Upenyu Hlangabeza

Upenyu Hlangabeza

Software Development | Machine Learning | Data Analytics | Data Science

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About Me

I'm a graduate of Swansea University with a BSc in Computer Science (Hons). During my studies, I took on various projects, including collaborations with Lonewalker Productions.

I’m all about continuous learning—if there’s something new to master, I’m diving in headfirst. This curiosity led me to explore Python in the realms of Data Science and Data Analytics, with a particular focus on Machine Learning and Artificial Intelligence. My research on Human Skeleton Data Analysis showcased the exciting potential of tech to make a real impact on health and society.

When I'm not deep in code or analyzing data, you’ll find me indulging in my love for anime—yes, I’m that person who can discuss plot twists and character arcs with a bit too much enthusiasm. I’m also a thrill-seeker at heart, always up for an adventure, whether it's trying out a new extreme sport or finding the spiciest dish on the menu. Life’s too short to stay in the comfort zone, and I’m all about making the most of it.

Tech Stack

PHP :
SQL :
Java :
Python :
Laravel :
Django :
Excel :

My Projects

Object Recognition Project Summary on CIFAR-100 Dataset **Problem Overview:** The project tackles the challenge of processing large volumes of image data, emphasizing the need for automated object recognition due to the impracticality of manual methods. **Solution:** Using the CIFAR-100 dataset, the project employs a Convolutional Neural Network (CNN) to automate image classification across 100 categories, aiming to improve object recognition accuracy. **Methodology:** - **Data Handling:** Images were aligned with labels and processed through a CNN model. - **Model Design:** The CNN included convolutional, pooling, and dense layers, using ReLU and softmax for classification. - **Training & Evaluation:** The model was trained over 10 epochs, and its performance was evaluated with a confusion matrix and accuracy score. **Results:** The CNN achieved strong classification accuracy, effectively identifying objects in the dataset. **Conclusion:** The project demonstrates the effectiveness of CNNs for object recognition on the CIFAR-100 dataset, providing a robust solution for image classification.

Object Recognition

Object Recognition

Object Recognition Project Summary on CIFAR-100 Dataset

**Problem Overview:**
The project tackles the challenge of processing large volumes of image data, emphasizing the need for automated object recognition due to the impracticality of manual methods.

**Solution:**
Using the CIFAR-100 dataset, the project employs a Convolutional Neural Network (CNN) to automate image classification across 100 categories, aiming to improve object recognition accuracy.

**Methodology:**
- **Data Handling:** Images were aligned with labels and processed through a CNN model.
- **Model Design:** The CNN included convolutional, pooling, and dense layers, using ReLU and softmax for classification.
- **Training & Evaluation:** The model was trained over 10 epochs, and its performance was evaluated with a confusion matrix and accuracy score.

**Results:**
The CNN achieved strong classification accuracy, effectively identifying objects in the dataset.

**Conclusion:**
The project demonstrates the effectiveness of CNNs for object recognition on the CIFAR-100 dataset, providing a robust solution for image classification.
Project Summary: Evaluating LSTM Networks for Gesture and Pose Analysis **Overview** This project assesses Long Short-Term Memory (LSTM) networks for predicting human gestures and poses, comparing them with Convolutional Neural Networks (CNNs) and traditional Recurrent Neural Networks (RNNs). It uses the KIMORE dataset, which includes 3D skeleton data from 78 subjects performing rehabilitation exercises for low back pain. **Techniques and Methods Implemented** **Data Representation:** Sequential 3D coordinates of human skeletal joints are used for time-series analysis, making LSTM networks particularly suitable. - **Data Processing:** - Normalization: Standardized joint positions to ensure consistency. - Feature Extraction: Extracted joint angles, velocities, and distances for enhanced model input. - Data Augmentation: Applied rotation, scaling, and mirroring to diversify the dataset. **Model Comparison:** LSTM networks were evaluated against CNNs and traditional RNNs to determine the most effective approach for gesture and pose prediction. This study was aimed to identify the best model for automated assessment of rehabilitation exercises.

Human Skeleton 3D Graph Data Analysis

Human Skeleton 3D Graph Data Analysis

Project Summary: Evaluating LSTM Networks for Gesture and Pose Analysis

**Overview**
This project assesses Long Short-Term Memory (LSTM) networks for predicting human gestures and poses, comparing them with Convolutional Neural Networks (CNNs) and traditional Recurrent Neural Networks (RNNs). It uses the KIMORE dataset, which includes 3D skeleton data from 78 subjects performing rehabilitation exercises for low back pain.

