ml_techniques

This repository showcases techniques and best practices for dataset preprocessing, machine learning algorithms, and model training. It is organized with a clean, logical directory structure for maximum clarity and reproducibility.

---

Directory Structure

src/
├── app.py
├── datasets
│   ├── adult.csv
│   ├── enriched_student_academic_performance_dataset.csv
│   ├── preprocessed_SAP_ds.csv
│   └── processed_adult.csv
├── models
│   ├── ds1
│   │   ├── best_model_random_forest.pkl
│   │   ├── label_encoder.pkl
│   │   ├── minmax_scaler.pkl
│   │   ├── standard_scaler.pkl
│   │   └── top_features.pkl
│   └── ds2
│       ├── daves_bouldin_model.pkl
│       ├── label_encoder.pkl
│       └── top_features.pkl
├── processing
│   ├── ds1
│   │   ├── ds1_pre-processing.ipynb
│   │   ├── prediction.txt
│   │   └── sample.py
│   ├── ds2
│   │   ├── ds2_preprocessing.ipynb
│   │   └── ds2_Student_Academic_Performance_Report
│   └── Feature Extraction
│       └── feature_extraction.ipynb
├── requirements.txt
├── templates
│   ├── index.html
│   └── script.js
└── utils.py

---

Project Overview

• ds1 (Adult Income Dataset):
  Processing notebooks and scripts for the UCI Adult Income dataset are found in src/processing/ds1/ds1_pre-processing.ipynb.
  Outputs: processed datasets and trained models stored in src/datasets/ and src/models/ds1/.

• ds2 (Student Academic Performance Dataset):
  Processing and analysis for student academic performance in src/processing/ds2/ds2_preprocessing.ipynb.
  Outputs: processed datasets and models in src/datasets/ and src/models/ds2/.

• Feature Extraction (Wine Dataset):
  Demonstrates advanced feature extraction using an autoencoder on the Wine dataset in src/processing/Feature Extraction/feature_extraction.ipynb.

---

How to Run

1. Install Requirements

   pip install -r src/requirements.txt
   

2. Process Data & Train Models
   Run the relevant notebook(s) in the src/processing/ folders (ds1, ds2, or Feature Extraction) to generate processed datasets and trained models.

3. Start the Application

   cd src
   python app.py
   

   The web UI will be accessible at http://127.0.0.1:{port} (default port as defined in app.py).

---

Notes

• The templates/ folder contains the UI files (index.html, script.js) for the web interface.
• Utility functions are defined in src/utils.py.
• All data and model artifacts are stored in the respective datasets/ and models/ subfolders.
• For best results, follow the directory structure and execution order as described above.

---

Datasets Used

• Adult Income Dataset (UCI)
• Student Academic Performance Dataset
• Wine Dataset (scikit-learn)

---

Contributions

Feel free to fork, open issues, or submit pull requests to enhance functionality or add new ML techniques!

---