🧠 Machine_Learning_Algorithms_GUI — Interactive Workflow in Python

Machine_Learning_Algorithms_GUI is a Python-based MVC application for exploring both supervised and unsupervised machine learning algorithms.
It guides users step by step: dataset upload, preprocessing, learning type selection, algorithm choice, visualization, and comparison.

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🔎 Overview

This project provides a hands-on environment to experiment with machine learning techniques.
The GUI is designed for clarity and pedagogy: each step of the workflow is implemented and visualized interactively.
Built entirely with Python in a local environment, the app follows an MVC architecture for modularity and extensibility.

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🔑 Key highlights:

• Upload .csv datasets directly into the GUI.
• Preprocess data: handle missing values, detect outliers, normalize features.
• Select the learning type:
  • Supervised: KNN, Naive Bayes, C4.5
  • Unsupervised: K-Means, K-Medoids, DIANA, AGNES, DBSCAN
• Choose the algorithm within the selected type.
• Visualize results with scatter plots (partitioning & density), dendrograms (hierarchical), or classification plots (supervised).
• Compare algorithms:
  • Within the same type
  • Across all types
• Evaluate metrics: Silhouette Score, intra/inter-cluster distances, and supervised accuracy metrics.

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🚀 Features

📂 Step 1 — Dataset Upload

• Import .csv files via the GUI.
• Preview dataset structure before processing.

⚙️ Step 2 — Preprocessing

• Analyze and replace missing values.
• Detect and handle outliers.
• Normalize features for consistent scaling.

🔀 Step 3 — Learning Type Selection

• Choose between Supervised or Unsupervised learning.
• Supervised: KNN, Naive Bayes, C4.5.
• Unsupervised: K-Means, K-Medoids, DIANA, AGNES, DBSCAN.

🔬 Step 4 — Algorithm Selection

• Select the specific algorithm within the chosen type.

👁 Step 5 — Visualization

• Scatter plots for partitioning & density algorithms.
• Dendrograms for hierarchical algorithms.
• Classification plots for supervised algorithms.

🆚 Step 6 — Comparison

• Compare algorithms of the same type.
• Compare across supervised and unsupervised algorithms.
• Metrics: Silhouette Score, intra/inter-cluster distances, supervised accuracy & precision.

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🛠️ Technologies Used

• Python (3.x) — core language
• Tkinter — GUI framework
• scikit-learn — supervised & unsupervised algorithms, metrics
• pandas — dataset handling
• numpy — numerical operations
• matplotlib / seaborn — visualization
• MVC architecture — structured application design

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💻 Tech stack

• Python (3.x) — main environment
• scikit-learn — ML algorithms & evaluation
• pandas + numpy — data manipulation
• matplotlib + seaborn — plotting
• Tkinter — GUI interface
• MVC — application architecture

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🚀 Getting Started

1. Clone the repository.
2. Install dependencies:

   git clone your-repo
   python -m venv venv
   venv\Scripts\activate
   pip install -r requirements.txt
   python main.py