{"id":24523595,"url":"https://github.com/samuele-lolli/data-analytics-techniques","last_synced_at":"2026-04-11T13:33:34.141Z","repository":{"id":271930199,"uuid":"914659518","full_name":"samuele-lolli/Data-Analytics-Techniques","owner":"samuele-lolli","description":"A practical approach to data analytics pipeline.","archived":false,"fork":false,"pushed_at":"2025-01-10T19:55:08.000Z","size":11021,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-15T14:12:36.708Z","etag":null,"topics":["numpy","pandas","pytorch","scikit-learn"],"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/samuele-lolli.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":"2025-01-10T03:30:46.000Z","updated_at":"2025-01-10T20:43:03.000Z","dependencies_parsed_at":"2025-01-10T20:43:26.999Z","dependency_job_id":null,"html_url":"https://github.com/samuele-lolli/Data-Analytics-Techniques","commit_stats":null,"previous_names":["samuele-lolli/data-analytics-techniques"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/samuele-lolli%2FData-Analytics-Techniques","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/samuele-lolli%2FData-Analytics-Techniques/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/samuele-lolli%2FData-Analytics-Techniques/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/samuele-lolli%2FData-Analytics-Techniques/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/samuele-lolli","download_url":"https://codeload.github.com/samuele-lolli/Data-Analytics-Techniques/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243738979,"owners_count":20340002,"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","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":["numpy","pandas","pytorch","scikit-learn"],"created_at":"2025-01-22T04:15:57.182Z","updated_at":"2025-12-31T00:21:50.090Z","avatar_url":"https://github.com/samuele-lolli.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analytics Techniques\n\nThis project uses a subset of the Million Song Database, which contains acoustic features and a `year` column. The objective of this task is to predict the year of release of a track based on its acoustic features.\n\nThe project covers the complete standard data analytics pipeline, including the following steps:\n\n1. **Data Visualization**\n\n2. **Preprocessing**\n   - Handling outliers (Winsorization and other techniques)\n   - Various types of scaling and normalization\n   - Principal Component Analysis (PCA)\n\n3. **Training Classical Machine Learning Models**\n   - Random Forest\n   - K-Nearest Neighbors (KNN)\n   - Support Vector Machine (SVM)\n   - Linear Regression\n\n4. **Training a Feedforward Neural Network using PyTorch**\n\n5. **Using PyTorch Tabular Models**\n   - TabNet\n   - TabTransformers\n\n6. **Hyperparameter Tuning**\n\n7. **Model Evaluation on Test Set**\n\nFor more information, you can view the following Jupyter Notebooks:\n- `data-visualization.ipynb`\n- `ml-sklearn.ipynb`\n- `feedforward-pytorch.ipynb`\n- `tabular-pytorch.ipynb`\n\nThis project demonstrates a comprehensive approach to data analytics, from preprocessing to model evaluation, using both classical machine learning models and more advanced neural network models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuele-lolli%2Fdata-analytics-techniques","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamuele-lolli%2Fdata-analytics-techniques","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamuele-lolli%2Fdata-analytics-techniques/lists"}