{"id":25444182,"url":"https://github.com/dioptx/volumepredictionassignment","last_synced_at":"2025-09-06T20:32:20.850Z","repository":{"id":78768372,"uuid":"189586554","full_name":"dioptx/VolumePredictionAssignment","owner":"dioptx","description":"Quick assignment for skill showcase","archived":false,"fork":false,"pushed_at":"2019-10-02T20:02:41.000Z","size":2374,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-01T18:43:13.861Z","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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dioptx.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}},"created_at":"2019-05-31T11:58:01.000Z","updated_at":"2019-10-02T20:03:09.000Z","dependencies_parsed_at":"2023-03-18T03:45:15.725Z","dependency_job_id":null,"html_url":"https://github.com/dioptx/VolumePredictionAssignment","commit_stats":null,"previous_names":["dioptx/volumepredictionassignment"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dioptx/VolumePredictionAssignment","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dioptx%2FVolumePredictionAssignment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dioptx%2FVolumePredictionAssignment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dioptx%2FVolumePredictionAssignment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dioptx%2FVolumePredictionAssignment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dioptx","download_url":"https://codeload.github.com/dioptx/VolumePredictionAssignment/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dioptx%2FVolumePredictionAssignment/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273958495,"owners_count":25198060,"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":"2025-09-06T02:00:13.247Z","response_time":2576,"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":"2025-02-17T15:29:53.492Z","updated_at":"2025-09-06T20:32:20.829Z","avatar_url":"https://github.com/dioptx.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Volume Stream Prediction - Tech: LSTM \u0026 SARIMA\n---\n### Code Coverage: ~85%\n\n\n### Setup:\n\n### Step 1:\n\n#####  | Dependencies:\n\n   1. Create a pip / conda Python 3.7 environment.\n   2. Navigate into the project tree.\n   3. Run the following command:\n    \n    # Unix\n    sudo pip install -r requirements.txt\n    # Windows (Elevated)\n    pip install -r requirements.txt\n\n\n----\n### Step 2:\n\n#####  | routes.csv import:\n\nIn order for all the tests to be run successfully\n place the routes.csv file that was given in the project description like this:\n  \n - module\n    - data\n      - raw\n        - routes.csv\n        \n----\n\nThe prediction functionality can be shown in the respective notebooks for SARIMA and LSTM models,\nand follows the same mentality like the one bellow:\n\n    from src.modeling import lstmModel\n    from src.processing import dataProc\n    \n    # Aggregated 1 hour Dataset fetch\n    DATASET_PATH = module_path + \"/notebook/dt_agg1hour.h5\"\n    dataset = pd.read_pickle(\n        DATASET_PATH)\n    \n    dataset = dataProc.create_features(dataset= dataset)\n\n    # Model creation\n    lstmodel = lstmModel(perform_scale=True)\n    \n    # Model training\n    lstmodel.train(dataset[start_date:end_date], evaluate=False)\n    \n    # Model usage\n    y_pred, y = lstmodel.generate_prediction(input_data= dataset[end_date:pd.Timestamp(end_date)+pd.Timedelta(hours=48)])\n\n    \n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdioptx%2Fvolumepredictionassignment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdioptx%2Fvolumepredictionassignment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdioptx%2Fvolumepredictionassignment/lists"}