{"id":29913739,"url":"https://github.com/punitkumar4871/backpack_price_prediction","last_synced_at":"2026-02-09T04:38:22.764Z","repository":{"id":305013380,"uuid":"1021636657","full_name":"punitkumar4871/Backpack_Price_Prediction","owner":"punitkumar4871","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-17T17:46:29.000Z","size":424,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-17T20:41:37.148Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/punitkumar4871.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}},"created_at":"2025-07-17T17:45:48.000Z","updated_at":"2025-07-17T17:46:32.000Z","dependencies_parsed_at":"2025-07-17T23:24:21.068Z","dependency_job_id":null,"html_url":"https://github.com/punitkumar4871/Backpack_Price_Prediction","commit_stats":null,"previous_names":["punitkumar4871/backpack_price_prediction"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/punitkumar4871/Backpack_Price_Prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/punitkumar4871%2FBackpack_Price_Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/punitkumar4871%2FBackpack_Price_Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/punitkumar4871%2FBackpack_Price_Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/punitkumar4871%2FBackpack_Price_Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/punitkumar4871","download_url":"https://codeload.github.com/punitkumar4871/Backpack_Price_Prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/punitkumar4871%2FBackpack_Price_Prediction/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268326739,"owners_count":24232496,"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","status":"online","status_checked_at":"2025-08-02T02:00:12.353Z","response_time":74,"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":[],"created_at":"2025-08-02T02:14:53.777Z","updated_at":"2026-02-09T04:38:17.737Z","avatar_url":"https://github.com/punitkumar4871.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# `Backpack_Price_Prediction`\n\n### `Backpack Price Prediction: Kaggle Competition`\n#### `Overview`\nThis repository contains our submission for the Kaggle Competition: Backpack Price Prediction. The goal was to predict backpack prices based on various features using a structured machine learning approach. We performed exploratory data analysis, feature engineering, and trained multiple models to achieve accurate predictions. This project demonstrates end-to-end data science workflows, from preprocessing to model evaluation.\n\n`Data Analysis \u0026 Preprocessing`\nWe conducted thorough data preparation to ensure high-quality inputs for modeling:\n\nIn-depth exploratory data analysis (EDA), including univariate and bivariate analysis to understand distributions and relationships.\n\nDetection and handling of null values for data consistency.\n\nCreation of additional columns for enhanced visualizations and insights.\n\nFeature engineering by extracting meaningful information from existing attributes.\n\nAnalysis of correlations and relationships between variables to identify relevant features.\n\nDropping unnecessary columns to reduce noise and improve model performance.\n\n`Model Training \u0026 Evaluation`\nWe experimented with various machine learning models and evaluated them using Mean Absolute Error (MAE):\n\n## Model\tEmoji\tMAE\n`Linear Regression\t🏹\t39.18`\n`Ridge Regression\t🏔️\t39.18`\n`Lasso Regression\t🔗\t39.19`\n`Elastic Net Regression\t⚡\t39.19`\n`Decision Tree\t🌳\t59.43`\n`Random Forest\t🌲\t43.93`\n`K-Means Clustering\t📌\t89.14`\n`K-Nearest Neighbors (KNN)\t👥\t42.95`\n`XGBoost\t🚀\t39.33`\n\n## Insights \u0026 Learnings\nLinear, Ridge, and Lasso regressions performed the best with an MAE of approximately 39.18, effectively capturing data relationships.\n\nXGBoost was highly competitive at 39.33, showcasing its strength in structured data tasks.\n\nDecision Tree (59.43) and K-Means (89.14) showed higher errors, possibly due to overfitting or unsuitability for regression.\n\nFeature engineering and careful column selection were pivotal in boosting overall model performance.\n\n`Next Steps`\nFine-tune hyperparameters for further improvements.\n\nExplore ensemble techniques to enhance generalization.\n\nExperiment with deep learning models for potential gains.\n\n`Requirements`\nPython 3.x\n\n`Libraries: pandas, scikit-learn, xgboost, matplotlib/seaborn (for EDA and visualizations)`\n\nJupyter Notebook or similar for running the code\n\n## `Installation and Setup`\nClone this repository.\n\nInstall dependencies: pip install -r requirements.txt (if provided, or install manually).\n\nDownload the Kaggle dataset and place it in the data/ folder.\n\nOpen the Jupyter Notebook to explore the analysis.\n\n## `Usage`\nRun the notebook cells sequentially to reproduce the EDA, preprocessing, and model training.\n\nAdjust parameters in the code for custom experiments.\n\nSubmit predictions to Kaggle using the generated output.\n\n## `Acknowledgments`\nA huge shoutout to my amazing teammates Vaibhav Tamang and Yash Bhardwaj for their dedication and collaboration. Their expertise and teamwork made this project a success! \n\n## `Contributing`\nFeel free to fork the repo, suggest improvements, or submit pull requests for new features or optimizations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpunitkumar4871%2Fbackpack_price_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpunitkumar4871%2Fbackpack_price_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpunitkumar4871%2Fbackpack_price_prediction/lists"}