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emircansoftwareMCP SERVER

AutoML

Take a CSV from first look to a tuned model without leaving the conversation.

by emircansoftware ↗MLOps & Model LifecycleCommunity

LABSCO SUMMARY

A whole modelling pass, described rather than coded.

The sequence is fixed and sensible — inspect, preprocess, prepare, compare, tune — and each step is its own call, so you can stop at the correlation or outlier plots before committing to the next one. Regression covers Linear, Ridge, Lasso, ElasticNet, Random Forest, XGBoost, SVR, KNN and CatBoost; classification covers Logistic Regression, Ridge Classifier, Random Forest, XGBoost, SVM, KNN, Decision Tree, Naive Bayes and CatBoost.

What it is

A Python server wrapping an automated machine-learning workflow: dataset inspection, correlation and outlier plots, preprocessing, model comparison for regression or classification, hyperparameter tuning and prediction.

What you get

  • First look — information_about_data for shape, memory use, dtypes and missing values; reading_csv to load the file
  • Visual checks — visualize_correlation_num, visualize_correlation_cat and visualize_outliers, plus visualize_correlation_final and visualize_outliers_final for the post-preprocessing view
  • Preparation — preprocessing_data handles outliers and nulls, prepare_data encodes and scales for the problem type you name
  • Model comparison — models evaluates a set of algorithms and reports R², MAE and MSE for regression, or accuracy and F1 for classification
  • best_model_hyperparameter tunes the chosen model over a trial count and scoring metric you set
  • test_external_data scores a separate file, predict_value predicts for new input, feature_importance_analysis ranks features with XGBoost, visualize_accuracy_matrix draws the confusion matrix

Requirements

Python 3.8 or higher and pip install -r requirements.txt — pandas, pyarrow, numpy, scikit-learn, xgboost, lightgbm and catboost. Set the data directory path in utils/read_csv_file.py to match your machine, then point your client at uv --directory C:\YOUR\PROJECT\PATH\AutoML run main.py.

Setup effort

Build from source — git clone https://github.com/emircansoftware/AutoML.git

See installation options