Benedict Tachie

Industrial Store Client-Server Application

• Overview: Developed a client-server system for an industrial store where customers submit shopping lists at a till.
• Key Features:Designed a MySQL database (network_store) with tables for customers, items, and invoices (with auto-incrementing IDs, constraints, and unique fields).
  Built separate server and client applications using socket programming in Python to handle customer registration, item lookup, and invoice generation (with invoices output as text files).
  
• Technologies: Python, MySQL, Socket Programming

Examination Data Analysis & Visualization

• Overview: Captured and analyzed examination data for 150 students (across two exams) to deliver key insights.
• Key Features:Created two CSV datasets incorporating student numbers, age groups, study hours, marks (with calculated percentages), and exam time.
  Cleaned the data by removing zero values and generated frequency tables for study hours, age groups, and marks.
  Developed visualizations including a bar chart (student age groups), a line graph (correlating study hours with marks), and scatter charts (plotting marks vs. exam time and study hours vs. age).
  
• Technologies: Python, CSV, Matplotlib, Data Cleaning & EDA

Loan Qualification Analysis

• Overview: Evaluated algorithm suitability for predicting loan qualification.
• Key Features:Explained the operation of linear regression and why it isn’t ideal for binary classification tasks (such as loan approval).
  Compared parametric (e.g., linear regression) versus non-parametric methods (e.g., decision trees) and justified more appropriate approaches for classification.
  Calculated the mean squared error (MSE) for a sample dataset to illustrate model performance.
  
• Technologies: Python (for calculations and model evaluation)

Student Score Prediction with Linear Regression

• Overview: Implemented a predictive model to forecast student scores based on class attendance.
• Key Features:Created and split the dataset into training and testing sets.
  Trained a linear regression model, evaluated its performance using mean squared error, and visualized actual vs. predicted scores with scatter and line plots.
  
• Technologies: Python, Scikit-learn, Matplotlib

DSS for Automating HR Employee Selection

• Overview: Developed a Decision Support System (DSS) to automate the pre-selection of quality candidates for an IT company.
• Key Features:Generated a dataset from historical HR data (Prek_HR_data.csv) featuring attributes like age, NQF level, and previous experience with quality scores as the target.
  Split the dataset (85% training, 15% testing) and designed a decision tree classifier.
  Visualized the decision tree (using libraries such as graphviz) and evaluated model accuracy on both training and test data.
  Assessed candidate profiles against the trained model to determine eligibility criteria.
  
• Technologies: Python, Scikit-learn, Graphviz, Data Preprocessing

MMORPG Database Design & Implementation

• Overview: Served as the database designer for a breakthrough MMORPG, managing player accounts, characters, error logs, and inventory systems.
• Key Features:Created an ER diagram to map relationships between accounts, characters, and items.
  Developed a relational database with robust constraints (e.g., CHECK constraints, UNIQUE fields) and implemented tables for player accounts (with monthly fee tracking), character profiles (with team and skill levels), and an inventory system allowing item stacking.
  Designed views (e.g., blocked accounts, top skill characters, popular items) and stored procedures (for account registration, time extension, item and character addition, and automated communication).
  Implemented triggers and indexes to ensure data integrity and performance.
  
• Technologies: SQL (MySQL/PostgreSQL), ER Modeling, Stored Procedures, Triggers

Parking Garage Reservation System

• Overview: Designed a comprehensive system to manage parking occupancy and reservations at an office parking garage.
• Key Features:Conducted requirements analysis to identify system actors (e.g., clients, administrators, sensors, licence plate readers) and defined key use cases such as registration, reservation (daily/monthly), and payment processing.
  Developed detailed UML diagrams including use case, activity (with partitions for client, system, and devices), class, communication, and state machine diagrams to model the entire reservation process and account state transitions.
  Proposed system workflows addressing real-time issues like space occupancy, reservation updates, and payment via online platforms and physical devices (e.g., debit machines).
  
• Technologies: UML, Systems Analysis, Requirement Engineering