{"id":49818965,"url":"https://github.com/anto4k/flightanalysis_bigdataproject","last_synced_at":"2026-05-13T09:10:27.946Z","repository":{"id":325613767,"uuid":"1101815137","full_name":"Anto4K/FlightAnalysis_BigDataProject","owner":"Anto4K","description":"Flight data analysis and ML prediction platform powered by Apache Spark and Streamlit.","archived":false,"fork":false,"pushed_at":"2025-11-22T09:52:50.000Z","size":68,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-11-22T11:28:53.882Z","etag":null,"topics":["machine-learning","spark","spark-sql","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Python","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/Anto4K.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-11-22T09:40:54.000Z","updated_at":"2025-11-22T09:55:41.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/Anto4K/FlightAnalysis_BigDataProject","commit_stats":null,"previous_names":["anto4k/flightanalysis_bigdataproject"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Anto4K/FlightAnalysis_BigDataProject","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anto4K%2FFlightAnalysis_BigDataProject","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anto4K%2FFlightAnalysis_BigDataProject/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anto4K%2FFlightAnalysis_BigDataProject/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anto4K%2FFlightAnalysis_BigDataProject/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anto4K","download_url":"https://codeload.github.com/Anto4K/FlightAnalysis_BigDataProject/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anto4K%2FFlightAnalysis_BigDataProject/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32975316,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-13T06:31:55.726Z","status":"ssl_error","status_checked_at":"2026-05-13T06:31:51.336Z","response_time":115,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["machine-learning","spark","spark-sql","streamlit"],"created_at":"2026-05-13T09:10:26.910Z","updated_at":"2026-05-13T09:10:27.935Z","avatar_url":"https://github.com/Anto4K.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ✈️ Flight Analysis - Big Data Project\n\nIn-depth analysis and interactive visualization of flights in the United\nStates using Big Data technologies and Machine Learning.\n\n## 📚 Description\n\nThis project was developed as an activity for the course \"Models and\nTechniques for Big Data\" and focuses on the analysis of U.S. flight data\nfrom 2013, leveraging the capabilities of **Apache Spark** for\ndistributed processing and **Streamlit** for building interactive web\ndashboards.\\\nThe goal is to provide a user-friendly platform for exploring\nstatistics, delays, performance, and predictive models related to\nflights.\n\n## 📁 Project Structure\n\n    .\n    ├── spark/                      \n    │   ├── ml/                     \n    │   │   ├── classificatoreRandomForest.py\n    │   │   ├── clustering.py\n    │   │   └── metodiAux.py\n    │   │\n    │   ├── query/                  # Queries and analyses organized by topic\n    │   │   ├── aeroporti_analysis/\n    │   │   ├── dashboard_analysis/\n    │   │   ├── route/\n    │   │   ├── statistiche_annuali/\n    │   │   └── allQuery.py\n    │   │\n    │   └── utils/                  # Common utilities and support files\n    │       ├── citta_lat_long.csv\n    │       ├── create_session.py\n    │       ├── lista_nomi_aeroporti.txt\n    │       ├── preprocessing.py\n    │       └── utils.py\n    │\n    ├── pages/                      # Streamlit interface divided into sections\n    │   ├── 1_📊_Analisi_mensile.py       # Monthly data analysis\n    │   ├── 2_🔍_Ricerca_voli.py          # Flight search\n    │   ├── 3_🌍_Analisi_aeroporti.py     # City and airport statistics\n    │   ├── 4_📆_Analisi_annuale.py       # Aggregated annual analyses\n    │   ├── 5_🤖_Classificazione_ML.py    # Prediction with ML models\n    │   └── 6_🧩_Clustering_ML.py         # Segmentation with clustering\n    │\n    ├── data/                       # Input CSV datasets (not uploaded to GitHub)\n    │\n    ├── README.md\n    └── Home.py                      # Main entry point of the Streamlit app\n\n## ⚙️ Technologies and Libraries Used\n\n-   **Python**: main programming language\n-   **Apache Spark (PySpark)**: for distributed data processing\n-   **Streamlit**: for building the web interface\n-   **Plotly**: interactive charts (bar, pie, scatter, heatmaps)\n-   **Pydeck**: for geographic maps integrated in Streamlit\n-   **airportsdata**: to retrieve detailed airport data\n-   **Spark MLlib**: for Machine Learning algorithms (Random Forest,\n    K-Means)\n-   **pandas, numpy**: for support operations and local data\n    manipulation\n\n## 🚀 Running the Project\n\n1.  Clone the repository\\\n2.  Make sure you have Apache Spark and Python ≥ 3.8 installed\\\n3.  Install the required dependencies:\n\n``` bash\npip install streamlit plotly pydeck pandas numpy airportsdata\n```\n\n4.  Launch the application:\n\n``` bash\nstreamlit run Home.py\n```\n\n## 📊 Page Contents and Results\n\n### 1. 📈 Monthly Flight Analysis\n\n-   Metrics on flights, delays, distances, and duration\n-   Delay classification in 15-minute categories\n-   Flight status (on time, delayed, cancelled, diverted)\n-   Main causes of delays (airline, weather, NAS, security, late\n    aircraft)\n\n### 2. 🔍 Flight Search\n\n-   Custom search with interactive map and detailed information\n-   Separate visualization for on-time, delayed, cancelled, or diverted\n    flights\n\n### 3. 🌍 Cities and Airports\n\n-   Statistics by city: departures/arrivals, average delays, airports\n-   Most frequent destinations and interactive geographic map\n\n### 4. 📆 Annual Statistics\n\n-   Monthly distribution of flight statuses\n-   Weekly heatmap (days vs months)\n-   Map of the busiest cities\n\n### 5. 🤖 Delay Prediction (ML)\n\n-   **Random Forest** model for binary classification (delay \\\u003e15 min)\n-   Class balancing with under-sampling\n-   Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix\n\n### 6. 🧩 Flight Clustering\n\n-   **K-Means** algorithm with silhouette score\n-   Scatter plots and histograms for different *k* values\n\n------------------------------------------------------------------------\n\n## 👤 Author\n\n🎯 *Project developed by Rocco Pio Vardaro and Antonio Pio Francica as\npart of the course on Analysis and Techniques for Big Data*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanto4k%2Fflightanalysis_bigdataproject","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanto4k%2Fflightanalysis_bigdataproject","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanto4k%2Fflightanalysis_bigdataproject/lists"}