Sean Atkinson's Projects
In this mini-project, I engage in solving practice problems related to probabilities before transitioning to explore various statistical distributions.
This mini-project applies Bayesian statistics to baseball data, using prior information and observed statistics to create posterior probabilities.
This mini-project is an attempt to predict chronic kidney disease in patients using classification modeling, employing statistical techniques to identify risk factors and forecast the likelihood of the disease.
This hackathon project aims to use a Convolutional Neural Network (CNN) to accurately identify promoter classes for DNA sequencing, leveraging spatial pattern recognition to enhance genetic analysis.
This mini-project involves clustering data using the unsupervised k-means algorithm, grouping similar data points together to uncover patterns and insights.
This mini-project involves classifying clothing images using a Convolutional Neural Network (CNN), employing spatial hierarchies and filters to identify and categorize various types of apparel.
This mini-project entails end-to-end work on a data science problem, utilizing various classification models to determine whether psychological factors can contribute to predicting left-handedness in individuals.
This Conversational AI project aims to develop a chatbot capable of open and dynamic conversation, utilizing a knowledge-grounded human-to-human conversation dataset to create engaging and responsive dialogues.
A mini-project tackling a series of correlated data problems, analyzing the interdependencies between variables to derive meaningful insights.
This project conducts an analysis of an e-commerce retailer, examining sales trends, marketing channels, and operational efficiency metrics to pinpoint opportunities for growth and optimization.
This mini-project utilizes a Feedforward Neural Network to accurately identify digits from a dataset comprising tens of thousands of handwritten images, demonstrating the model's capability in pattern recognition.
This mini-project involves conducting linear regression analysis, utilizing Sacramento real estate data, to identify relationships and trends between variables within the housing market.
This initiative focuses on predicting housing prices using regression modeling, creating a regression model specifically designed to provide millennials with an easy-to-use tool to estimate the appropriate cost of a house.
In this mini-project, I engage in statistical modelling and model validation with Citi Bike data, utilizing mathematical methods to discern patterns and forecast trends.
This mini-project explores the use of Natural Language Processing (NLP) and the Naive Bayes algorithm to analyze Facebook statuses, investigating whether they can be predictive of a person's agreeableness, in the context of data practices like those employed by Cambridge Analytica.
This mini-project applies Principal Component Analysis (PCA) to speed dating data, reducing dimensionality to identify key factors influencing dating preferences and outcomes.
In this mini-project, I tackle a series of probability problems, diving into mathematical challenges to unravel solutions for different probabilistic situations.
This mini-project consists of a series of Python coding challenges that become increasingly difficult, testing and enhancing programming skills through progressive complexity.
This mini-project focuses on text classification using a Recurrent Neural Network (RNN), applying sequential learning to categorize text into predefined classes or groups.
This analysis evaluates a tech-forward insurance company's Wellness Reimbursement Program, concentrating on providing recommendations for product and marketing teams and addressing ad-hoc questions to optimize program effectiveness.
This mini-project involves the design and implementation of predictive models using Apache Spark, leveraging its distributed computing capabilities for efficient data processing and analysis.
My personal repository
This project conducts an analysis to determine whether the state of California should do away with standardized testing, evaluating the potential benefits and drawbacks through a comprehensive examination of educational practices and outcomes.
In this mini-project, I explore statistical distributions by having fun with loot boxes, analyzing the probabilities and patterns that govern their behaviour and outcomes.
This mini-project involves experimenting with a variety of classification and regression models, exploring different techniques to understand their behaviors and applications in predictive analytics.
This mini-project focuses on forecasting Walmart store sales using time series analysis, leveraging historical data to predict future sales trends.
This mini-project focuses on exploratory data analysis using the Titanic manifest, delving into the details and characteristics of the data to uncover insights and patterns related to the historic voyage.
This project involves collecting Reddit submission posts from r/LiverpoolFC and r/Everton using Pushshift's API, and employing Natural Language Processing (NLP) to train models that can accurately determine the origin of the posts, differentiating between the two football club subreddits.
This mini-project involves web scraping using Beautiful Soup, extracting and manipulating data from websites to gather specific information for analysis or utilization.