{"id":25905354,"url":"https://github.com/djdhairya/electronic-search-engine","last_synced_at":"2026-04-13T00:04:51.193Z","repository":{"id":279473253,"uuid":"938929125","full_name":"djdhairya/Electronic-Search-Engine","owner":"djdhairya","description":null,"archived":false,"fork":false,"pushed_at":"2025-02-25T18:18:31.000Z","size":2073,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-10T05:54:18.396Z","etag":null,"topics":["bytesio","countervector","nltk","numpy","pandas","pickel","pillow","sklearn","streamlit"],"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/djdhairya.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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":"2025-02-25T18:08:57.000Z","updated_at":"2025-02-25T18:18:35.000Z","dependencies_parsed_at":"2025-02-26T00:15:14.437Z","dependency_job_id":null,"html_url":"https://github.com/djdhairya/Electronic-Search-Engine","commit_stats":null,"previous_names":["djdhairya/electronic-search-engine"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/djdhairya/Electronic-Search-Engine","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djdhairya%2FElectronic-Search-Engine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djdhairya%2FElectronic-Search-Engine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djdhairya%2FElectronic-Search-Engine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djdhairya%2FElectronic-Search-Engine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djdhairya","download_url":"https://codeload.github.com/djdhairya/Electronic-Search-Engine/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djdhairya%2FElectronic-Search-Engine/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":280308521,"owners_count":26308494,"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":"2025-10-21T02:00:06.614Z","response_time":58,"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":["bytesio","countervector","nltk","numpy","pandas","pickel","pillow","sklearn","streamlit"],"created_at":"2025-03-03T05:14:53.841Z","updated_at":"2025-10-21T18:04:09.973Z","avatar_url":"https://github.com/djdhairya.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Electronic Search Engine\r\n\r\n## Overview\r\n\r\nThe **Electronic Search Engine** is a recommendation system built using Streamlit that helps users find similar electronic products based on their selection. The system leverages a precomputed similarity matrix to provide recommendations.\r\n\r\n## Features\r\n\r\n- User-friendly Streamlit interface.\r\n- Provides top 10 similar products based on selected electronic items.\r\n- Displays product names and images for better visualization.\r\n- Uses a precomputed similarity matrix to ensure quick recommendations.\r\n- Handles image validation before displaying recommendations.\r\n\r\n## Installation\r\n\r\n### Prerequisites\r\n\r\n\r\nEnsure you have the following installed:\r\n\r\n- Python 3.x\r\n- Required Python libraries: `streamlit`, `pandas`, `numpy`, `nltk`, `scikit-learn`, `pickle`, `PIL`, `requests`\r\n\r\n### Setup\r\n\r\n1. Clone the repository or download the project files.\r\n2. Install the required dependencies:\r\n   ```bash\r\n   pip install streamlit pandas numpy nltk scikit-learn pillow requests\r\n   ```\r\n3. Place the `data.pkl` and `similarity.pkl` files inside the `model` directory.\r\n\r\n## Usage\r\n\r\nRun the application using Streamlit:\r\n\r\n```bash\r\nstreamlit run app.py\r\n```\r\n\r\n## File Structure\r\n\r\n```\r\nElectronic Search Engine/\r\n│── model/\r\n│   ├── data.pkl\r\n│   ├── similarity.pkl\r\n│── data/\r\n│   ├── All Electronics.csv\r\n│── app.py\r\n│── test.py\r\n│── electronic_search.ipynb\r\n│── README.md\r\n```\r\n\r\n- `app.py`: Main Streamlit application for product search and recommendations.\r\n- `test.py`: Additional script for testing image validity before displaying recommendations.\r\n- `model/data.pkl`: Pickle file containing electronic product data.\r\n- `model/similarity.pkl`: Precomputed similarity matrix for recommendations.\r\n- `model_training.py`: Script for processing data, generating similarity matrix, and saving model files.\r\n\r\n## How It Works\r\n\r\n1. **Data Preprocessing**:\r\n\r\n   - Load electronic product data from CSV.\r\n   - Clean and preprocess data (handle missing values, remove duplicates, tokenize text, apply stemming, etc.).\r\n   - Convert text data into a numerical format using Count Vectorization.\r\n   - Compute similarity scores using Cosine Similarity.\r\n\r\n2. **Recommendation System**:\r\n\r\n   - User selects a product from the dropdown.\r\n   - The application fetches the top 10 most similar products based on precomputed similarity.\r\n   - The recommended products are displayed with images and names.\r\n\r\n## Example Output\r\n\r\n- **User selects**: *iPhone 12*\r\n- **Recommended products**:\r\n  - iPhone 12 Pro\r\n  - iPhone 11\r\n  - Samsung Galaxy S21\r\n  - etc.\r\n\r\n## Future Enhancements\r\n\r\n- Implement a search bar for quick product lookup.\r\n- Improve similarity calculations using deep learning models.\r\n- Add a feedback mechanism to improve recommendations.\r\n\r\n## License\r\n\r\nThis project is open-source under the MIT License.\r\n\r\n![Screenshot 2025-02-25 234705](https://github.com/user-attachments/assets/25c962ae-b462-48d2-86e7-becbdd46d83f)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjdhairya%2Felectronic-search-engine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjdhairya%2Felectronic-search-engine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjdhairya%2Felectronic-search-engine/lists"}