/DSAccelerate

Accelerating Analysts' Data Science Development

Primary LanguagePython

DSAccelerate

Accelerating Data Science Development using ChatGPT.

Instructions

  1. Setup DSAccelerate
    1. Download the repository zip file or clone it using the git clone command.
    2. (Recommended) Create a new python environment using conda, venv or any other service.
    3. Run the make download command to set it up.
  2. Create your new project
    1. Provide your specifications in the project_config.json file.
    2. Run the make run command to generate your project.
  3. Setup your new project
    1. (Recommended) Create a new python environment using conda, venv or any other service for your project.
    2. Get into your project directory and run the make requirements command to setup your environments.
    3. Tweak the generated files as per your requirements and resolve any errors if they occur.
    4. Finally, run your project using the command python src/__init__.py

Available ML Algorithms

model_algorithm_name support in PyCaret:

  • Logistic Regression ('lr')
  • K Neighbors Classifier ('knn')
  • Naive Bayes ('nb')
  • Decision Tree Classifier ('dt')
  • Random Forest Classifier ('rf')
  • Extra Trees Classifier ('et')
  • Gradient Boosting Classifier ('gbc')
  • Extreme Gradient Boosting Classifier ('xgboost')
  • Light Gradient Boosting Machine ('lightgbm')
  • CatBoost Classifier ('catboost')
  • AdaBoost Classifier ('ada')
  • Linear Discriminant Analysis ('lda')
  • Quadratic Discriminant Analysis ('qda')
  • Ridge Classifier ('ridge')
  • Ridge Classifier CV ('ridgecv')
  • Passive Aggressive Classifier ('pac')
  • Perceptron ('perceptron')
  • Voting Classifier ('voting')
  • Stacking Classifier ('stacking')