ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!🌟💫 Devfolio URL, https://devfolio.co/projects/mlcrate-98f9
Info about the related issue (Aim of the project) : This project aims to develop predictive models to estimate scalar coupling constants between atom pairs in molecules based on their structural features and conduct exploratory data analysis (EDA) to uncover relationships between molecular properties and structural characteristics.
Name: S G V Kamalakar
Email ID for further communication: sgvkamalakar@gmail.com
Give a clear description of what have you added or modifications made
Modifications Made:
Added interactive 3D visualizations of molecular structures using Plotly, color-coded by atom type, and implemented animations to highlight atoms and bonds.
Analyzed and visualized the distribution of unique atom counts with histograms. Conducted extensive EDA, visualizing scalar coupling constants, Mulliken charges, dipole moments, and potential energy.
Created new features based on atomic interactions, standardized molecular data, and trained multiple regression models to predict scalar coupling constants, evaluating performance with MAE, MSE, and R².
Type of change ☑️
What sort of change have you made:
[x] New feature (non-breaking change which adds functionality)
How Has This Been Tested? ⚙️
Describe how it has been tested
Describe how have you verified the changes made
The changes were tested and verified through various steps. Interactive 3D visualizations were rendered and cross-checked against known molecular structures.
Histograms of unique atom counts were generated and validated by comparing them with raw data. EDA visualizations were inspected for accuracy and consistency.
Newly engineered features were analyzed for relevance and contribution to model performance.
Finally, regression models (Linear Regressor, Random Forest Regressor, K Nearest Neighbors, Support Vector Regressor, Decision Tree Regressor, and Simple Feed Forward Neural Network) were evaluated against benchmarks, and the entire workflow was validated for reproducibility in a clean environment.
Checklist: ☑️
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for ML-Crate 💡
Issue Title: Predicting Molecular Properties #636
Closes: #636
Describe the add-ons or changes you've made 📃
Give a clear description of what have you added or modifications made
Modifications Made:
Added interactive 3D visualizations of molecular structures using Plotly, color-coded by atom type, and implemented animations to highlight atoms and bonds.
Analyzed and visualized the distribution of unique atom counts with histograms. Conducted extensive EDA, visualizing scalar coupling constants, Mulliken charges, dipole moments, and potential energy.
Created new features based on atomic interactions, standardized molecular data, and trained multiple regression models to predict scalar coupling constants, evaluating performance with MAE, MSE, and R².
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
Describe how it has been tested Describe how have you verified the changes made
The changes were tested and verified through various steps. Interactive 3D visualizations were rendered and cross-checked against known molecular structures.
Histograms of unique atom counts were generated and validated by comparing them with raw data. EDA visualizations were inspected for accuracy and consistency.
Newly engineered features were analyzed for relevance and contribution to model performance.
Finally, regression models (Linear Regressor, Random Forest Regressor, K Nearest Neighbors, Support Vector Regressor, Decision Tree Regressor, and Simple Feed Forward Neural Network) were evaluated against benchmarks, and the entire workflow was validated for reproducibility in a clean environment.
Checklist: ☑️