{"id":23589093,"url":"https://github.com/akarshankapoor7/dynamic_pricing_model_in-python","last_synced_at":"2025-11-03T21:30:30.809Z","repository":{"id":267473852,"uuid":"901363168","full_name":"akarshankapoor7/Dynamic_Pricing_model_in-python","owner":"akarshankapoor7","description":"Dynamic Ride Pricing App This is a Streamlit web application designed to implement a dynamic pricing model for ride-sharing platforms. ","archived":false,"fork":false,"pushed_at":"2024-12-10T14:27:43.000Z","size":1792,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-27T06:12:59.265Z","etag":null,"topics":["data-science","dynamic-pricing","plotly-python","random-forest-regressor","streamlit-application","supply-chain"],"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/akarshankapoor7.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":"2024-12-10T14:18:47.000Z","updated_at":"2024-12-10T14:45:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"0cdaa6b2-6919-4a73-b38f-39c4d5536155","html_url":"https://github.com/akarshankapoor7/Dynamic_Pricing_model_in-python","commit_stats":null,"previous_names":["akarshankapoor7/dynamic_pricing_model_in-python"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akarshankapoor7%2FDynamic_Pricing_model_in-python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akarshankapoor7%2FDynamic_Pricing_model_in-python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akarshankapoor7%2FDynamic_Pricing_model_in-python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/akarshankapoor7%2FDynamic_Pricing_model_in-python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/akarshankapoor7","download_url":"https://codeload.github.com/akarshankapoor7/Dynamic_Pricing_model_in-python/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239419545,"owners_count":19635405,"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":["data-science","dynamic-pricing","plotly-python","random-forest-regressor","streamlit-application","supply-chain"],"created_at":"2024-12-27T06:12:49.565Z","updated_at":"2025-11-03T21:30:30.770Z","avatar_url":"https://github.com/akarshankapoor7.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\nDynamic Ride Pricing App\n\nThis is a Streamlit web application designed to implement a dynamic pricing model for ride-sharing platforms. Users can enter different parameters, and the app will predict the ride fare using a trained Random Forest Regressor model. The application also features visualizations that compare the predicted prices with the actual fares, offering insights into the model's accuracy.\n\n-Predict ride prices based on user inputs such as the number of riders, number of drivers, vehicle type, and expected ride duration.\n\n-Visualize predicted ride prices vs. actual values using interactive Plotly graphs.\n\n-Analyze the distribution of profitable and loss-making rides for valuable insights.\n\n-Handle missing data through effective preprocessing techniques.\n\n-Use a Random Forest Regressor model to generate accurate price predictions.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakarshankapoor7%2Fdynamic_pricing_model_in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakarshankapoor7%2Fdynamic_pricing_model_in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakarshankapoor7%2Fdynamic_pricing_model_in-python/lists"}