{"id":23118639,"url":"https://github.com/xaxm007/mlchemy","last_synced_at":"2025-10-08T10:57:15.743Z","repository":{"id":268373907,"uuid":"859313212","full_name":"xaxm007/MLchemy","owner":"xaxm007","description":"A workspace related to all my Machine Learning study.","archived":false,"fork":false,"pushed_at":"2024-12-16T10:44:56.000Z","size":224022,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-24T19:39:21.729Z","etag":null,"topics":["association-rule-learning","classification-model","clustering","deep-neural-networks","dimentionality-reduction","machine-learning","nlp-machine-learning","pca","regression-models","reinforcement-learning-algorithms"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/xaxm007.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-18T13:03:17.000Z","updated_at":"2024-12-16T10:45:01.000Z","dependencies_parsed_at":"2024-12-16T11:38:30.011Z","dependency_job_id":"1f2fb063-bf55-4bbd-9869-2d82f2776f06","html_url":"https://github.com/xaxm007/MLchemy","commit_stats":null,"previous_names":["xaxm007/mlchemy"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/xaxm007/MLchemy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xaxm007%2FMLchemy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xaxm007%2FMLchemy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xaxm007%2FMLchemy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xaxm007%2FMLchemy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xaxm007","download_url":"https://codeload.github.com/xaxm007/MLchemy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xaxm007%2FMLchemy/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278931653,"owners_count":26070789,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["association-rule-learning","classification-model","clustering","deep-neural-networks","dimentionality-reduction","machine-learning","nlp-machine-learning","pca","regression-models","reinforcement-learning-algorithms"],"created_at":"2024-12-17T05:19:13.990Z","updated_at":"2025-10-08T10:57:15.713Z","avatar_url":"https://github.com/xaxm007.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 💻 MLchemy\n\nA repository showcasing all my Jupyter notebooks and datasets used to implement various machine learning models during my learning journey.\n\n---\n\n## 📋 Table of Contents\n\n- [Folder Structure](#folder-structure)\n- [Topics Covered](#topics-covered)\n- [How to Use](#how-to-use)\n- [Requirements](#requirements)\n\n---\n\n## 📂 Folder Structure\n\n```\nMLchemy/\n│\n├── Regression/\n│   ├── data\n│   │   └── dataset.csv\n│   └── notebook\n│       ├── simple_linear_regression.ipynb\n│       ├── support_vector_regression.ipynb\n│       └── decision_tree_regression.ipynb\n│\n├── Classification/\n│   ├── data\n│   │   └── dataset.csv\n│   └── notebook\n│       ├── k_nearest_neighbours.ipynb\n│       ├── support_vector_machine.ipynb\n│       └── naive_bayes.ipynb\n│\n└── Dimensionality Reduction (PCA)/\n    ├── data\n    │   └── dataset.csv\n    └── notebook\n        ├── principal_component_analysis.ipynb\n        └── kernel_pca.ipynb\n```\n\n---\n\n## 📎 Topics Covered\n\nMachine Learning Implementations covered in this repository:\n\n- **[Regression](./Regression/)**: \n   - Simple Linear Regression\n   - Multiple Linear Regression\n   - Polynomial Regression\n   - Support Vector Regression\n   - Decision Tree Regression\n   - Random Forest Regression\n\n- **[Classification](./Classification/)**: \n   - Logistic Regression\n   - K Nearest Neighbors \n   - Support Vector Machines\n   - Kernel SVM\n   - Naive Bayes\n   - Decision Tree Classification\n   - Random Forest Classification\n\n- **[Clustering](./Clustering/)**: \n   - K-Means Clustering\n   - Hierarchical Clustering\n\n- **[Association Rule Learning](./Association%20Rule%20Learning/)**:\n   - Apriori\n   - Eclat\n\n- **[Reinforcement Learning](./Reinforcement%20Learning/)**\n   - Upper Confidence Bound\n   - Thompson Sampling\n\n- **[Natural Language Processing](./Natural%20Language%20Processing/)**\n   - Bag of Words\n\n- **[Deep Learning](./Deep%20Learning/)**\n   - Artificial Neural Networks\n   - Convolutional Neural Networks\n\n- **[Dimensionality Reduction](./PCA/)**: \n   - Principal Component Analysis (PCA)\n   - Linear Discriminant Analysis (LDA)\n   - Kernel PCA\n\n- **[Model Selection](./Model%20Selection/)**\n   - Grid Search\n   - k-Fold Cross Validation\n\n---\n\n## 👉 How to Use\n\nTo run the notebooks locally, follow these steps:\n\n1. **Clone the repository**:\n\n   ```bash\n   git clone https://github.com/yourusername/MLchemy.git\n   cd MLchemy\n   ```\n\n   `Recommended`, Follow steps to use conda env:\n\n   1. **Create a conda env**: \n\n      ```bash\n      conda create -n \u003cyour_environment_name\u003e\n      ```\n\n   2. **Install Ipykernel for using Jupyter Notebook**:\n\n      ```bash\n      conda install ipykernel\n      ```\n\n   3. **Connect new ipykernel to the conda env**:\n\n      ```bash\n      python -m ipykernel install --user --name \u003cyour_env_name\u003e --display-name \"\u003cnew_name_for_your_kernel\"\n      ```\n\n2. **Install the required dependencies** (see [Requirements](#requirements)):\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Run the Jupyter Notebooks**:\n\n   Launch Jupyter Notebook:\n\n   ```bash\n   jupyter notebook\n   ```\n\n   Navigate to the relevant notebook under the respective folder and run the cells.\n\n---\n\n## 🛠️ Requirements\n\nTo run the code in this repository, you need the following:\n\n- Numpy\n- Pandas \n- Scikit-learn\n- Matplotlib\n- Apyori\n- Seaborn\n- NLTK\n- XGBoost\n- Jupyter Notebook\n- Required packages are listed in `requirements.txt`. Install them using:\n\n  ```bash\n  pip install -r requirements.txt\n  ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxaxm007%2Fmlchemy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxaxm007%2Fmlchemy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxaxm007%2Fmlchemy/lists"}