{"id":32578464,"url":"https://github.com/atalv/experiments","last_synced_at":"2026-06-30T23:31:54.422Z","repository":{"id":176535614,"uuid":"656624998","full_name":"atalv/experiments","owner":"atalv","description":"Experiments done for hands-on learning with dummy data","archived":false,"fork":false,"pushed_at":"2024-01-18T21:08:29.000Z","size":5911,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-29T14:58:07.646Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/atalv.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-06-21T10:07:11.000Z","updated_at":"2023-06-24T03:13:48.000Z","dependencies_parsed_at":"2024-01-18T22:45:17.189Z","dependency_job_id":"dd0f2632-6dc1-4425-bd9a-8e4ecd88a2ec","html_url":"https://github.com/atalv/experiments","commit_stats":null,"previous_names":["atalv/experiments"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/atalv/experiments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atalv%2Fexperiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atalv%2Fexperiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atalv%2Fexperiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atalv%2Fexperiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/atalv","download_url":"https://codeload.github.com/atalv/experiments/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atalv%2Fexperiments/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34987610,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-30T02:00:05.919Z","response_time":92,"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":[],"created_at":"2025-10-29T14:57:01.992Z","updated_at":"2026-06-30T23:31:54.404Z","avatar_url":"https://github.com/atalv.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Overview\n\nThis repo is to store the experiments done for hands-on learning with *dummy data*. It is never too late to learn and *showcase*!\n\n- Each sub-directory in the root is named as the main topic of the experiment.\n- All the contents are created solely by me with guidance from official resources and academic experts.\n\n## Citation\n\nIf you use any of this work then please add a referrence to this repository ['Experiments by Vivek Atal'](https://github.com/atalv/experiments/) as a fair usage policy.\n\n## Some highlighs\n\n- **GraphNetwork**: \n    - Predicted whether a user of LastFM would follow another user and serve as a recommendation.\n    - Implemented multiple node embedding approaches for link prediction - *[Graph Factorization](https://doi.org/10.1145/2488388.2488393), [DeepWalk](https://arxiv.org/pdf/1403.6652.pdf), [Node2Vec](https://arxiv.org/pdf/1607.00653.pdf), [Adamic-Adar index](http://www.cs.cornell.edu/home/kleinber/link-pred.pdf)* - and compared their performance for link prediction task.\n    - Referred to excellent materials by Stanford CS224W course on [Machine Learning with Graphs](http://web.stanford.edu/class/cs224w/).\n\n- **MachineLearning**: \n    - Predicted NYC taxi trip duration.\n    - Implemented typical machine learning models from *[scikit-learn](https://scikit-learn.org/stable/)* ([GammaRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html), [RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html), [HistGradientBoostingRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html)) with intelligently derived features, viz., traffic information in an area at a given time window based on average active number of trips originating or ending.\n\n- **ReinforcementLearning**: \n    - Simulated multiple UCB (Upper Confidence Bound) policies for MAB ([Multi Armed Bandit](https://en.wikipedia.org/wiki/Multi-armed_bandit)) problems and MDP ([Markov Decision Process](https://en.wikipedia.org/wiki/Markov_decision_process)) and compared their performance.\n    - Learned to do simulation of multiple states Markov Chain and calculate average reward, expected present value, estimate steady state probabilities, etc.\n    - Most of the research papers referred for simulation exercises are authored by [Dr. Michael Katehakis](http://en.wikipedia.org/wiki/Michael_N._Katehakis).\n\n- **TimeSeries**: \n    - Forecasted 2 weeks ahead grocery store sales of 33 product groups across 54 stores, approx. 1.8K time series.\n    - Engineered multiple sensible features, viz., cross-store, cross-product elements, algorithmically short-listed important events for a given store-product, etc.\n    - Some Seasonal ARIMA models were built manually, and then scaled it using ARIMA where seasonal components were extracted beforehand for faster execution.\n    - Experimented with [DeepAR on AWS Sagemaker](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html) to build a single global model instead of 1.8K ARIMA models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatalv%2Fexperiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fatalv%2Fexperiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatalv%2Fexperiments/lists"}