{"id":15989015,"url":"https://github.com/smirnovlad/data-science-notebooks","last_synced_at":"2026-04-10T15:48:23.681Z","repository":{"id":207717078,"uuid":"719922551","full_name":"smirnovlad/data-science-notebooks","owner":"smirnovlad","description":"A collection of various data analysis approaches","archived":false,"fork":false,"pushed_at":"2024-06-01T11:22:38.000Z","size":12450,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T07:13:00.972Z","etag":null,"topics":["data-science","deep-learning","kaggle","machine-learning","numpy","pandas","pytorch"],"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/smirnovlad.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":"2023-11-17T07:31:43.000Z","updated_at":"2024-06-01T11:22:41.000Z","dependencies_parsed_at":"2024-06-01T12:55:12.965Z","dependency_job_id":null,"html_url":"https://github.com/smirnovlad/data-science-notebooks","commit_stats":null,"previous_names":["smirnovlad/data-science-notebooks"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smirnovlad%2Fdata-science-notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smirnovlad%2Fdata-science-notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smirnovlad%2Fdata-science-notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/smirnovlad%2Fdata-science-notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/smirnovlad","download_url":"https://codeload.github.com/smirnovlad/data-science-notebooks/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247257025,"owners_count":20909391,"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":["data-science","deep-learning","kaggle","machine-learning","numpy","pandas","pytorch"],"created_at":"2024-10-08T04:22:54.336Z","updated_at":"2025-12-30T23:07:52.186Z","avatar_url":"https://github.com/smirnovlad.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data science notebooks\nThis repository comprises a compilation of my solutions and experiments in machine learning problems. Jupyter notebooks encompass research on open-source datasets and solutions to Kaggle competitions. Here, you can explore a diverse range of approaches and techniques for data analysis, along with the application of machine and deep learning methods.\n\n## Kaggle\n- [Simpsons classification](https://www.kaggle.com/competitions/journey-springfield), [simpsons.ipynb](kaggle/simpsons.ipynb) (DLS assignment)\n\n## MIPT ML courses\n\n### Generic\n- [Wine dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html), [wine.ipynb](mipt_ml_courses/generic/wine.ipynb)\n- [Regression task with a raw dataset](https://www.kaggle.com/competitions/fall-ml2-mipt-2023/overview), [raw_dataset_regression.ipynb](mipt_ml_courses/generic/raw_dataset_regression.ipynb)\n\n### Computer vision\n- [Breast cancer cells segmentation](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490494/), [cancer_cells_segmentation](mipt_ml_courses/cv/cancer_cells_segmentation.ipynb)\nTODO: add classification metrics such as Precision, Recall, etc.\n- [Panorama stitching](mipt_ml_courses/cv/panorama_stitching.ipynb)\n\n### Advanced\n[Repository](https://github.com/andriygav/MachineLearningSeminars)\n\n- [Analysis of CNN on the EMNIST-letters dataset](mipt_ml_courses/advanced/cnn_emnist.ipynb)\n- [Prediction of POS tags for tokens in the NERUS dataset using LSTM](mipt_ml_courses/advanced/lstm_nerus.ipynb)\n- [Analysis of an autoencoder in the sentence reconstruction task for the Twitter dataset](mipt_ml_courses/advanced/autoencoder_twitter.ipynb)\n- [Generation of image annotations for the COCO dataset](mipt_ml_courses/advanced/image_captioning_coco.ipynb)\n\n## Deep Learning School\n- [PH2 Dataset images](https://www.fc.up.pt/addi/ph2%20database.html), [ph2_semantic_segmentation.ipynb](dls/ph2_semantic_segmentation.ipynb)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmirnovlad%2Fdata-science-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmirnovlad%2Fdata-science-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmirnovlad%2Fdata-science-notebooks/lists"}