sean-atkinson Goto Github PK

followers: 9.0 following: 9.0 repos: 29.0 gists: 5.0

Name: Sean Atkinson

Type: User

Bio: Data analyst with 12 years of experience in sales and marketing. Fuelled by tenacity and a passion for simplifying the complex.

Location: Toronto, Ontario, Canada

Hi, I'm Sean!

⚡ A little about me:
- Toronto is home.
- The furthest city from Toronto in the world? That’s where my wife was born.
- I'm a Liverpool FC supporter.
- I love Formula 1.
- I'm a runner.
- I think gloomy weather is the best weather.
- I find great joy in the seemingly illogical and nonsensical.
- My two-year-old likes to test my devotion to her on a daily basis by placing herself in increasingly dangerous situations.

Some of the languages and tools I use:

You can find me here:

Sean Atkinson's Projects

bayes_theorem

In this mini-project, I engage in solving practice problems related to probabilities before transitioning to explore various statistical distributions.

bayesian_modeling

This mini-project applies Bayesian statistics to baseball data, using prior information and observed statistics to create posterior probabilities.

classification_model_validation

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.

classifying_dna_sequences

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.

clustering_kmeans

This mini-project involves clustering data using the unsupervised k-means algorithm, grouping similar data points together to uncover patterns and insights.

cnn

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.

comparing_classification_models

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.

conversational_ai

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.

correlated_data

A mini-project tackling a series of correlated data problems, analyzing the interdependencies between variables to derive meaningful insights.

elist_ecommerce_analysis

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.

fnn

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.

linear_regression_sacramento

This mini-project involves conducting linear regression analysis, utilizing Sacramento real estate data, to identify relationships and trends between variables within the housing market.

machine_learning_real_estate_forecasting

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.

model_validation_citi_bike

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.

nlp_naive_bayes

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.

pca

This mini-project applies Principal Component Analysis (PCA) to speed dating data, reducing dimensionality to identify key factors influencing dating preferences and outcomes.

probability_challenges

In this mini-project, I tackle a series of probability problems, diving into mathematical challenges to unravel solutions for different probabilistic situations.

python_ladder

This mini-project consists of a series of Python coding challenges that become increasingly difficult, testing and enhancing programming skills through progressive complexity.

rnn

This mini-project focuses on text classification using a Recurrent Neural Network (RNN), applying sequential learning to categorize text into predefined classes or groups.

row_health_wellness_program_analysis

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.

scala_apache_spark

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.

sean-atkinson

My personal repository

standardized_test_analysis

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.

statistical_distributions

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.

supervised_learning_models

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.

time_series

This mini-project focuses on forecasting Walmart store sales using time series analysis, leveraging historical data to predict future sales trends.

titanic_eda

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.

web_apis_and_nlp

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.

web_scraping

This mini-project involves web scraping using Beautiful Soup, extracting and manipulating data from websites to gather specific information for analysis or utilization.

sean-atkinson Goto Github PK

Hi, I'm Sean!

Sean Atkinson's Projects

Recommend Projects

Recommend Topics

Recommend Org