RAQA-AutoML: An Integrated Solution for Automated Machine Learning
Overview AutoML-Lite is a powerful, user-friendly desktop application designed to democratize machine learning by automating the entire modeling pipeline. Built with Python and PyQt6, it provides a comprehensive GUI-based environment for data preprocessing, feature engineering, model selection, hyperparameter tuning, and results analysis. The application supports over 60 machine learning models, advanced statistical analysis, and professional report generation, making it suitable for both novice users and experienced data scientists. Key Features Data Management Support for multiple file formats: Excel (.xlsx, .xls, .xlsm, .xlsb), CSV, TSV, TXT, JSON, XML, HTML, SPSS (.sav), Stata (.dta), SAS (.sas7bdat), Parquet, Feather, and OpenDocument (.ods) Intelligent data loading with chunk-based processing for large datasets Real-time data preview with sorting and filtering capabilities Missing data detection Column and row management (delete, restore, reset to original) Automated Machine Learning +60 Machine Learning Models across multiple categories: Tree-based: RandomForest, XGBoost, LightGBM, CatBoost, ExtraTrees, GradientBoosting, AdaBoost, Bagging, HistGradientBoosting, DecisionTree Linear Models: LinearRegression, Ridge, Lasso, ElasticNet, BayesianRidge, HuberRegressor, TheilSen, RANSAC SVM: SVR, NuSVR, LinearSVR Neural Networks: MLPRegressor, LSTM, GRU, TransformerRegressor, CNNRegressor Ensemble: VotingRegressor, StackingRegressor, IsolationForestRegressor Hybrid: ARIMA-SVR, SARIMAX-SVR Bayesian: GaussianProcess, BayesianGaussianProcess Intelligent Model Selection Automatically selects the best model based on dataset characteristics using cross-validation and performance metrics Hyperparameter Optimization: Supports RandomizedSearch, GridSearch, and BayesianSearch strategies Cross-Validation: Standard K-Fold and Time Series cross-validation Polynomial Features: Automatic polynomial feature generation Data Preprocessing Intelligent Imputation: Mean, Median, Mode, KNN, Iterative, and Drop methodsSmart Imputation: Automatically selects the best imputation method based on data characteristicsFeature Engineering: Interaction features (addition, subtraction, multiplication) Polynomial features Combination features Scaling: StandardScaler, MinMaxScaler, RobustScaler with intelligent selection Statistical AnalysisDescriptive Statistics: Comprehensive summary statisticsHypothesis Testing: t-test (Independent and Paired) One-Way and Two-Way ANOVA Chi-Square Test Mann-Whitney U Test Kruskal-Wallis Test Wilcoxon Signed-Rank Test Correlation Analysis: Pearson and Spearman correlation Normality Tests: Shapiro-Wilk and Kolmogorov-Smirnov Advanced Analysis Factor Analysis with Scree plots K-Means and Hierarchical Clustering Linear Regression with full statistical summary Visualization and Reporting Diagnostic Plots: Feature Importance Predicted vs Actual Residual Plots Learning Curves Correlation Heatmaps Q-Q Plots Feature Histograms SHAP Summary and Dependence Plots Time Series Predictions Technical Architecture | Component | Technology ||-----------|------------|| GUI Framework | PyQt6 (Qt 6.x) || Core Language | Python 3.8+ || Machine Learning | Scikit-learn 1.3+, XGBoost, LightGBM, CatBoost || Deep Learning | TensorFlow 2.13+, Keras, PyTorch 2.0+ || Data Processing | Pandas 2.0+, NumPy 1.24+ || Visualization | Matplotlib 3.7+, Seaborn 0.12+ || Statistical Analysis | SciPy 1.10+, StatsModels 0.14+ || SHAP Analysis | SHAP 0.41+ || Caching | Joblib || Installation | PyInstaller, Inno Setup | System Requirements | Component | Minimum | Recommended ||-----------|---------|-------------|| OS | Windows 10/11 (64-bit) | Windows 10/11 (64-bit) || Processor | Intel Core i5 | Intel Core i7/i9 or AMD Ryzen 7+ || RAM | 8 GB | 16 GB or more || Storage | 3 GB free space || .NET Framework | 4.8+ | 4.8+ || Python | 3.8+ (for development) | 3.10+ | Installation and Usage 1. Download the installer: `AutoML_Lite_Setup.exe`2. Run the installer with administrator privileges3. Follow the installation wizard4. Launch from desktop shortcut or Start Menu Repository Structure AutoML-Lite/├── src/│ ├── main.py│ ├── base_page.py│ ├── cache_manager.py│ ├── language_manager.py│ ├── upload_page.py│ ├── project_management_page.py│ ├── start_page.py│ ├── imputation_page.py│ ├── imputation_methods.py│ ├── data_imputer.py│ ├── model_page.py│ ├── results_analysis_page.py│ ├── statistical_analysis_dialog.py│ ├── preprocessing.py│ └── style_manager.py├── assets/│ ├── background.png│ ├── output-onlinegiftools.gif│ ├── Vazirmatn-Regular.ttf│ └── icon.ico├── docs/│ ├── info.txt│ └── license.txt├── requirements.txt