README.md

metadata

license: mit
title: AutoML
sdk: streamlit
emoji: 💻
colorFrom: yellow
colorTo: green
sdk_version: 1.45.1

AutoML & Explainability Web Application

This Streamlit web application empowers users to perform end-to-end machine learning tasks with ease. Upload your data, automatically train and compare various models, understand their predictions through SHAP explainability, and export the best model for your needs.

🎯 Core Objectives

• Accessibility: Enable users of all technical backgrounds to leverage machine learning.
• Automation: Streamline the ML pipeline from data ingestion to model evaluation.
• Transparency: Provide clear insights into model behavior using SHAP.
• Efficiency: Quickly identify the best-performing model for a given dataset.

✨ Key Features

• Flexible Data Upload:
  • Supports .csv and .xlsx files.
  • Option to upload a single file (for automatic train/test splitting) or separate training and testing files.
• Data Preprocessing:
  • Automatic handling of missing values (imputation).
  • Encoding of categorical features.
  • Optional scaling of numeric features.
• Target Column & Problem Type Detection:
  • Easy selection of the target variable.
  • Automatic detection of problem type (Classification/Regression).
  • Auto-detection of common target column names.
• Automated Model Training & Comparison:
  • Trains a suite of models tailored to the problem type:
    • Classification: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, SVM, K-Nearest Neighbors, Gaussian Naive Bayes.
    • Regression: Linear Regression, Ridge Regression, ElasticNet, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, SVR, K-Nearest Neighbors Regressor.
  • Displays a leaderboard with key performance metrics (Accuracy, F1, AUC for classification; R2, MSE for regression).
• Model Explainability (XAI):
  • Utilizes SHAP (SHapley Additive exPlanations) for the best model.
  • Global feature importance plots.
  • Detailed SHAP summary plots (e.g., beeswarm) and individual prediction explanations (waterfall plots coming soon).
• Model Export: Download the trained best model (including preprocessing steps) as a .joblib file for deployment or further use.

⚙️ Setup & Installation

1. Prerequisites: Python 3.7+ installed.
2. Clone the Repository (Optional):

   # git clone <your_repository_url> # If you have it on Git
   # cd AutoML-WebApp
   

   Alternatively, ensure app.py and requirements.txt are in your project directory.
3. Create and Activate Virtual Environment (Recommended):

   python3 -m venv venv
   source venv/bin/activate  # macOS/Linux
   # venv\Scripts\activate    # Windows
   

4. Install Dependencies:

   pip install -r requirements.txt
   

🚀 Running the Application

1. Navigate to your project directory in the terminal.
2. Run the Streamlit app:

   streamlit run app.py
   

3. Open your browser and go to the URL provided (usually http://localhost:8501).

🔮 Upcoming Features & Enhancements

We are continuously working to improve this AutoML application. Here are some features on our roadmap:

• Advanced Preprocessing Options:
  • User control over imputation strategies (mean, median, mode, constant).
  • More encoding techniques (e.g., One-Hot Encoding, Target Encoding).
  • Feature selection techniques.
• Hyperparameter Tuning:
  • Integration of GridSearchCV or RandomizedSearchCV for optimizing model hyperparameters.
  • User interface to define search spaces.
• Expanded Model Support:
  • LightGBM, XGBoost, CatBoost for both classification and regression.
  • Basic Time Series forecasting models (e.g., ARIMA, Prophet) if applicable data is provided.
• Enhanced Evaluation & Visualization:
  • Interactive Confusion Matrix, ROC/AUC curves, Precision-Recall curves for classification.
  • Residual plots, Actual vs. Predicted plots for regression.
  • Cross-validation score details.
• Deployment & Integration:
  • Option to generate a simple Flask API endpoint for the exported model.
  • Dockerization support for easier deployment.
• User Experience & Robustness:
  • More detailed error handling and user guidance.
  • Saving and loading of experiment configurations.
  • Support for larger datasets (optimizations for memory and speed).
• Advanced Explainability:
  • Individual prediction explanations (waterfall plots).
  • Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots.
• Data Insights:
  • Automated exploratory data analysis (EDA) report generation.

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This application is actively developed, with assistance from AI pair programming.