**Techniques and Methods Implemented**

**Data Representation:** Sequential 3D coordinates of human skeletal joints are used for time-series analysis, making LSTM networks particularly suitable.

- **Data Processing:**
- Normalization: Standardized joint positions to ensure consistency.
- Feature Extraction: Extracted joint angles, velocities, and distances for enhanced model input.
- Data Augmentation: Applied rotation, scaling, and mirroring to diversify the dataset.

**Model Comparison:** LSTM networks were evaluated against CNNs and traditional RNNs to determine the most effective approach for gesture and pose prediction.

This study was aimed to identify the best model for automated assessment of rehabilitation exercises.
The problem involves designing hydrostatic thrust bearings with the primary goal of minimizing operational power loss when subjected to axial loads. The design parameters affecting power loss include flow rate (Q), recess radius (R0), bearing step radius (R), and fluid viscosity (μ). Hydrostatic bearings employ pressurized fluid to create a thin film separating moving and stationary surfaces, reducing friction and wear while offering high stiffness and load capacity.

Hydrostatic bearing

Hydrostatic bearing

The problem involves designing hydrostatic thrust bearings with the primary goal of minimizing operational power loss when subjected to axial loads. The design parameters affecting power loss include flow rate (Q), recess radius (R0), bearing step radius (R), and fluid viscosity (μ). Hydrostatic bearings employ pressurized fluid to create a thin film separating moving and stationary surfaces, reducing friction and wear while offering high stiffness and load capacity.
I developed a ray tracing engine to demonstrate the capabilities of advanced rendering techniques. The project aimed to create photorealistic images by simulating how light interacts with objects in a scene. I managed to design and develop multiple objects that could interact with the light, focusing on implementing efficient algorithms to trace rays from the camera through each pixel. This involved calculating intersections with surfaces and accurately rendering shadows, reflections, and refractions. This experience deepened my understanding of computer graphics and the principles of light propagation and object visualization.

Ray Tracing

Ray Tracing

I developed a ray tracing engine to demonstrate the capabilities of advanced rendering techniques. The project aimed to create photorealistic images by simulating how light interacts with objects in a scene. I managed to design and develop multiple objects that could interact with the light, focusing on implementing efficient algorithms to trace rays from the camera through each pixel. This involved calculating intersections with surfaces and accurately rendering shadows, reflections, and refractions. This experience deepened my understanding of computer graphics and the principles of light propagation and object visualization.
Building a chat server was intended to demonstrate the capabilities of parallel computing. This project allowed me to create a real-time communication platform where users could interact seamlessly. It served as the backbone for exchanging messages, sharing media, and managing user presence, ensuring smooth and efficient interactions. Through this experience, I enhanced my skills in networking, server management, and application design while showcasing how parallel computing can improve the performance and scalability of applications.

ChatServer App

ChatServer App

Building a chat server was intended to demonstrate the capabilities of parallel computing. This project allowed me to create a real-time communication platform where users could interact seamlessly. It served as the backbone for exchanging messages, sharing media, and managing user presence, ensuring smooth and efficient interactions. Through this experience, I enhanced my skills in networking, server management, and application design while showcasing how parallel computing can improve the performance and scalability of applications.
### Project Aim: Analyzing Mental Health in International Students Using PostgreSQL **Aim**: This SQL-based project investigates how studying abroad impacts international students' mental health, focusing on whether the length of stay influences depression, social connectedness, and acculturative stress. Using PostgreSQL, the project analyzes data from a Japanese university to replicate findings that social connectedness and acculturative stress are key predictors of depression. ### Key SQL Projects in PostgreSQL 1. **Depression and Length of Stay** - **Objective**: Query depression scores (PHQ-9) to assess how they vary with students' length of stay. 2. **Social Connectedness and Stay** - **Objective**: Use SQL to evaluate the relationship between social connectedness (SCS scores) and stay duration. 3. **Acculturative Stress and Stay** - **Objective**: Analyze acculturative stress (ASISS scores) using SQL to see if stress changes with time abroad. 4. **Comprehensive Mental Health Analysis** - **Objective**: Combine all factors (depression, connectedness, stress) in SQL to find key mental health predictors. This project heavily utilizes SQL in PostgreSQL to provide insights into mental health trends among international students.

