performs feature engineering, and trains a machine learning model to identify customers at risk of leaving.

Script for installing a standalone hadoop and spark environment

This ETL pipeline project is a practical demonstration of my skills in data engineering and automation using Python and Apache Airflow. By integrating MySQL for data storage and leveraging Airflow for task orchestration, the project simulates a scalable and modular ETL solution often required in enterprise data workflows.

To mitigate the housing business in a specific region, the proposed model helps to predict the best feasible pricing(real-estate).