herqules/machine-learning-models

A comprehensive suite of Python-based machine learning models for predictive analytics, employing different evolutionary algorithms for data analysis across various topics.

Machine Learning Models

This repository is a curated collection of advanced machine learning models, designed and implemented during my senior year. It includes a range of models suitable for a variety of applications, from straightforward regression analyses to sophisticated deep learning tasks. The models are developed to deliver precision and robustness in real-world scenarios.

Overview

The project focuses on leveraging state-of-the-art algorithms to solve challenging problems in data analysis and prediction. Each model is crafted with attention to data preprocessing, model architecture design, parameter tuning, and validation to ensure the business-level performance.

Contents

• Backpropagation.py: Implements the backpropagation algorithm for neural networks.
• CensusDataSet.py: Processes and prepares census data for analysis.
• DatasetRetrieval.py: Script for downloading and cleaning datasets.
• DifferentialEvolution.py: An optimization algorithm script based on the differential evolution strategy.
• EvolutionaryStrategies.py: Contains implementations of various evolutionary algorithms for optimization.
• ExperimentalAI.py: A testing ground for experimental AI concepts and models.
• MaskedAIErrorChecking.py: Error checking utilities for models using masked AI techniques.
• StockAI.py: Machine learning models specific to stock market prediction.

Installation and Dependencies

To ensure seamless execution of the models, the following dependencies must be installed:

• Python 3.x
• Machine Learning Libraries: scikit-learn, tensorflow, keras, pytorch
• Data Manipulation: numpy, pandas
• Data Visualization: matplotlib, seaborn, tensorboard
• API and Data Retrieval: requests, aiohttp
• Utilities: prettytable, opt-einsum, threadpoolctl, tqdm

Please refer to requirements.txt for a complete list of necessary packages.

Getting Started

1. Clone the repository to your local machine.
2. Install all required dependencies: pip install -r requirements.txt.
3. Navigate to the script corresponding to the model you're interested in.
4. Run the script in your Python environment.