Update README.md

embedded diagrams in readme, updated table of contents

Author: SamoraHunter

README.md

@@ -6,7 +6,7 @@ This repository contains Python code for binary classification using grid search
 
 - [ml_binary_classification_gridsearch_hyperOpt](#ml_binary_classification_gridsearch_hyperopt)
 - [Overview](#overview)
-- [ML repository architecture](#ml-repository-architecture)
+- [Diagrams](#diagrams)
 - [Getting Started](#getting-started)
   - [Prerequisites](#prerequisites)
 - [Installation](#installation)
@@ -24,9 +24,50 @@ This repository contains Python code for binary classification using grid search
 
 Binary classification is a common machine learning task where the goal is to categorize data into one of two classes. This repository provides a framework for performing binary classification using various machine learning algorithms and optimizing their hyperparameters through grid search and hyperparameter optimization techniques.
 
-## ML repository architecture
+## Diagrams
+
+Below are visual diagrams representing various components of the project. All `.mmd` source files are Mermaid diagrams, and the rendered versions are available in `.svg` or `.png` formats.
+
+### Feature Importance
+- [Mermaid source](assets/data_feature_importance_methods.mmd)  
+  <img src="assets/data_feature_importance_methods.svg" width="400" height="300"/>
+
+### Data Pipeline
+- [Mermaid source](assets/data_pipeline.mmd)  
+  <img src="assets/data_pipeline.svg" width="400" height="300"/>
+
+### Grid Parameter Search Space
+- [Mermaid source](assets/grid_param_space.mmd)  
+  <img src="assets/grid_param_space.svg" width="400" height="300"/>
+
+### Hyperparameter Search
+- [Mermaid source](assets/hyperparameter_search.mmd)  
+  <img src="assets/hyperparameter_search.svg" width="400" height="300"/>
+
+### Imputation Pipeline
+- [Mermaid source](assets/impute_data_for_pipe.mmd)  
+  <img src="assets/impute_data_for_pipe.svg" width="400" height="300"/>
+
+### ML Repository Architecture
+- [Mermaid source](assets/ml_repository_architecture.mmd)  
+  <img src="assets/ml_repository_architecture.png" width="400" height="300"/>
+
+### Model Class Listing (Time Series)
+- [Mermaid source](assets/model_class_list_model_class_list_ts.mmd)  
+  <img src="assets/model_class_list_model_class_list_ts.svg" width="400" height="300"/>
+
+### Project Scoring and Model Saving
+- [Mermaid source](assets/project_score_save.mmd)  
+  <img src="assets/project_score_save.svg" width="400" height="300"/>
+
+### Time Series Helper Functions
+- [Mermaid source](assets/time_series_helper.mmd)  
+  <img src="assets/time_series_helper.svg" width="400" height="300"/>
+
+### Unit Test - Synthetic Data
+- [Mermaid source](assets/unit_test_synthetic.mmd)  
+  <img src="assets/unit_test_synthetic.svg" width="400" height="300"/>
 
-![Alt text](assets/ml_repository_architecture.png)
 
 ## Getting Started
 
@@ -81,21 +122,22 @@ See Appendix
 See [ml_grid/tests/unit_test_synthetic.ipynb]
 
 
-Contributing
+## Contributing
 If you would like to contribute to this project, please follow these steps:
 
 Fork the repository on GitHub.
 Create a new branch for your feature or bug fix.
 Make your changes and commit them with descriptive commit messages.
 Push your changes to your fork.
 Create a pull request to the main repository's master branch.
-License
+
+## License
 This project is licensed under the MIT License - see the LICENSE file for details.
 
 
 ## Appendix
 
 
-Acknowledgments
+## Acknowledgments
 scikit-learn
 hyperopt