📊 Matrix Solver & ML Toolkit
Python
NumPy
Matplotlib
Scikit-learn
CLI / Colorama
Project Category: Individual Project
A comprehensive Python-based Command-Line Interface (CLI) interactive toolkit for exploring Linear Algebra, Probabilities, Machine Learning Principles, and Linear Equation Systems (L.E.S.). The tool functions as an intelligent computational assistant to parse, validate, solve, and visualize complex matrix-related problems.
📸 Demos & Visualizations
Main Menu Interface
Interactive Cheatsheets & Learning Resources
Matrix Solvability Analysis & Engine
Matplotlib 2D Model Rendering
Matplotlib 3D Surface Visualization
🛠️ Skills & Capabilities
Matrix Algebra
Deep handling of matrix manipulations (Multiplication, Dot Products, Transposes), Left/Right Pseudo-inverses, Rank computing, and Determinants.
Machine Learning
Implementing Ordinary Least Squares (OLS), Ridge Regression (Primal/Dual with L2 norm), and Multi-Class Classification.
Probabilities Engine
Managing permutations, combinations, conditional probability, Bayes theorem, and disjoint events.
Advanced Parse/Vis
MATLAB-style string parsing to structure data, with dynamic conditional plotting for 2D planes/3D meshes.
🏛️ System Architecture
graph TD
A[Main Menu Interface] --> B[Tool 1: Matrix Math]
A --> C[Tool 2: Matrix Analyser]
A --> D[Tool 3: Regression / L.E.S. Solver]
A --> E[Tool 4: Classifiers & Poly]
A --> F[Tool 5: KNN & Probability]
A --> G[Tool 6: ML Cheat Sheets]
B -.-> B1(Dot Products, Transpose, Math)
C -.-> C1(Determinants, Inv, Rank Nullity)
D -.-> D1(OLS, Ridge Regression Primal/Dual)
E -.-> E1(W-Weights, Polynomal Features)
F -.-> F1(Combinations, Probabilities)
G -.-> G1(Interactive Documentation)
