Welcome to my project showcase – the digital equivalent of a mad scientist's lab, but hopefully with fewer explosions and more actionable insights! This is where theory gets its hands dirty, where algorithms face real-world data (which is often messier than my desk after a long coding session), and where the goal is always to build something meaningful and effective.
Think of each project as a mini-adventure: a problem to decipher, data to explore, models to train (and retrain... and retrain...), and ultimately, a solution to engineer. From predicting economic shifts to helping plants feel better with AI, it's a diverse collection reflecting my journey. Use the filters to navigate, and click around – exploration is encouraged!
Tasked with gazing into the economic crystal ball (metaphorically, of course!). This project involved wrangling diverse economic indicators – inflation rates doing the cha-cha, fluctuating exchange rates, historical trends – and feeding them into a machine learning model (shoutout to Scikit-learn!) to forecast annual GDP growth. The goal? To provide policymakers with slightly less fuzzy data for their big decisions.
Playing doctor for the plant kingdom! This collaborative capstone project involved training a Convolutional Neural Network (CNN) using TensorFlow and OpenCV to identify common plant diseases from leaf images. It felt like teaching a computer botany. The aim was ambitious: empower farmers with early detection capabilities to safeguard precious crops and contribute to food security. Required lots of image augmentation – plants aren't always cooperative models!
Zooming in on the nation's economic pulse. This project focused specifically on Kenya's GDP, utilizing time-series analysis techniques (ARIMA, Prophet – the usual suspects) to model historical patterns and project future trends. The end product was a tool designed to give stakeholders a clearer, data-informed view for strategic decisions, moving beyond simple trend lines.
An ambitious venture into creating an intelligent assistant for Kenyan investors! The concept involves using neural networks fed with diverse economic indicators, trade data, and regional metrics to identify promising investment opportunities and business ventures. It's currently a work-in-progress, aiming to democratize data-driven investment insights. Still wrestling with some data pipelines here!
Combining my interest in data with... well, watching shows! This analysis used Pandas to dissect historical Netflix stock data, visualizing trends with Matplotlib and Seaborn to understand performance patterns and potential profitability signals for investors. It turns out analyzing stock charts is almost as engaging as a good season finale.
Building on my experience at Kingdom Hospital, this project involved creating a simulation model (using Python libraries like SimPy) to analyze patient flow bottlenecks. By simulating different scenarios (e.g., adding staff, changing appointment systems), we could identify strategies to reduce wait times without needing real-world trial-and-error. It's like playing SimHospital, but with real potential impact!
Exploring the world of Natural Language Processing (NLP). Built a simple web application using Flask and a pre-trained sentiment analysis model (like VADER or TextBlob) to instantly classify text input as positive, negative, or neutral. A fun exercise in deploying a basic ML model and understanding text data nuances. It's surprisingly hard to teach a computer sarcasm!
Drawing from my IT management days, I developed a basic web-based tool (likely using Python with a simple database like SQLite or integrating with Frappe) to track hardware inventory, software licenses, and maintenance schedules. Aimed at small organizations needing a straightforward way to manage their tech resources without complex enterprise software. Keeping track of who has which laptop can be a surprising challenge!
Investigating the age-old question: why do customers leave? This involved analyzing a sample customer dataset using Pandas and visualization libraries to identify key factors correlated with churn. Explored basic classification models (like Logistic Regression) to see if churn could be predicted based on usage patterns and demographics. Understanding customer behavior is key to retention.
You're looking at it! A constantly evolving project to showcase my skills and journey. Built with HTML, Tailwind CSS for rapid styling, and vanilla JavaScript for interactivity (like the particle background, theme toggle, and filtering). Focus on clean design, responsiveness, and adding those little touches of animation and interaction. It's also an exercise in content creation and personal branding!
While crunching numbers and training models is my bread and butter, bringing those insights to life often involves connecting with the wider digital world through APIs and building user-friendly interfaces. Making data useful often means making it accessible! Here are some types of APIs and frontend tools that I find particularly powerful or fascinating for building complete data-driven applications:
The artists' tools for data! Essential for creating interactive, dynamic charts directly in the browser, turning numbers into visual narratives.
Putting data on the map, literally. Indispensable for geospatial analysis and location-based insights visualization.
Tapping into the vast ocean of publicly available information to enrich analyses and build context-aware applications.
Leveraging the power of the cloud giants for tasks like model training, deployment, or accessing sophisticated pre-built AI services.
Unlocking the meaning within text data – sentiment analysis, entity recognition, translation, text generation, the possibilities are huge.
Enabling applications to 'see' – classifying content, detecting objects, analyzing faces, or even processing video streams.
Accessing real-time and historical market data, stock quotes, cryptocurrency prices, or even banking information (with permission!).
The backbone for serving data. Building robust, scalable REST APIs to deliver model predictions or processed data to frontends.
Connecting different services and automating workflows triggered by data events – making systems work smarter, not harder.
Tools for building the actual user interface, making the data and interactions intuitive and engaging for the end-user.
Exploring these projects might just spark an idea for your own data challenge! Whether you've got a complex dataset that needs taming, a process begging for automation, or a prediction you'd love to make reality, I'm always eager to discuss potential collaborations and new adventures in data.
