{"id":26776952,"url":"https://github.com/pandaiscodingcpu/carcalcx","last_synced_at":"2025-03-29T04:37:19.906Z","repository":{"id":279562697,"uuid":"938720306","full_name":"pandaiscodingcpu/CarCalcX","owner":"pandaiscodingcpu","description":"A linear regression model created using Sklearn, pandas and numpy to predict the car prize using parameters such as Brand, Fuel type, age etc.","archived":false,"fork":false,"pushed_at":"2025-03-15T14:31:14.000Z","size":397,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T15:29:16.669Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/pandaiscodingcpu.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":"2025-02-25T11:54:31.000Z","updated_at":"2025-03-15T14:31:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"14678e3f-732d-4b2a-be0b-20db6919d973","html_url":"https://github.com/pandaiscodingcpu/CarCalcX","commit_stats":null,"previous_names":["pandaiscodingcpu/carcalcx"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pandaiscodingcpu%2FCarCalcX","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pandaiscodingcpu%2FCarCalcX/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pandaiscodingcpu%2FCarCalcX/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pandaiscodingcpu%2FCarCalcX/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pandaiscodingcpu","download_url":"https://codeload.github.com/pandaiscodingcpu/CarCalcX/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246140573,"owners_count":20729797,"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":[],"created_at":"2025-03-29T04:37:19.301Z","updated_at":"2025-03-29T04:37:19.898Z","avatar_url":"https://github.com/pandaiscodingcpu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CarCalcX\nA linear regression model created using Sklearn, pandas and numpy to predict the car prize using parameters such as Brand, Fuel type, age etc.\n\n\n# Libraries used and Workflow  \n\npandas: For data analysis , used https://www.kaggle.com/datasets/taeefnajib/used-car-price-prediction-dataset from kaggle  \nScikit-Learn: To train a linear regression model  \nNumpy: To use log transformation on large price values in the dataset   \nPickle (with AI): To save the model  \nStreamlit: Used Streamlit to deploy the model but due to some technical issues the model has not been completely deployed.  \n\n\n# Additional changes  \nUsed feature engineering to reduce RMSE,MSE,R(square) on the model  \n📊 Model Evaluation AFTER Feature Engineering:  \n\nMAE  : 0.32  \n\nMSE  : 0.19  \n\nRMSE : 0.44  \n\nR² Score : 0.7306    \n\n# STEPS TO USE THE MODEL  \nSTEP 1: Download the dataset  \n\nSTEP 2: Download all the reuqired libraries  \n\nSTEP 3: Run the two files 1. data_gathering.ipynb and 2. final_dataset.ipynb in jupyter notebook  \n\nSTEP 4: using app.py type the command in terminal: streamlit run app.py  \n\nSTEP 5: You will see the web interface follow further commnands to use the model.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpandaiscodingcpu%2Fcarcalcx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpandaiscodingcpu%2Fcarcalcx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpandaiscodingcpu%2Fcarcalcx/lists"}