pynick

NickData Web Application

An End-to-End Machine Learning Web Application for Classification and Regression and clustering problem. Currently supported for csv and excel files. The application relies on these two excellent libraries for machine learning:

Features

  1. Drap and drop file from local system for training.

  2. Simple Data Exploration.

  3. Many Preprocessing methods:
    • Sample and Split
    • Data Preprocessing (Missing Values Imputation, One Hot Encoding, Handle Unknown Levels, Fix Imbalance for Classification)
    • Scale and Transform (Normalization, Transformation, Target Transformation)
    • Feature Engineering (Feature Interaction, Polynomial Features, Trigonometry Features, Group Features, Bin Numeric Features, Combine Rare Levels)
    • Feature Selection (Feature Importance, Remove Multicollinearity, Principal Components Analysis, Ignore Variances)
    • Unsupervised (Create Clusters, Remove Outliers)
  4. Model Training:
    • Compare all available Machine Learning Algorithm automatically.
    • Train a selected single model
    • Train an ensemble model
    • Hyperparameter tuning for single model
  5. Model Result Visualization:
    • All plots for Regression and Classification
    • SHAP Value

  6. Prediction and Save Model:

    • Online Prediction
    • Batch Prediction
    • Save whole Machine Learning Pipeline as pickle file

Install and Run

  1. Clone the repository to you computer: shell script git clone https://github.com/nickjas/pynick cd pynick
  2. Creata a conda virtual or python virtual environment and then activate it. shell script conda create -n myvirtual-name python=3.8 -y conda activate myvirtual-name

  3. Install requirements shell script pip install -r requirements.txt

  4. Run streamlit locally and start web service: shell script streamlit run app.py

Run on Docker

  1. Pull the Docker image: 
    docker pull cican/github-action:latest
  2. Run the Docker image locally: 
    docker run -p 8501:8501 cican/github-action
  3. Open the localhost:8501