{"id":29926504,"url":"https://github.com/kasraskari/tumor-predict","last_synced_at":"2026-04-09T11:03:05.072Z","repository":{"id":307740215,"uuid":"1030567865","full_name":"KasrAskari/Tumor-Predict","owner":"KasrAskari","description":"Streamlit app for predicting tumor malignancy using logistic regression.","archived":false,"fork":false,"pushed_at":"2025-08-01T21:55:36.000Z","size":66,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-01T23:39:04.524Z","etag":null,"topics":["logistic-regression","machine-learning","numpy","pandas","python","scikit-learn","streamlit","tumor-detection"],"latest_commit_sha":null,"homepage":"","language":"Python","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/KasrAskari.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,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-01T21:45:17.000Z","updated_at":"2025-08-01T21:55:40.000Z","dependencies_parsed_at":"2025-08-01T23:39:07.176Z","dependency_job_id":"9b5fd796-24e1-415c-aa40-7fa5983c58e0","html_url":"https://github.com/KasrAskari/Tumor-Predict","commit_stats":null,"previous_names":["kasraskari/tumor-predict"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/KasrAskari/Tumor-Predict","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasrAskari%2FTumor-Predict","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasrAskari%2FTumor-Predict/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasrAskari%2FTumor-Predict/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasrAskari%2FTumor-Predict/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KasrAskari","download_url":"https://codeload.github.com/KasrAskari/Tumor-Predict/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasrAskari%2FTumor-Predict/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268391992,"owners_count":24243296,"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":"2025-08-02T02:00:12.353Z","response_time":74,"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":["logistic-regression","machine-learning","numpy","pandas","python","scikit-learn","streamlit","tumor-detection"],"created_at":"2025-08-02T12:41:32.560Z","updated_at":"2025-12-30T21:51:31.133Z","avatar_url":"https://github.com/KasrAskari.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🧠 Tumor Malignancy Predictor\r\n\r\nA simple yet powerful web application that predicts the likelihood of a tumor being malignant or benign using a logistic regression model trained on selected features from a breast cancer dataset.\r\n\r\n## 💻 Overview\r\n\r\nThis is a Streamlit application where you can input tumor characteristics such as radius, perimeter, area, compactness and concavity to obtain a probability score on whether the tumor is malignant. The model is trained with logistic regression with hyper-parameter tuning on GridSearchCV.\r\n\r\n## 🔍 Features\r\n\r\n* Clean UI with interactive inputs powered by Streamlit\r\n* Logistic regression with 10-fold cross-validation\r\n* Hyperparameter tuning using GridSearchCV\r\n* Probability-based prediction with visual feedback\r\n* Built-in data preprocessing (feature scaling)\r\n\r\n## 🏗️ Project Structure\r\n\r\n```\r\n├── tumor-app.py          # Streamlit application script\r\n├── data.csv              # Dataset with tumor records\r\n├── README.md             # Project documentation\r\n```\r\n\r\n## 🛠️ Technologies Used\r\n\r\n* **Python 3**\r\n* **Pandas**\r\n* **NumPy**\r\n* **Scikit-learn**\r\n* **Streamlit**\r\n\r\n## 📈 Results\r\n\r\n* **Model:** Logistic Regression\r\n* **Best Hyperparameter (C):** Determined via GridSearchCV\r\n* **Cross-Validated Accuracy:** Displayed in-app after training\r\n\r\n## 📂 Dataset\r\n\r\nThe dataset used in this project is the **Breast Cancer Wisconsin (Diagnostic) Data Set**, publicly available on Kaggle.\r\n🔗 [Click here to download the dataset](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?resource=download)\r\n\r\nMake sure to download and rename the dataset to `data.csv`, and place it in the same directory as the app script.\r\n\r\nExpected columns used in this project:\r\n\r\n```\r\nradius_mean, perimeter_mean, area_mean, compactness_mean, concavity_mean, diagnosis\r\n```\r\n\r\n## ▶️ How to Run\r\n\r\n1. **Install Dependencies** (preferably inside a virtual environment):\r\n\r\n```bash\r\npip install streamlit pandas numpy scikit-learn\r\n```\r\n\r\n2. **Download the Dataset** from the [Kaggle link above](https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?resource=download)\r\n   and save it as `data.csv`.\r\n\r\n3. **Run the App:**\r\n\r\n```bash\r\nstreamlit run tumor-app.py\r\n```\r\n\r\n## 📜 License\r\n\r\n\r\nThis project is licensed under the MIT License.\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkasraskari%2Ftumor-predict","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkasraskari%2Ftumor-predict","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkasraskari%2Ftumor-predict/lists"}