{"id":22552677,"url":"https://github.com/lilivalgo/ml_time_series","last_synced_at":"2026-04-11T00:14:39.831Z","repository":{"id":228244598,"uuid":"773458095","full_name":"LiliValGo/ML_Time_Series","owner":"LiliValGo","description":"This project uses time series data to predict corn crop yield in Colombia ","archived":false,"fork":false,"pushed_at":"2024-03-17T18:37:21.000Z","size":3418,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-02T10:45:43.446Z","etag":null,"topics":["matplotlib","numpy","pandas","scipy","seaborn","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LiliValGo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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}},"created_at":"2024-03-17T18:03:02.000Z","updated_at":"2024-03-17T18:43:48.000Z","dependencies_parsed_at":"2024-03-17T20:51:05.464Z","dependency_job_id":null,"html_url":"https://github.com/LiliValGo/ML_Time_Series","commit_stats":null,"previous_names":["lilivalgo/ml_time_series"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LiliValGo%2FML_Time_Series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LiliValGo%2FML_Time_Series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LiliValGo%2FML_Time_Series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LiliValGo%2FML_Time_Series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LiliValGo","download_url":"https://codeload.github.com/LiliValGo/ML_Time_Series/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246009905,"owners_count":20709027,"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":["matplotlib","numpy","pandas","scipy","seaborn","sklearn"],"created_at":"2024-12-07T18:06:59.236Z","updated_at":"2025-12-30T23:20:35.728Z","avatar_url":"https://github.com/LiliValGo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Colombia Crop Yield Prediction\n\nThis project uses time series data to predict corn crop yield in Colombia.\n\n## Data\n\nThe training data was obtained from [Datos Abiertos Colombia](https://www.datos.gov.co/Agricultura-y-Desarrollo-Rural/RENDIMIENTO-DE-CULTIVOS-EN-COLOMBIA-POR-A-O/fgh5-rjkd). All data related to corn crops were selected.\n\n## Methodology\n\n* Data processing and visualization were performed with Pandas and Seaborn.\n\n* Two-time series linear regression models were constructed using two data transformation processes:\n\n  - Time-Step Feature\n  - Lag Feature\n\n## Metrics\n\nThe models were evaluated using the following metrics:\n\n* R-squared\n* Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)\n* Mean Absolute Error (MAE)\n* Residual Analysis\n\n  - A visual analysis of the residuals was carried out using Matplotlib and Seaborn.\n\n  - A quantitative analysis of the residuals was conducted using the Shapiro-Wilk normality test.\n\n## Conclusions\n\nIt was concluded that using either transformation technique for the model's input features (Time-Step Feature and Lag Feature) does not represent much variation in the model's results except for the p-value of the Shapiro-Wilk normality test which marked a slight difference.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flilivalgo%2Fml_time_series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flilivalgo%2Fml_time_series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flilivalgo%2Fml_time_series/lists"}