{"id":19606402,"url":"https://github.com/simran2911/flight-price-pridiction","last_synced_at":"2026-04-21T10:01:51.695Z","repository":{"id":248545972,"uuid":"828589138","full_name":"Simran2911/Flight-Price-Pridiction","owner":"Simran2911","description":"This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.","archived":false,"fork":false,"pushed_at":"2024-07-15T16:53:30.000Z","size":942,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-26T16:49:41.601Z","etag":null,"topics":["accuracy-score","catboostregressor","machine-learning","matplotlib","numpy","pandas","r2-score","random-forest-regression","seaborn","xgboost-regression"],"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/Simran2911.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":"2024-07-14T15:47:58.000Z","updated_at":"2024-07-15T16:55:33.000Z","dependencies_parsed_at":null,"dependency_job_id":"9577a778-c1fa-4890-887b-215d84930793","html_url":"https://github.com/Simran2911/Flight-Price-Pridiction","commit_stats":null,"previous_names":["simran2911/flight-price-pridiction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Simran2911/Flight-Price-Pridiction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simran2911%2FFlight-Price-Pridiction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simran2911%2FFlight-Price-Pridiction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simran2911%2FFlight-Price-Pridiction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simran2911%2FFlight-Price-Pridiction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Simran2911","download_url":"https://codeload.github.com/Simran2911/Flight-Price-Pridiction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Simran2911%2FFlight-Price-Pridiction/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32086815,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-21T06:27:27.065Z","status":"ssl_error","status_checked_at":"2026-04-21T06:27:21.250Z","response_time":128,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["accuracy-score","catboostregressor","machine-learning","matplotlib","numpy","pandas","r2-score","random-forest-regression","seaborn","xgboost-regression"],"created_at":"2024-11-11T10:05:15.433Z","updated_at":"2026-04-21T10:01:51.680Z","avatar_url":"https://github.com/Simran2911.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Project Overview:\n\nThe goal of this project is to develop a predictive model that estimates future flight prices based on historical data and various influencing factors.\n\n\n## Objectives/Buisness Goals:\n\n\n- Predict flight prices for specific routes.\n- Analyze factors influencing flight price fluctuations.\n- Provide actionable insights for travelers.\n\n## Data Preprocessing:\n\n- Data Cleaning: Handle missing values, outliers, and inconsistencies.\n- Feature Engineering: Create features such as:\n   \n      1. Day of the week.\n\n      2. Time of booking (lead time).\n\n      3. Airline Preffered.\n\n      4. Number of stops.\n\n\n## Exploratory Data Analysis (EDA):\n\n- Visualize price trends With number of stops.\n - Analyze correlation between features and price.\n- Identify seasonal patterns and price volatility.     \n- Identify Price variation with Source and Destination.\n\n## Model Selection:\n- Regression Models\n     \n       1. Extratree Regression\n       2. Random Forest.\n       3. Catboost Regression\n\nGradient Boosting (XGBoost, LightGBM).\n\n## Model Evaluation:\n- Split data into training and test sets (e.g., 80/20).\n- Use metrics like RMSE, MAE, and R² to evaluate model performance.\n\n## Accuracy:\n\n\n| Model Name             |Accuracy                                                               |\n| ----------------- | ------------------------------------------------------------------ |\n| Random Forest Regression | 0.8532074703106208 |\n| Extratree Regressor | 0.7890353681268577|\n|Catboost Regressor | 0.8596289688357996 |\n\n\n\n## Deployment:\n- Create a web application Flask serve predictions.\n\n## Future Work:\n- Incorporate real-time data for ongoing price updates.\n- Explore deep learning models for improved accuracy.\n- Develop a user interface for travelers to input parameters and get price predictions.\n## Tools and Technologies:\n - Programming Languages: Python\n- Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.\n- Database: SQL for storing historical data.\n- Web Framework: Flask, Django for the application.\n\n## Conclusion:\nThis project aims to empower travelers with predictive insights, helping them make informed decisions and potentially save on flight costs.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimran2911%2Fflight-price-pridiction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsimran2911%2Fflight-price-pridiction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimran2911%2Fflight-price-pridiction/lists"}