Telematics-Mental-Health-SQL

Telematics-Mental-Health-SQL

### Project Aim: Analyzing Mental Health in International Students Using PostgreSQL

**Aim**:
This SQL-based project investigates how studying abroad impacts international students' mental health, focusing on whether the length of stay influences depression, social connectedness, and acculturative stress. Using PostgreSQL, the project analyzes data from a Japanese university to replicate findings that social connectedness and acculturative stress are key predictors of depression.

### Key SQL Projects in PostgreSQL

1. **Depression and Length of Stay**
- **Objective**: Query depression scores (PHQ-9) to assess how they vary with students' length of stay.

2. **Social Connectedness and Stay**
- **Objective**: Use SQL to evaluate the relationship between social connectedness (SCS scores) and stay duration.

3. **Acculturative Stress and Stay**
- **Objective**: Analyze acculturative stress (ASISS scores) using SQL to see if stress changes with time abroad.

4. **Comprehensive Mental Health Analysis**
- **Objective**: Combine all factors (depression, connectedness, stress) in SQL to find key mental health predictors.

This project heavily utilizes SQL in PostgreSQL to provide insights into mental health trends among international students.
In this project, I looked at the market share and yearly growth of several treadmill suppliers, including Apex Athletics, Hercules Gear, Spartan Sports, Steel Power, and Summit Strength. I used Excel to analyze data on sales and supplier performance to understand how these companies are doing in the treadmill market. The analysis shows: Market Share Distribution: A clear picture of how much of the market each supplier controls, showing their standing compared to others. Yearly Growth Rates: An overview of how each supplier has grown over the years, helping to identify trends in their performance. Comparative Insights: Key trends and patterns that highlight the strengths and weaknesses of each supplier, which can help businesses understand the market better. Overall, this project provides useful insights for anyone in the fitness equipment industry, allowing them to make smarter decisions in a competitive environment.

Excel MarketShare Insights: A Comparative Analysis

Excel MarketShare Insights: A Comparative Analysis

In this project, I looked at the market share and yearly growth of several treadmill suppliers, including Apex Athletics, Hercules Gear, Spartan Sports, Steel Power, and Summit Strength. I used Excel to analyze data on sales and supplier performance to understand how these companies are doing in the treadmill market.

The analysis shows:

Market Share Distribution: A clear picture of how much of the market each supplier controls, showing their standing compared to others.

Yearly Growth Rates: An overview of how each supplier has grown over the years, helping to identify trends in their performance.

Comparative Insights: Key trends and patterns that highlight the strengths and weaknesses of each supplier, which can help businesses understand the market better.

Overall, this project provides useful insights for anyone in the fitness equipment industry, allowing them to make smarter decisions in a competitive environment.

Certificates

DataCamp

DataCamp

Introduction to Python

Introduction to Python

In this Introduction to Python course, I gained a robust foundation in Python programming tailored for beginners, including those with no prior coding experience. The course provided a comprehensive overview of fundamental concepts such as basic syntax, variable management, and list operations. This foundational knowledge equipped me with the skills to use Python interactively, enabling me to manipulate and analyse data effectively.

In addition to learning Python basics, I explored the extensive ecosystem of libraries and packages that Python offers. The course emphasized how to utilize pre-written code, functions, and methods to streamline development tasks. This aspect of the training enhanced my ability to leverage existing tools and solutions to address various programming challenges efficiently.

The course concluded with an introduction to NumPy, a pivotal library in data science. Through this segment, I acquired practical experience with NumPy arrays and developed initial skills in data handling and analysis. This training has prepared me to apply Python effectively in both data-driven and general programming contexts, making me well-equipped to contribute to projects requiring Python expertise.
Datacamp

Datacamp

AI Fundamentals

AI Fundamentals

In this AI fundamentals course, I learned how to identify various use cases across different AI sub-domains and gained a clear understanding of Generative AI, including its key terminology.

I developed the skills to construct effective prompts for generative AI tools and explored the ethical considerations associated with AI and generative AI solutions. Overall, I now have a solid grasp of both the practical applications and the ethical dimensions of working with AI technologies.
Datacamp

Datacamp

Data Literacy

Data Literacy

**Summary of Data Literacy Course Learnings**

During the Data Literacy course, I acquired a comprehensive understanding of analytics fundamentals, data visualization, and data storytelling. I am proficient in describing the various analytic approaches utilized by data professionals, ranging from summary statistics to complex models and hypothesis testing. This has equipped me with the ability to interpret analysis results effectively and use common analytic terminology with confidence.

