This project offers a dual approach to understanding e-commerce customer behavior through: Batch data analysis and Real-time data processing.

Have you ever tried to guess the genre of a book by reading its title? Well, in this project, I was trying to do it using a massive database of Books (their titles and genres), MLLib Spark, and the use of three different ML models, including: 1- Support Vector Machine (SVM) 2- Logistic Regression 3- Neural Networks

Explanatory Data Analysis and ML model building using Apache Spark and PySpark

Big Data Engineering studying using a dataset that includes all reported crimes from Chicago, IL from 2001 to present day.

A PySpark Recommendation System for predicting Yelp ratings for 1.5M+ users and 200k+ businesses.

“Word Count Analyzer” project using Apache Spark (PySpark)

My solution to Introduction to Big Data with Apache Spark MOOC at Edx

A dockerized application for performing sentiment analysis, tag recognition, and text summarization on YouTube videos.

The Data-Engineering-Project-Taxi-data involves analyzing and processing taxi data to extract valuable insights for business optimization. It includes building ETL pipelines, data modeling, and real-time analytics.

2019 Canadian Federal Election: Calculating the results using Apache Spark (Databricks notebook in Scala)

Taxi versus Uber in NYC

This respository contains projects made for the Large Scale Data Analysis course at the AGH UST in 2024.

Jupyter notebook portant portant sur l'analyse des bases de données annuelles des accidents corporels de la circulation routière

This project uses a Spark pipeline (PySpark) to analyze the sentiment of user reviews.

Managing distributed databases using Pyspark API of Apache Spark for customer data of an Ecommerce Website and predicting Yearly Amount spent using Linear Regression

The "Olympic Games Analytics Using Apache Spark Databricks" project explores data from the Olympic Games (1896-2016) to identify trends and insights. Using Apache Spark for big data processing and Databricks for visualization, the project analyzes key factors like top-performing countries and athlete attributes, showcasing real-world analytics.

Spark ML pipeline for Seattle housing price prediction with feature engineering and regression modeling.

Distributed processing challenge

A Scala-based contract management app using Apache Spark. It processes contract data in various formats (JSON, CSV, Parquet, ORC, XML) and performs tasks like cost calculation, status determination, and data extraction. This is an educational fork of an existing projet.