Projects | Scott Miner

Job Grove

Tue, 11 Mar 2025 00:00:00 +0000

Job Grove is a job board and aggregator that pulls job postings into a single, searchable feed. The front end is built with Vue.js and deployed on Netlify.

Features

Real-time job posts: new listings appear in the feed as they are published.
Aggregation: postings from multiple sources are normalized into one consistent card layout.
Accounts: visitors can sign up to save their place and personalize the experience.
Responsive UI: the layout adapts from phone to desktop.

What it demonstrates

Building a responsive single-page application in Vue.js, consuming external APIs, managing client-side state, and shipping a static front end to a CDN.

to try it out.

Jackets Store

Tue, 11 Mar 2025 00:00:00 +0000

Jackets Store is a full-stack e-commerce proof of concept. A Vue.js storefront talks to a Django REST API that handles products, carts, orders, and user accounts.

Features

Product catalog with product detail pages.
Shopping cart and checkout flow from browsing to order confirmation.
User accounts for sign-up, login, and order history.
Payments as donations: the checkout processes a payment flow for demonstration purposes only. No physical items are shipped.

What it demonstrates

Designing a REST API with Django REST Framework, authenticating users between a decoupled front end and back end, and modeling the core entities of an online store.

to walk through the store.

Dev Blog (ASP.NET MVC)

Tue, 11 Mar 2025 00:00:00 +0000

A developer blog I built from scratch on ASP.NET MVC. A RESTful C# backend manages posts and image uploads, and the application is deployed to Azure App Service.

Features

Post management: create, edit, and publish articles with a clean URL handle per post.
Image uploads handled server-side through a REST endpoint.
Server-rendered views with ASP.NET MVC for fast, indexable pages.
Cloud hosting on Azure App Service.

What it demonstrates

Server-side .NET skills: MVC architecture, REST API design in C#, file handling, and deploying a .NET web app to Azure. The blog is also where I write about ASP.NET Core topics such as setting up a development environment.

to read the latest posts.

eCommerce Microservices

Fri, 23 May 2025 00:00:00 +0000

An e-commerce platform decomposed into independently deployable microservices. Each service lives in its own repository (wired in as Git submodules), owns its data store, and communicates through an API gateway and a message broker.

Architecture

Service	Responsibility	Storage & messaging
Products	CRUD API for the catalog with business-logic and data-access layers (EF Core) and xUnit tests	MySQL, publishes events to RabbitMQ
Orders	Order creation and querying, RabbitMQ consumers, Ocelot API gateway for cross-service calls	MongoDB, Redis cache
Users	Authentication and user retrieval with a Dapper data layer	PostgreSQL
Frontend	Angular 17 single-page app that talks to the services through the gateway	—

Deployment

docker compose -f docker-compose-build.yaml up --build builds and starts the whole stack with seeded databases for local development.
Kubernetes manifests under aks/ run every service in Azure Kubernetes Service or any other cluster.

What it demonstrates

Service boundaries and polyglot persistence, asynchronous communication with RabbitMQ, API gateway patterns with Ocelot, containerization, and orchestrating a multi-service system with Docker Compose and Kubernetes.

Resume Creator Word Add-in

Sat, 22 Feb 2025 00:00:00 +0000

Applying for jobs means producing many near-identical documents. Resume Creator turns that into a Ribbon button inside Microsoft Word.

What it does

Database-backed content: companies, master experiences, per-role bullet points, qualifications, core competencies, and every job application live in a SQL Server database, so a resume is assembled from data rather than copied from the last one.
Template-driven output: a Word template (.dotm) is filled with the selected role, focus areas, and bullets; a matching cover-letter template reuses the same application record.
AI-assisted drafting: forms call the OpenAI API with per-request settings (model, temperature, length) to propose bullet points, competencies, key interests, and hidden keywords that are then edited by hand.
Application tracking: recruiters, addresses, salary ranges, work arrangement, and status are recorded per application, with saved queries for recent activity.

Engineering

The solution is split into a presentation layer (the VSTO add-in), document services, validations, and contract projects, with xUnit and Moq tests and a SQL database project with post-deployment seed scripts. Secrets are stored encrypted outside the repository. The code is private because it holds personal job-search data; ask me about it if you’re curious.

IMDb Sentiment Classifier

Thu, 07 Nov 2024 00:00:00 +0000

A sentiment analysis model trained on the IMDb Movie Reviews dataset to classify reviews as positive or negative.

Approach

Preprocessing: tokenization, vocabulary building, and sequence padding of raw review text.
Embeddings: pre-trained GloVe word vectors give the network a head start on word meaning.
Model: a bidirectional LSTM reads each review in both directions, with batch normalization and dropout-style regularization to improve accuracy and generalization.
Training and evaluation: scripted training loop with held-out evaluation and metrics reporting.

What it demonstrates

End-to-end NLP with TensorFlow and Keras: text preprocessing, transfer learning with embeddings, recurrent architectures, and disciplined model evaluation.

Word2Vec Skip-Gram in TensorFlow 2

Tue, 11 Apr 2023 00:00:00 +0000

An implementation of the Word2Vec skip-gram algorithm in TensorFlow 2.0 that learns vector representations of words from a slice of Wikipedia (the text8 dataset).

