Machine Learning Projects

This repository contains a collection of machine learning projects organized into different thematic areas.

Repository Description

The main goal of this repository is to provide a centralized reference for various machine learning methods and applications.

Folder Structure

• Anomalies: Projects related to anomaly detection.
• Basico: Implementations of basic machine learning algorithms.
• Classification: Algorithms and examples of classification.
• Cluster: Clustering and cluster analysis projects.
• Dashboard: Dashboards and data visualization tools.
• Dimensionality Reduction: Dimensionality reduction techniques.
• Docker: Docker configuration files.
• End-to-End-catboost: Complete End-to-End-catboost machine learning projects.
• Lector OCR: Projects related to optical character recognition.
• Roadmaps: Roadmaps and study plans for learning machine learning.
• Shap: Projects and examples using SHAP for model interpretability.
• Test: Testing scripts and tools.
• Visualization: Data visualization tools and examples.
• WebScraping: Web scraping projects for data collection.

Documentation

Each folder contains additional README files that explain each method and project in detail, along with usage examples and additional references.

Source Code

The source code is primarily written in Python and is well-documented to facilitate understanding and usage.

Requirements and Dependencies

To run the code in this repository, you'll need to have the following Python libraries installed:

• NumPy
• scikit-learn
• Pandas

You can install these dependencies by running:

!pip install numpy pandas matplotlib tensorflow torch catboost scikit-learn shap streamlit sweetviz
!pip install pandas==1.3.5

Please note that the versions of the libraries may need to be adjusted based on your specific requirements.txt

!pip install -r requirements.txt