Low Resolution Face Detection (A Deep Learning Model)

A machine learning project that implements a predictive modeling pipeline for analyzing structured datasets and generating predictions using regression-based techniques.

The project focuses on data preprocessing, model training, and evaluation, providing a clean workflow that can be reused for experimentation and research.

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Overview

This repository contains an implementation of a predictive modeling system designed to:

• Load and preprocess structured datasets
• Perform feature engineering and data transformation
• Train regression-based machine learning models
• Evaluate model performance using common metrics
• Generate predictions for new input data

The project demonstrates the end‑to‑end lifecycle of building a machine learning model from raw data to prediction.

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Key Features

• Data preprocessing pipeline
• Feature engineering support
• Model training and evaluation
• Easy experimentation with different datasets
• Clean and modular code structure

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Project Structure

LRFD_Model
│
├── data/                # Dataset files
├── notebooks/           # Jupyter notebooks for experimentation
├── src/                 # Core source code
│   ├── preprocessing.py
│   ├── train.py
│   ├── evaluate.py
│
├── models/              # Saved trained models
├── requirements.txt     # Python dependencies
└── README.md

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Installation

Clone the repository:

git clone https://github.com/quantdevv/LRFD_Model.git
cd LRFD_Model

Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

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Usage

Run the training pipeline:

python train.py

Evaluate the model:

python evaluate.py

You can also explore the dataset and experiments using Jupyter notebooks.

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Example Workflow

1. Load dataset\
2. Clean and preprocess data\
3. Train machine learning model\
4. Evaluate model performance\
5. Generate predictions

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Tech Stack

• Python
• NumPy
• Pandas
• Scikit-learn
• Jupyter Notebook

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Future Improvements

• Add hyperparameter tuning
• Implement additional ML algorithms
• Improve visualization and reporting
• Add automated training pipelines

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Contributing

Contributions are welcome. Feel free to open an issue or submit a pull request if you'd like to improve the project.

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License

This project is licensed under the MIT License.