{"id":23623597,"url":"https://github.com/freedisch/optimization-with-multiple-variables","last_synced_at":"2025-06-30T08:08:25.242Z","repository":{"id":208117012,"uuid":"720853215","full_name":"Freedisch/optimization-with-multiple-variables","owner":"Freedisch","description":null,"archived":false,"fork":false,"pushed_at":"2023-11-19T20:22:45.000Z","size":130,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-14T09:41:03.898Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","readme":"# Submission for Summative Assignment: Linear Regression Optimization and Comparative Modeling\n\n## Introduction\nThis document accompanies my submission for the Summative Assignment on optimizing a linear regression model using gradient descent and comparing it with Decision Trees and Random Forests models. \n\n## Work Process\n### Notebook Review and Implementation\n- **Code Snippets Completion**: I've thoroughly reviewed the provided notebook and completed all the required code snippets. \n- **Passing Unit Tests**: Each section of the notebook was carefully addressed, ensuring that the code passed all the unit tests.\n\n### Exercises\nThe exercises I completed in the notebook include:\n1. **Data Preparation and Analysis**: Preprocessing and understanding the dataset for TV sales prediction.\n2. **Implementing Gradient Descent for Linear Regression**: Developing the linear regression model using gradient descent.\n3. **Model Optimization and Evaluation**: Fine-tuning the linear regression model for optimal performance.\n4. **Decision Trees and Random Forests Models**: Creating and analyzing these models for comparison.\n5. **Comparative Analysis**: Evaluating the RMSE of all models and ranking them based on their performance.\n\n## Results and Observations\n- **Model Comparisons**: The RMSE of each model was carefully calculated and compared. \n- **Ranking Models**: Based on the RMSE, I ranked the models from best to least performing in terms of accuracy.\n\n## Challenges and Learning\n- I faced challenges in optimizing the gradient descent algorithm but managed to overcome them through research and experimentation.\n- The comparative analysis of different models provided me with deeper insights into the strengths and weaknesses of each modeling approach.\n\n## Conclusion\nThis assignment was a comprehensive learning experience in understanding and implementing linear regression optimization and comparative model analysis. It has significantly enhanced my skills in machine learning and data analysis.\n\n## Additional Files\n- **Notebook File**: Attached with this submission.\n- **Cheat Sheet Reference**: Used for quick guidance and troubleshooting.\n\n## Acknowledgements\nI would like to thank the instructors and peers for their support and guidance throughout this project.\n\n---\n\n*Student Name: [Magnim Thibaut Batale]*  \n*Date: [19 November 2023]*  \n*Course: [Mathematics for Machine Learning]*  \n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffreedisch%2Foptimization-with-multiple-variables","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffreedisch%2Foptimization-with-multiple-variables","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffreedisch%2Foptimization-with-multiple-variables/lists"}