{"id":25501739,"url":"https://github.com/singhxtushar/diamondpriceprediction","last_synced_at":"2025-11-12T09:30:15.951Z","repository":{"id":207503184,"uuid":"719392922","full_name":"SINGHxTUSHAR/DiamondPricePrediction","owner":"SINGHxTUSHAR","description":"This is a DiamondPricePrediction project with fully automated pipelines, logging, exception handling and with setup module. 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It leverages machine learning techniques to analyze various features of diamonds and build a predictive model.\n\n## Table of Contents\n\n- [Features](#features)\n- [Requirements](#requirements)\n- [Setup](#setup)\n- [Usage](#usage)\n- [Data](#data)\n- [Models](#models)\n- [Results](#results)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Features :\nThe goal is to predict `price` of given diamond (Regression Analysis).\n\nThere are 10 independent variables (including `id`):\n\n* `id` : unique identifier of each diamond\n* `carat` : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.\n* `cut` : Quality of Diamond Cut\n* `color` : Color of Diamond\n* `clarity` : Diamond clarity is a measure of the purity and rarity of the stone, graded by the visibility of these characteristics under 10-power magnification.\n* `depth` : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface)\n* `table` : A diamond's table is the facet which can be seen when the stone is viewed face up.\n* `x` : Diamond X dimension\n* `y` : Diamond Y dimension\n* `x` : Diamond Z dimension\n\nTarget variable:\n* `price`: Price of the given Diamond.\n\n## Requirements :\n\nEnsure you have the following dependencies installed:\n\n- Python (version 3.9)\n- Jupyter Notebook\n- Other dependencies (refer to the requirements.txt)\n\nYou can install the required Python packages using:\n\n```bash\npip install -r requirements.txt\n```\n## Setup :\n\n- Clone the repository:\n```bash\ngit clone https://github.com/SINGHxTUSHAR/diamond-price-prediction.git\ncd diamond-price-prediction\n```\n- Create a virtual environment (optional but recommended):\n```bash\npython -m venv venv\n```\n- Activate the virtual environment:\n  - On Windows:\n   ```bash\n   venv\\Scripts\\activate\n   ```\n  - On macOS/Linux:\n  ```bash\n  source venv/bin/activate\n  ```\n\n## Usage :\n\n- Open the Jupyter Notebook:\n```bash\njupyter notebook\n```\n- Navigate to the Diamond_Price_Prediction.ipynb notebook and open it.\n- Follow the instructions in the notebook to run the code cells.\n\n\n## Dataset Source Link :\n[https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv](https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv)\n\n## Models :\n\n- Linear Regression\n- Lasso(L1 Regularization)\n- Ridge(L2 Regularization)\n- ElasticNet\n- DecisionTreeRegressor\n- RandomForestRegressor\n- SVR(Support Vector Regression)\n\n## Results :\n\n* LinearRegression Model Training Performance :\n   - RMSE: 1013.9047094344004\n   - MAE: 674.0255115796832\n   - R2 score 93.68908248567512\n\n* Lasso Model Training Performance :\n  - RMSE: 1013.8784226767013\n  - MAE: 675.0716923362161\n  - R2 score 93.68940971841704\n\n* Ridge Model Training Performance :\n  - RMSE: 1013.9059272771647\n  - MAE: 674.0555800798206\n  - R2 score 93.68906732505937\n\n* Elastic net Model Training Performance :\n  - RMSE: 1533.4162456064048\n  - MAE: 1060.7368759154729\n  - R2 score 85.56494831165182\n\n* DecisionTreeRegressor Model Training Performance :\n  - RMSE: 838.7860494085679\n  - MAE: 423.35310763649716\n  - R2 score 95.68082679027064\n\n* RandomForestRegressor Model Training Performance :\n  - RMSE: 610.7311352754283\n  - MAE: 311.5482746987208\n  - R2 score 97.71019581207089\n\n\n## Contributing :\nIf you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!\n\n## License :\nThis project is licensed under the \u003ca href=\"https://github.com/SINGHxTUSHAR/DiamondPricePrediction/blob/main/LICENSE\"\u003eMIT License\u003c/a\u003e - see the LICENSE file for details.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fdiamondpriceprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsinghxtushar%2Fdiamondpriceprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fdiamondpriceprediction/lists"}