{"id":22540451,"url":"https://github.com/sureshbeekhani/gemstone-price-prediction","last_synced_at":"2025-03-28T07:20:34.422Z","repository":{"id":262761251,"uuid":"875480982","full_name":"SURESHBEEKHANI/gemstone-price-prediction","owner":"SURESHBEEKHANI","description":"The Gemstone Price Prediction project aims to predict gemstone prices using a machine learning approach. The project involves various steps such as data ingestion, transformation, model training, and deploying the model through a Flask web application.","archived":false,"fork":false,"pushed_at":"2024-11-14T05:35:14.000Z","size":16044,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-02T08:13:21.849Z","etag":null,"topics":["data-science","machine-learning","predictive-modeling","regression-analysis"],"latest_commit_sha":null,"homepage":"https://sureshbeekhani-gemstone-price-prediction.hf.space/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SURESHBEEKHANI.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":"2024-10-20T05:03:06.000Z","updated_at":"2024-11-14T05:35:17.000Z","dependencies_parsed_at":"2024-11-14T06:32:05.652Z","dependency_job_id":null,"html_url":"https://github.com/SURESHBEEKHANI/gemstone-price-prediction","commit_stats":null,"previous_names":["sureshbeekhani/gemstone-price-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2Fgemstone-price-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2Fgemstone-price-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2Fgemstone-price-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SURESHBEEKHANI%2Fgemstone-price-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SURESHBEEKHANI","download_url":"https://codeload.github.com/SURESHBEEKHANI/gemstone-price-prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245984934,"owners_count":20704880,"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-science","machine-learning","predictive-modeling","regression-analysis"],"created_at":"2024-12-07T12:10:57.360Z","updated_at":"2025-03-28T07:20:34.406Z","avatar_url":"https://github.com/SURESHBEEKHANI.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Gemstone Price Prediction - Suresh beekhani\n### Introduction About the Data :\n\n**The dataset** The 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\nDataset 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### It is observed that the categorical variables 'cut', 'color' and 'clarity' are ordinal in nature\n\n\n# Deployment Link :\n\n link : [https://sureshbeekhani-gemstone-price-prediction.hf.space/](https://sureshbeekhani-gemstone-price-prediction.hf.space/)\n\n# Screenshot of UI\n\n\n# YouTube Video Link\n\nLink for YouTube Video : Click the below thumbnail to open \n\n![API Prediction](./Screenshots/Prediction.jpg)\n\n# Approach for the project \n\n1. Data Ingestion : \n    * In Data Ingestion phase the data is first read as csv. \n    * Then the data is split into training and testing and saved as csv file.\n\n2. Data Transformation : \n    * In this phase a ColumnTransformer Pipeline is created.\n    * for Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data.\n    * for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.\n    * This preprocessor is saved as pickle file.\n\n3. Model Training : \n    * In this phase base model is tested . The best model found was catboost regressor.\n    * After this hyperparameter tuning is performed on catboost and knn model.\n    * A final VotingRegressor is created which will combine prediction of catboost, xgboost and knn models.\n    * This model is saved as pickle file.\n\n4. Prediction Pipeline : \n    * This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.\n\n5. Flask App creation : \n    * Flask app is created with User Interface to predict the gemstone prices inside a Web Application.\n\n# Exploratory Data Analysis Notebook\n\nLink : [EDA Notebook](./notebook/1_EDA_Gemstone_price.ipynb)\n\n# Model Training Approach Notebook\n\nLink : [Model Training Notebook](./notebook/2_Model_Training_Gemstone.ipynb)\n\n# Model Interpretation with LIME \n\nLink : [LIME Interpretation](./notebook/3_Explainability_with_LIME.ipynb)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsureshbeekhani%2Fgemstone-price-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsureshbeekhani%2Fgemstone-price-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsureshbeekhani%2Fgemstone-price-prediction/lists"}