{"id":19321480,"url":"https://github.com/burhanahmed1/CompuWorth","last_synced_at":"2025-12-24T16:58:57.926Z","repository":{"id":247027456,"uuid":"824844088","full_name":"burhanahmed1/LaptopPricing-MachineLearning-Analysis","owner":"burhanahmed1","description":"Data Analysis, training Machine Learning models, and Model Evaluation and Refinement for LaptopPricing dataset.","archived":false,"fork":false,"pushed_at":"2024-07-06T14:00:52.000Z","size":98,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-29T14:51:11.385Z","etag":null,"topics":["data-analysis-project","data-analytics-project","data-aquisition","data-wrangling","datascience","exploratory-data-analysis","insights","jupyter-notebook","machine-learning","machine-learning-models","matplotlib","model-evaluation-and-refinement","numpy","pandas","python","scikit-learn","scipy-stats","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/burhanahmed1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-07-06T05:26:42.000Z","updated_at":"2025-01-12T09:57:07.000Z","dependencies_parsed_at":"2025-01-06T05:27:43.548Z","dependency_job_id":"577f112f-8c36-40fb-bd7a-d9ceedd31dff","html_url":"https://github.com/burhanahmed1/LaptopPricing-MachineLearning-Analysis","commit_stats":null,"previous_names":["burhanahmed1/laptoppricing-machinelearning-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FLaptopPricing-MachineLearning-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FLaptopPricing-MachineLearning-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FLaptopPricing-MachineLearning-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burhanahmed1%2FLaptopPricing-MachineLearning-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/burhanahmed1","download_url":"https://codeload.github.com/burhanahmed1/LaptopPricing-MachineLearning-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250237798,"owners_count":21397399,"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-analysis-project","data-analytics-project","data-aquisition","data-wrangling","datascience","exploratory-data-analysis","insights","jupyter-notebook","machine-learning","machine-learning-models","matplotlib","model-evaluation-and-refinement","numpy","pandas","python","scikit-learn","scipy-stats","seaborn"],"created_at":"2024-11-10T01:37:26.683Z","updated_at":"2025-12-24T16:58:57.921Z","avatar_url":"https://github.com/burhanahmed1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CompuWorth: Hardware–Price Regression for Laptop Valuation\n\n## Introduction\nThis repository contains the analysis and machine learning model implementation for the laptop-pricing dataset. The goal is to predict various price of laptops having various attributes using different machine learning techniques.\n\n## Table of Contents\n1. Data Import and Cleaning\n2. Exploratory Data Analysis (EDA)\n3. Model Evaluation\n4. Over-fitting, Under-fitting, and Model Selection\n5. Ridge Regression\n6. Grid Search\n\n## Technologies Used\n- **Programming Language**: Python\n- **Libraries**: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn\n- **Tools**: Jupyter Notebook\n\n## Getting Started\nTo get started with this project, clone the repository and install the necessary dependencies:\n```bash\ngit clone https://github.com/burhanahmed1/LaptopPricing-MachineLearning-Analysis.git\ncd LaptopPricing-MachineLearning-Analysis\npip install -r requirements.txt\n```\n\n## Usage\nOpen the Jupyter notebook:\n```bash\njupyter notebook LaptopPricing-ML.ipynb\n```\n\n## Dataset\nThe dataset used in this analysis is LaptopPricing.csv, which contains various features related to laptops such as CPU_frequency, RAM_GB, Storage_GB_SSD , CPU_core , OS , GPU, Category and price.\n\n## R^2 scores\n\u003cdiv align=\"center\"\u003e\n  \n  **R^2** scores of the **Linear Regression** model created using different degrees of polynomial features, ranging from 1 to 5.\n  \u003cimg src=\"src/R2_1.png\" alt=\"R2_polynomial-features\" width=\"800\"/\u003e\n  \n  **R^2** values of **Ridge Regression** model for training and testing sets with respect to the values of alpha.\n  \u003cimg src=\"src/R2_2.png\" alt=\"R2_for-alphas\" width=\"800\"/\u003e\n\u003c/div\u003e\n\n## Contributing\nContributions are welcome! Please fork this repository and submit pull requests.\n\n## License\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburhanahmed1%2FCompuWorth","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fburhanahmed1%2FCompuWorth","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburhanahmed1%2FCompuWorth/lists"}