JobSlayerML: Because Engineers Shouldn't Settle for Job Security!

   

Welcome to JobSlayerML, a fun and tongue-in-cheek automated machine learning tool created by a engineer who believe in pushing the boundaries of technology, even if it means potentially "slaying" my own job!

Table of Contents

1. Introduction
2. Getting Started
3. Features
4. Usage
5. Contributing
6. License

Introduction

JobSlayerML is a Streamlit-based application designed to make machine learning model selection and evaluation a breeze. It supports both regression and classification tasks, and it allows you to select from a range of machine learning models. It also provides options for performance evaluation, such as Mean Squared Error, Mean Absolute Error, Accuracy, Precision, Recall, F1 Score, F2 Score, or ROC AUC. Finally, it offers a comparative study of multiple models for quick analysis.

Getting Started

To get started with JobSlayerML, follow these simple steps:

1. Clone the GitHub repository:

   git clone https://github.com/jaywyawhare/JobSlayerML.git

2. Install the necessary libraries by running:

   pip install -r requirements.txt

3. Run the Streamlit app using

   streamlit run app.py

4. Access the application locally at http://localhost:8501 in your browser or on streamlit sharing at JobSlayerML.

Features

• Supports both regression and classification tasks.
• Handles missing data through various imputation methods.
• Encodes categorical variables using Label Encoding or One-Hot Encoding.
• Allows you to select from a range of machine learning models.
• Provides options for performance evaluation, such as Mean Squared Error, Mean Absolute Error, Accuracy, Precision, Recall, F1 Score, F2 Score, or ROC AUC.
• Offers a comparative study of multiple models for quick analysis.

Usage

JobSlayerML is designed to simplify the process of machine learning model selection and evaluation, and also to provide a comparative study of multiple models for quick analysis. It can be used for both regression and classification tasks.

Contributing

Contributions to JobSlayerML are welcome! If you have suggestions for improvements or new features, please feel free to open an issue or submit a pull request on the GitHub repository.

Scope for improvement:

• Add feature engineering options.
• Adding hyperparameter tuning options.
• Adding prediction options.
• Adding support to neural networks using NeuralBuilder
• Adding support for time series analysis.
• Adding support for natural language processing.
• Adding support for image processing.

GitHub Repository: JobSlayerML on GitHub

License

This project is licensed under the MIT License - see the LICENSE file for details.

Enjoy using JobSlayerML, and remember, its AGI is closer than you think!