{"id":18655794,"url":"https://github.com/jezbravo/lstm","last_synced_at":"2026-04-08T11:32:10.232Z","repository":{"id":240024975,"uuid":"766519923","full_name":"jezbravo/lstm","owner":"jezbravo","description":"Stock predictions with LSTM","archived":false,"fork":false,"pushed_at":"2024-05-16T08:07:48.000Z","size":196,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-03T16:19:34.700Z","etag":null,"topics":["jupiter-notebook","keras","keras-neural-networks","keras-tensorflow","lstm","lstm-model","lstm-neural-networks","machine-learning","matplotlib","numpy","pandas","pandas-dataframe","python","scikit-learn","scikitlearn-machine-learning","stock-price-prediction"],"latest_commit_sha":null,"homepage":"https://bit.ly/3yo54Z1","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/jezbravo.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":"2024-03-03T13:51:53.000Z","updated_at":"2024-05-29T00:25:09.000Z","dependencies_parsed_at":"2024-05-16T10:48:54.745Z","dependency_job_id":null,"html_url":"https://github.com/jezbravo/lstm","commit_stats":null,"previous_names":["jezbravo/lstm"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jezbravo/lstm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jezbravo%2Flstm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jezbravo%2Flstm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jezbravo%2Flstm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jezbravo%2Flstm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jezbravo","download_url":"https://codeload.github.com/jezbravo/lstm/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jezbravo%2Flstm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31554090,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T10:21:54.569Z","status":"ssl_error","status_checked_at":"2026-04-08T10:21:38.171Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["jupiter-notebook","keras","keras-neural-networks","keras-tensorflow","lstm","lstm-model","lstm-neural-networks","machine-learning","matplotlib","numpy","pandas","pandas-dataframe","python","scikit-learn","scikitlearn-machine-learning","stock-price-prediction"],"created_at":"2024-11-07T07:20:08.056Z","updated_at":"2026-04-08T11:32:10.206Z","avatar_url":"https://github.com/jezbravo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Stock predictions with LSTM\n\n## Description\n\nThis application exemplifies the implementation of an LSTM network for the prediction of stock values ​​in the market.\n\n### Stack\n\n- Python:\n  - Keras\n  - Matplotlib\n  - Numpy\n  - Pandas\n  - Scikt Learn\n  - Jupiter Notebook\n\n## Setup\n\nFirst of all you must clone the repository:\n\n```sh\ngit clone https://github.com/jezbravo/lstm.git\ncd lstm\n```\n\nCreate a virtual environment to install dependencies in and activate it:\n\n```python\npython.exe -m venv venv\n.\\venv\\Scripts\\activate\n```\n\nThen install the dependencies:\n\n```python\n(venv)pip install -r requirements.txt\n```\n\nNote the `(venv)` in front of the prompt. This indicates that this terminal session operates in a virtual environment set up by \"venv\".\n\nOnce everything is ready:\n\n```python\n(venv)python.exe .\\app.py\n```\n\n## Demo\n\nBoth the dataset and the training parameters can be adjusted to improve the performance of the model and obtain better results.\n\nYou can review the development of the project at the following link: [LSTM Notebook](https://bit.ly/3yo54Z1).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjezbravo%2Flstm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjezbravo%2Flstm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjezbravo%2Flstm/lists"}