{"id":24970201,"url":"https://github.com/shabari48/mobile_project","last_synced_at":"2026-04-28T08:37:58.170Z","repository":{"id":275143669,"uuid":"925151579","full_name":"shabari48/mobile_project","owner":"shabari48","description":" An ML-powered system that predicts mobile phone prices and provides personalized recommendations based on user preferences.","archived":false,"fork":false,"pushed_at":"2025-02-10T04:22:48.000Z","size":1647,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T05:24:52.226Z","etag":null,"topics":["exploratory-data-analysis","kmeans-clustering","machine-learning","streamlit","support-vector-machines","xgboost-regression"],"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/shabari48.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":"2025-01-31T10:30:06.000Z","updated_at":"2025-02-10T04:22:51.000Z","dependencies_parsed_at":"2025-01-31T13:38:57.235Z","dependency_job_id":"11c7fe2b-04d1-4484-8363-0677a320eb05","html_url":"https://github.com/shabari48/mobile_project","commit_stats":null,"previous_names":["shabari48/mobile_project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shabari48%2Fmobile_project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shabari48%2Fmobile_project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shabari48%2Fmobile_project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shabari48%2Fmobile_project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shabari48","download_url":"https://codeload.github.com/shabari48/mobile_project/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246140588,"owners_count":20729802,"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":["exploratory-data-analysis","kmeans-clustering","machine-learning","streamlit","support-vector-machines","xgboost-regression"],"created_at":"2025-02-03T15:56:34.065Z","updated_at":"2026-04-28T08:37:58.115Z","avatar_url":"https://github.com/shabari48.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Mobile Price Prediction and Recommendation System\n\n## Overview\n\nThis project aims to predict the price of mobile phones based on their specifications and recommend similar phones using clustering techniques. The system uses a regression model to predict the price and a classification model to categorize the phones into different categories (Budget, Midrange, Flagship). Additionally, it employs KMeans clustering to find similar phones based on the predicted price and input data.\n\n## Project Structure\n\n```plaintext\n\nmobile_project/\n│\n├── data/\n│   ├── processed/\n│   │   └── unique.csv\n│   └── raw/\n│\n├── models/\n│   ├── myreg.joblib\n│   ├── myclassify.joblib\n│   ├── mainscaler.joblib\n│   └── kmeanscaler.joblib\n│\n├── notebooks/\n│   ├── datatransformation.ipynb\n│   ├── eda.ipynb\n│   └── model.ipynb\n│\n├── src/\n│   └── app.py\n│\n├── LICENSE\n└── README.md\n\n```\n\n## Setup\n\n### Prerequisites\n\n- Python 3.x\n- Jupyter Notebook\n- Streamlit\n- Pandas\n- Scikit-learn\n- Joblib\n\n### Installation\n\n1. **Clone the Repository**\n\n   ```bash\n   git clone https://github.com/yourusername/mobile_project.git\n   cd mobile_project\n   ```\n\n2. **Create a Virtual Environment**\n\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n   ```\n\n3. **Install Dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Data\n\nThe dataset used in this project is stored in the `data/processed/` directory. The dataset contains various features of mobile phones, including battery power, RAM, memory, processor performance, camera specifications, and price.\n\n## Models\n\nThe project uses the following models:\n\n- **Regression Model**: Trained to predict the price of mobile phones based on their specifications.\n- **Classification Model**: Trained to categorize mobile phones into different categories (Budget, Midrange, Flagship).\n- **KMeans Clustering**: Used to find similar phones based on the predicted price and input data.\n\nThe models and scalers are saved in the `models/` directory.\n\n## Notebooks\n\nThe `notebooks/` directory contains Jupyter notebooks for data transformation, exploratory data analysis (EDA), and model training.\n\n- **datatransformation.ipynb**: Contains the code for data cleaning and transformation.\n- **eda.ipynb**: Contains the code for exploratory data analysis.\n- **model.ipynb**: Contains the code for training the regression and classification models.\n\n## Streamlit App\n\nThe `src/app.py` file contains the code for the Streamlit application. The app allows users to input the specifications of a mobile phone and predict its price. It also recommends similar phones based on the predicted price and input data.\n\n### Running the Streamlit App\n\n1. **Activate the Virtual Environment**\n\n   ```bash\n   source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n   ```\n\n2. **Run the Notebook File**\n\n     ```bash\n   model.ipynb\n   ```\n\n3. **Run the Streamlit App**\n\n   ```bash\n   streamlit run src/app.py\n   ```\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.\n\n## Contact\n\nFor any questions or suggestions, please feel free to contact us at [shabariprakashsv@gmail.com](mailto:shabariprakashsv@gmail.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshabari48%2Fmobile_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshabari48%2Fmobile_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshabari48%2Fmobile_project/lists"}