THESE PROJECTS DEMONSTRATE MY PROFICIENCY IN PYTHON, INCLUDING MY ABILITY TO WORK WITH COMPLEX DATA SETS.
THEY SHOWCASE MY SKILLS IN DATA EXPLORATION, STATISTICS, AND DATA VISUALIZATION, EMPLOYING NUMPY, SCIPY, PANDAS, SEABORN, AND MATPLOTLIB TO ACCOMPLISH THESE TASKS.
Analysis of the impact of 'Internet buzz' on movie box office revenues
This project is based on the undergraduate honors thesis by Versaci (2009) that investigated the concept of "Internet buzz variables". The goal is to determine whether these buzz variables offer any additional predictive value for a movie's box office revenues, beyond traditional movie characteristics such as genre, actors, and budget.
Market-Basket Analysis
This project contains an implementation of a market basket analysis for a store focusing on items from their Stationary and Health & Beauty Aids departments. Using the provided transaction data, the project explores the purchasing relationships of seventeen products over a three-month period.
Bank Customer Segmentation for Promotions
This project aims to identify categories of customers for potential future promotions for a bank in the U.K. The data used for analysis is collected on 600 customers.
Customer Response Prediction
A machine learning project aimed at predicting customer responses to catalog mailers, aiding a direct marketing firm in identifying and targeting profitable customers.
Starcraft Rank Prediction
A machine learning model to predict a player’s rank on a dataset of Starcraft player performance in ranked games.
ETL Pipeline with AWS Redshift and Apache Airflow
A project to load song data from S3, process the data into analytics tables using Spark, and load them back into S3 as a set of dimensional tables in order to allow the analytics team to draw insights in what songs users are listening to. Also, automate the loading of output S3 dimensional tables to Redshift using Airflow DAGs.