{"id":50784601,"url":"https://github.com/sedefkjamili/dengai-ml-prediction","last_synced_at":"2026-06-12T06:32:30.721Z","repository":{"id":357682065,"uuid":"1238086356","full_name":"sedefkjamili/dengai-ml-prediction","owner":"sedefkjamili","description":"Machine learning project for predicting dengue fever outbreaks using climate and environmental data.","archived":false,"fork":false,"pushed_at":"2026-05-13T21:17:29.000Z","size":9924,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-05-13T22:13:57.641Z","etag":null,"topics":["data-science","dengue","gradient-boosting","healthcare","machine-learning","python","scikit-learn","time-series"],"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/sedefkjamili.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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-05-13T19:55:09.000Z","updated_at":"2026-05-13T21:17:33.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/sedefkjamili/dengai-ml-prediction","commit_stats":null,"previous_names":["sedefkjamili/dengai-ml-prediction"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/sedefkjamili/dengai-ml-prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sedefkjamili%2Fdengai-ml-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sedefkjamili%2Fdengai-ml-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sedefkjamili%2Fdengai-ml-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sedefkjamili%2Fdengai-ml-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sedefkjamili","download_url":"https://codeload.github.com/sedefkjamili/dengai-ml-prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sedefkjamili%2Fdengai-ml-prediction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34232790,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-12T02:00:06.859Z","response_time":109,"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":["data-science","dengue","gradient-boosting","healthcare","machine-learning","python","scikit-learn","time-series"],"created_at":"2026-06-12T06:32:29.339Z","updated_at":"2026-06-12T06:32:30.713Z","avatar_url":"https://github.com/sedefkjamili.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🦟 DengAI\nMachine learning project for predicting dengue fever outbreaks using climate and environmental data.\n\nDengAI is a machine learning project focused on predicting weekly dengue fever outbreaks using climate and environmental data. The project compares traditional regression techniques with ensemble learning methods to forecast dengue case counts for two tropical cities: San Juan (Puerto Rico) and Iquitos (Peru).\n\n## 🚀 Overview\n\nDengue fever is a mosquito-borne disease strongly influenced by environmental and climatic conditions such as temperature, humidity, precipitation, and vegetation. Early prediction of outbreaks can help public health organizations allocate resources more effectively and reduce the spread of disease.\n\nThis project uses machine learning models trained on historical climate and epidemiological data from the DrivenData DengAI competition dataset.\n\n## 🔍 Features\n\n### 📊 Data Preprocessing\n- Missing value imputation using city-specific averages\n- Time-aware train/validation split\n- Separate modeling for each city\n- Seasonal feature handling using `weekofyear`\n\n### 🔬 Feature Engineering\n- Climate and environmental indicators\n- Vegetation indices (NDVI)\n- Temperature and humidity variables\n- Time-series aware structure\n\n### 🧠 Machine Learning Models\n- Polynomial Regression (Baseline)\n- Gradient Boosting Regression\n- Time-series forecasting evaluation\n\n### 📈 Performance Evaluation\n- Mean Absolute Error (MAE)\n- Comparison between baseline and ensemble models\n- Forecasting horizon analysis\n- Feature importance visualization\n\n## 🗂️ Repository Structure\n\n```text\nDengAI/\n├── data/                  # Prediction outputs and datasets\n├── notebooks/             # Jupyter notebooks for training and analysis\n├── reports/               # Presentation slides and project report\n├── images/                # Figures, plots, and visualizations\n├── src/                   # Source code for preprocessing and modeling\n├── requirements.txt       # Python dependencies\n└── README.md              # Project documentation\n```\n\n## 🧪 Technologies \u0026 Libraries\n\n- Python 3.10+\n- scikit-learn\n- Pandas\n- NumPy\n- Matplotlib\n- Seaborn\n- Jupyter Notebook\n\n\n## ⚙️ Installation\n\nClone the repository:\n\n```bash\ngit clone https://github.com/YOUR_USERNAME/DengAI.git\n\ncd DengAI\n```\n\n##  Install dependencies:\n\n```bash\npip install -r requirements.txt\n🧠 Model Training \u0026 Evaluation\n```\n\nRun the notebook:\n\n```bash\njupyter notebook\n```\nor execute training scripts inside /src.\n\n## 📊 Results\nGradient Boosting Regression significantly outperformed Polynomial Regression.\nPolynomial models showed overfitting at higher degrees.\nBest performance was achieved using short-term forecasting horizons.\nEnvironmental variables such as temperature and vegetation indices were among the most influential features.\n\n## 📚 Dataset Source\n- [DrivenData – DengAI: Predicting Disease Spread](https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/)\n- Climate and environmental datasets for:\n  - San Juan, Puerto Rico\n  - Iquitos, Peru\n\n## 👩‍💻 Authors\n- Rabia Şevval Aydın\n- Sedef Kjamili\n\nIstanbul Technical University - Machine Learning Course Term Project\n\n## 📄 License\n\nThis project is for academic and educational purposes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsedefkjamili%2Fdengai-ml-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsedefkjamili%2Fdengai-ml-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsedefkjamili%2Fdengai-ml-prediction/lists"}