How it works

The text8 corpus is downloaded (a helper script fetches it on demand) and processed into a vocabulary, dropping rare words below a frequency threshold.
A skip-gram model is trained with stochastic gradient descent and noise-contrastive estimation (NCE) loss.
During training the model is evaluated by finding the nearest neighbors of a set of test words using cosine similarity between their embeddings.

The implementation follows the approach described in Mikolov et al., Efficient Estimation of Word Representations in Vector Space (2013), and builds on Aymeric Damien’s TensorFlow examples.

What it demonstrates

Working knowledge of word embeddings, negative-sampling style objectives, and writing training and evaluation loops directly against the TensorFlow 2 API.

FollowEqualizer

Sat, 14 Sep 2024 00:00:00 +0000

FollowEqualizer is a GitHub management tool with a desktop GUI. It helps you tidy up who you follow and what you have starred without clicking through the GitHub website one profile at a time.

Features

Find non-followers (accounts you follow that don’t follow you back) and unfollow them in bulk.
Follow back users who follow you.
Find and unstar repositories and starred topics.
Exceptions list to protect specific users or repositories from any action.
Comma-separated search that highlights matches across every list.
Multithreaded operations keep the interface responsive during long-running API calls, and a cache speeds up repeated operations.

What it demonstrates

Building a responsive desktop application in PyQt5, working with a paginated REST API and rate limits, and using worker threads to keep a GUI responsive.

EnhancedTextWriter COM Library

Thu, 22 Aug 2024 00:00:00 +0000

EnhancedTextWriterCOM shows how to extend Microsoft Office with .NET: a C# class library is exposed as a COM object, registered with regasm, and then called from VBA inside a Word document.

What’s in the repository

EnhancedTextWriterLib (C#): the COM-visible class with a SaveToFile method that writes text from a VBA form to disk.
EnhancedTextWriter.docm: a macro-enabled Word document with a form that calls the COM object.
VBA/EnhancedTextWriterVBA.bas: the VBA module demonstrating both binding styles.

Early vs. late binding

Early binding references the type library directly, giving IntelliSense and compile-time type checking.
Late binding creates the object at runtime with CreateObject, trading tooling support for deployment flexibility.

The step-by-step walkthrough, including project settings, assembly signing, and registration troubleshooting, is in the .

Universal EDA

Sat, 15 Apr 2023 00:00:00 +0000

Universal-EDA provides an easy-to-use interface for exploratory data analysis (EDA) and data loading so that the first hour with a new dataset is spent looking at the data rather than writing boilerplate.

Background

The project started while updating an older linear regression project and discovering that the Boston Housing dataset had been removed from newer versions of scikit-learn. Universal-EDA keeps classic teaching datasets available and wraps the repetitive first steps of any analysis.

Features

DataExplorer: summary statistics, missing-value reports, and quick visualizations for any DataFrame.
DataLoader: loads common datasets from scikit-learn and converts DataFrames to CSV files.

from src import DataExplorer, DataLoader

df = DataLoader().load_toy_dataset('boston')
DataExplorer(df).basic_info()

What it demonstrates

Packaging reusable analysis code into clean classes, and designing small APIs that make day-to-day data work faster.

Sequence Prediction ANN

Thu, 27 Apr 2023 00:00:00 +0000

A compact artificial neural network that learns to predict the next value in a numeric sequence, a small-scale version of the time-series forecasting problem.

Design

The code is deliberately modular so each concern can be swapped or tested on its own:

Data preparation: turns raw sequences into supervised learning windows and splits train and test sets.
Architecture: defines the layers and activation functions of the network.
Training: runs the optimization loop and reports evaluation metrics.

What it demonstrates

The fundamentals of supervised sequence learning, clean separation of concerns in machine learning code, and model evaluation with scikit-learn utilities.

Healthcare Expert System

Mon, 19 Jun 2023 00:00:00 +0000

A rule-based expert system that models the decision logic used in heart failure telemonitoring. Given patient-reported measurements and symptoms, the system applies clinical rules to suggest self-care actions and flag situations that need a clinician’s attention.

How it works

Domain knowledge is encoded as rules in Pyke, a Python knowledge engine.
Patient facts (weight changes, blood pressure, symptoms) are asserted into the knowledge base.
The inference engine fires matching rules to produce recommendations and alerts.

What it demonstrates

Symbolic AI and knowledge representation as a complement to statistical machine learning, and translating a real clinical workflow into explicit, auditable rules.

LeetCode Practice in C#

Thu, 19 Jun 2025 00:00:00 +0000

A growing collection of algorithm and data-structure solutions written the way production code should be: documented, tested, and measured.

Structure

Project	Purpose
`Problems`	Solutions with XML documentation, often with more than one approach per problem
`Tests`	xUnit tests covering every solution (`dotnet test`)
`Runner`	Console app for ad-hoc experiments
`Benchmarks`	BenchmarkDotNet comparisons of alternative implementations

Problems covered so far focus on arrays and strings, including Two Sum, Remove Duplicates from Sorted Array, Merge Sorted Array, Product of Array Except Self, and Increasing Triplet Subsequence.

What it demonstrates

Algorithmic problem solving in C#, test-first habits, and using benchmarks rather than intuition to compare approaches.