{"id":19963992,"url":"https://github.com/philippe2023/startup-success-predictor","last_synced_at":"2026-04-10T07:45:30.697Z","repository":{"id":259960766,"uuid":"878510975","full_name":"philippe2023/startup-success-predictor","owner":"philippe2023","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-29T09:51:48.000Z","size":769,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-01T17:12:50.670Z","etag":null,"topics":["jupyterlab","linear-regression","machine-learning","numpy","pandas","python3","scikit-learn","streamlit"],"latest_commit_sha":null,"homepage":"","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/philippe2023.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":"2024-10-25T14:24:34.000Z","updated_at":"2024-10-30T13:58:02.000Z","dependencies_parsed_at":"2024-10-28T20:35:13.306Z","dependency_job_id":"d4e5c379-b14f-4b04-b034-6fadb10c714b","html_url":"https://github.com/philippe2023/startup-success-predictor","commit_stats":null,"previous_names":["philippe2023/startup-success-predictor"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/philippe2023/startup-success-predictor","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philippe2023%2Fstartup-success-predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philippe2023%2Fstartup-success-predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philippe2023%2Fstartup-success-predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philippe2023%2Fstartup-success-predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/philippe2023","download_url":"https://codeload.github.com/philippe2023/startup-success-predictor/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/philippe2023%2Fstartup-success-predictor/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262235782,"owners_count":23279566,"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":["jupyterlab","linear-regression","machine-learning","numpy","pandas","python3","scikit-learn","streamlit"],"created_at":"2024-11-13T02:18:25.152Z","updated_at":"2026-04-10T07:45:25.670Z","avatar_url":"https://github.com/philippe2023.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Startup Success Predictor\n\nThis project is a **Streamlit application** that predicts the success likelihood of a startup based on funding and company characteristics. The model is built with a **Random Forest Classifier** and enhanced with data preprocessing using **SMOTE** for balanced training.\n\n## Features\n- **Predictive Model**: The app uses a Random Forest Classifier to predict whether a startup is likely to succeed.\n- **User Input Options**: Allows users to input startup information such as funding rounds, total funding, valuation, and whether it has received various types of funding (VC, Angel, etc.).\n- **Probability Output**: Displays the prediction along with the success and failure probabilities.\n\n## Files and Directories\n- `app/app.py`: Main Streamlit app file.\n- `model/model_with_scaler_and_encoder.pkl`: Contains the saved model, scaler, label encoder, and feature names.\n- `notebook/workbook_1.ipynb`: Contains the working notebook\n- `README.md`: Project overview (this file).\n\n## Installation and Setup\n\n### Prerequisites\n- Python 3.7+\n- Required Python packages (see `requirements.txt`)\n\n### Installation\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/philippe2023/startup-success-predictor.git\n    cd startup-success-predictor\n    ```\n\n2. Install dependencies:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n### Running the Application\nTo start the app, use the following command in the terminal:\n```bash\nstreamlit run app/app.py\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilippe2023%2Fstartup-success-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphilippe2023%2Fstartup-success-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilippe2023%2Fstartup-success-predictor/lists"}