This repository serves as a comprehensive guide and resource hub for the complete lifecycle of a data science project. It aims to assist aspiring data scientists and professionals by providing detailed information and links to tools and techniques across all stages, from business understanding to model deployment.

How It Works

The repository is structured to cover each phase of a data science project, including data collection, cleaning, feature engineering, EDA, model selection, training, evaluation, and deployment. It acts as a curated collection of resources, offering links to relevant libraries, articles, tutorials, and best practices for each step.

Quick Start & Requirements

This repository is a collection of links and information, not a runnable software package. No installation or specific requirements are needed to access the content.

Highlighted Details

• Extensive coverage of data collection methods, including web scraping, APIs, and dataset repositories.
• Detailed sections on data cleaning, feature engineering, and handling missing values, with numerous library recommendations.
• A vast overview of machine learning algorithms, deep learning architectures, and their applications.
• Comprehensive guidance on model deployment, MLOps, and related tools.

Maintenance & Community

The repository appears to be a personal project, with the primary contributor being "achuthasubhash." There is no explicit mention of a community or ongoing maintenance beyond the initial creation.

Licensing & Compatibility

The repository does not specify a license. It is a collection of links to external resources, and compatibility would depend on the licenses of those individual resources.

Limitations & Caveats

The repository is a highly detailed but unorganized collection of links. It lacks a clear structure for programmatic use or direct integration, and the sheer volume of information may be overwhelming without a more curated approach.