In the realm of data visualization, I learned to identify and utilize various visualization types appropriate for different analytical contexts. I am adept at interpreting visualizations to support informed decision-making and understand the potential for visualizations to be used misleadingly. This knowledge ensures that I can both create and critically evaluate visual data representations.

Additionally, the course enhanced my skills in data storytelling. I can now assess the background of different audiences and tailor my communication strategies accordingly. I am capable of identifying the essential components of a compelling data story and integrating them with visualizations to convey insights effectively. This skill set allows me to present data in a manner that is engaging and tailored to the needs of diverse audiences.
Datacamp

Datacamp

Data Analysis in Excel

Data Analysis in Excel

Through the Data Analysis in Excel course, I developed advanced skills in data manipulation and analysis, particularly using PivotTables, logical functions, What-If Analysis, and forecasting tools. I learned how to efficiently extract valuable insights from complex datasets using PivotTables, enabling me to uncover hidden trends and make data-driven decisions. The course also introduced me to keyboard shortcuts, significantly enhancing my workflow efficiency.

In the domain of logical functions, I gained the ability to identify ideal customer profiles and design targeted marketing strategies. This included exploring customer segmentation techniques to optimize customer success, drive revenue growth, and support business expansion. My understanding of these intermediate logical functions has equipped me to make data-informed decisions that align closely with business goals.

The What-If Analysis tool provided me with the expertise to conduct scenario analysis, allowing me to evaluate how different variables impact sales outcomes. By mastering sensitivity analysis, I can now make informed predictions and develop strategies to navigate dynamic business environments effectively.

Finally, the course taught me how to use forecasting techniques to anticipate future trends and visualize this data for stakeholders. I learned to leverage moving averages and trendlines to communicate my findings clearly, ensuring that I can present data-driven insights that inform strategic planning and decision-making processes.
Microsoft

Microsoft

Develop dynamic reports with Microsoft Power BI

Develop dynamic reports with Microsoft Power BI

Power BI Data Analyst Skills

Data Retrieval & Performance Optimization: Proficient in connecting to various data sources (Excel, relational databases, NoSQL) and optimizing data retrieval for efficient reporting.

Data Transformation & Cleaning: Experienced in using Power Query for data cleaning, transformation, and loading, ensuring high data quality for analysis.

Semantic Model Design: Skilled in designing and managing semantic models, including implementing star schemas and optimizing data granularity for better report performance.

Measures & Calculations: Capable of creating and managing both implicit and explicit measures, along with calculated columns and tables, to enhance data modeling.

Report Design & Visualization: Adept at selecting appropriate visuals, designing intuitive report layouts, and applying advanced filtering techniques for user-friendly, data-driven insights.

Workspace & Model Management: Competent in managing workspaces within the Power BI service, distributing reports, scheduling model refreshes, and resolving connectivity issues.
Datacamp

Datacamp

Introduction to SQL

Introduction to SQL

With my recently acquired Introduction to SQL certification, I can contribute to data-driven roles by:

Efficient Data Extraction and Analysis:

Writing precise queries to retrieve specific data from large databases
Supporting timely decision-making with quick, relevant data pulls

Database Performance Optimization:

Creating efficient queries to reduce server load and improve response times
Managing large datasets effectively using techniques like LIMIT clauses

Insightful Reporting:

Combining SQL with visualization tools to create comprehensive reports
Using aliases and views to present data clearly for non-technical stakeholders

Supporting Data-Driven Decisions:

Quickly answering data-related questions from various departments
Identifying connections between different datasets to uncover valuable insights

Cross-Departmental Collaboration:

Working with various database systems due to knowledge of different SQL flavours.
Bridging technical and non-technical teams by translating business questions into queries

Data Integrity Maintenance:

Contributing to database organization and cleanliness
Identifying and correcting data inconsistencies through targeted queries

Task Automation:

Creating views and procedures to automate regular reporting tasks

Database Design Input:

Offering foundational insights into database structure and improvement projects

By leveraging these SQL capabilities alongside my existing skills in data analysis and communication, I can add significant value to data-centric projects and support informed, data-driven decision-making across the organization.

Contact

I'm eager to connect with potential employers.

Let's discuss how I can contribute to your team!

Email Me