{"id":18924483,"url":"https://github.com/shakebshamsi/unemployement-analysis","last_synced_at":"2026-03-14T03:30:20.934Z","repository":{"id":190146649,"uuid":"682022426","full_name":"ShakebShamsi/Unemployement-Analysis","owner":"ShakebShamsi","description":"Unemployement Analysis","archived":false,"fork":false,"pushed_at":"2023-08-23T10:19:28.000Z","size":143,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-31T17:38:12.639Z","etag":null,"topics":["data-science","machine-learning","python"],"latest_commit_sha":null,"homepage":"https://unemployement-shakebshamsi.streamlit.app/","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/ShakebShamsi.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":"2023-08-23T09:13:27.000Z","updated_at":"2023-08-23T11:49:44.000Z","dependencies_parsed_at":null,"dependency_job_id":"84ee4ae7-83ae-4b75-8b71-bdc80ea7bfe3","html_url":"https://github.com/ShakebShamsi/Unemployement-Analysis","commit_stats":null,"previous_names":["shakebshamsi/unemployement-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShakebShamsi%2FUnemployement-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShakebShamsi%2FUnemployement-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShakebShamsi%2FUnemployement-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShakebShamsi%2FUnemployement-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShakebShamsi","download_url":"https://codeload.github.com/ShakebShamsi/Unemployement-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239921875,"owners_count":19718842,"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","machine-learning","python"],"created_at":"2024-11-08T11:06:57.280Z","updated_at":"2026-03-14T03:30:20.792Z","avatar_url":"https://github.com/ShakebShamsi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hello! This side Shakeb Shamsi\n\n# Unemployment Analysis using Python\n\nThis project analyzes the _unemployment scenario_ before and after the lockdown using Python. It includes data analysis, visualizations, and insights derived from the provided dataset.\n\n## Features\n\n- Analyze the unemployment rates, employment, and labor participation for different states and regions.\n- Visualize the unemployment rates through various plots and charts.\n- Compare the average unemployment rates before and after the lockdown.\n- Explore the impact of lockdown on employment in different states.\n- Deployed as a web application using Streamlit for a better user interface.\n\n## Dataset\n\nThe dataset used for this analysis is available in the **`data.csv`** file. It contains information about unemployment rates, employment, labor participation, and other relevant factors for different states and regions.\n\n## Code\n\n- The `unemploymentAnalysis.ipynb` file contains the Jupyter Notebook code used for data analysis and visualization.\n- The `app.py` file contains the Streamlit code for deploying the project as a web application.\n- The `requirements.txt` file lists the dependencies required for running the Streamlit app.\n\n## Deployed Application\n\nThe project has been deployed as a web application using _Streamlit_. You can access the deployed application [here](https://unemployement-shakebshamsi.streamlit.app/).\n\n## Project Structure\nThe project repository has the following structure:\n- app.py\n- data.csv\n- requirements.txt\n- unemploymentAnalysis.ipynb\n- README.md\nFeel free to explore the repository and run the project locally.\n\n## Additional Notes\nIn this project, I performed an in-depth analysis of unemployment rates using Python as part of the _data science internship_ at **Oasis Infobyte**. I explored various visualizations to understand the trends and patterns in unemployment data. The project includes descriptive statistics, heatmaps, box plots, bar plots, scatter plots, and geographical plots to gain insights into the impact of lockdown on employment.\n\n\nI also deployed the project as a web application using Streamlit, which provides an interactive and user-friendly interface for exploring the analysis results.\n\nPlease refer to the Jupyter Notebook file (_unemploymentAnalysis.ipynb_) for a detailed step-by-step analysis and visualization code.\n\nIf you have any questions or suggestions, feel free to reach out.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakebshamsi%2Funemployement-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshakebshamsi%2Funemployement-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshakebshamsi%2Funemployement-analysis/lists"}