├── .github └── workflows │ └── main_gempriceprediction.yml ├── .gitignore ├── LICENSE ├── README.md ├── Screenshots ├── APIPrediction.jpg └── HomepageUI.jpg ├── application.py ├── artifacts ├── data.csv ├── model.pkl ├── preprocessor.pkl ├── test.csv └── train.csv ├── catboost_info ├── catboost_training.json ├── learn │ └── events.out.tfevents ├── learn_error.tsv └── time_left.tsv ├── notebook ├── 1_EDA_Gemstone_price.ipynb ├── 2_Model_Training_Gemstone.ipynb ├── 3_Explainability_with_LIME.ipynb ├── catboost_info │ ├── catboost_training.json │ ├── learn │ │ └── events.out.tfevents │ ├── learn_error.tsv │ └── time_left.tsv └── data │ └── gemstone.csv ├── requirements.txt ├── setup.py ├── src ├── __init__.py ├── components │ ├── __init__.py │ ├── data_ingestion.py │ ├── data_transformation.py │ └── model_trainer.py ├── exception.py ├── logger.py ├── pipeline │ ├── __init__.py │ ├── predict_pipeline.py │ └── train_pipeline.py └── utils.py ├── static ├── Image │ └── your_profile_pic.jpg └── css │ └── style.css └── templates └── index.html /.github/workflows/main_gempriceprediction.yml: -------------------------------------------------------------------------------- 1 | # Docs for the Azure Web Apps Deploy action: https://github.com/Azure/webapps-deploy 2 | # More GitHub Actions for Azure: https://github.com/Azure/actions 3 | # More info on Python, GitHub Actions, and Azure App Service: https://aka.ms/python-webapps-actions 4 | 5 | name: Build and deploy Python app to Azure Web App - gempriceprediction 6 | 7 | on: 8 | push: 9 | branches: 10 | - main 11 | workflow_dispatch: 12 | 13 | jobs: 14 | build: 15 | runs-on: ubuntu-latest 16 | 17 | steps: 18 | - uses: actions/checkout@v2 19 | 20 | - name: Set up Python version 21 | uses: actions/setup-python@v1 22 | with: 23 | python-version: '3.8' 24 | 25 | - name: Create and start virtual environment 26 | run: | 27 | python -m venv venv 28 | source venv/bin/activate 29 | 30 | - name: Install dependencies 31 | run: pip install -r requirements.txt 32 | 33 | # Optional: Add step to run tests here (PyTest, Django test suites, etc.) 34 | 35 | - name: Upload artifact for deployment jobs 36 | uses: actions/upload-artifact@v2 37 | with: 38 | name: python-app 39 | path: | 40 | . 41 | !venv/ 42 | 43 | deploy: 44 | runs-on: ubuntu-latest 45 | needs: build 46 | environment: 47 | name: 'Production' 48 | url: ${{ steps.deploy-to-webapp.outputs.webapp-url }} 49 | 50 | steps: 51 | - name: Download artifact from build job 52 | uses: actions/download-artifact@v2 53 | with: 54 | name: python-app 55 | path: . 56 | 57 | - name: 'Deploy to Azure Web App' 58 | uses: azure/webapps-deploy@v2 59 | id: deploy-to-webapp 60 | with: 61 | app-name: 'gempriceprediction' 62 | slot-name: 'Production' 63 | publish-profile: ${{ secrets.AZUREAPPSERVICE_PUBLISHPROFILE_EF40C19E11A74CEE82A21D995823CCCF }} 64 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Gemstone Price Prediction - Utkarsh Gaikwad 2 | 3 | ### Introduction About the Data : 4 | 5 | Please this project is of a student. Just wanted to appreciate for knowledge sharing 6 | 7 | **The dataset** The goal is to predict `price` of given diamond (Regression Analysis). 8 | 9 | There are 10 independent variables (including `id`): 10 | 11 | * `id` : unique identifier of each diamond 12 | * `carat` : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds. 13 | * `cut` : Quality of Diamond Cut 14 | * `color` : Color of Diamond 15 | * `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. 16 | * `depth` : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface) 17 | * `table` : A diamond's table is the facet which can be seen when the stone is viewed face up. 18 | * `x` : Diamond X dimension 19 | * `y` : Diamond Y dimension 20 | * `x` : Diamond Z dimension 21 | 22 | Target variable: 23 | * `price`: Price of the given Diamond. 24 | 25 | Dataset Source Link : 26 | [https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv](https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv) 27 | 28 | ### It is observed that the categorical variables 'cut', 'color' and 'clarity' are ordinal in nature 29 | 30 | ### Check this link for details : [American Gem Society](https://www.americangemsociety.org/ags-diamond-grading-system/) 31 | 32 | # AWS Deployment Link : 33 | 34 | AWS Elastic Beanstalk link : [http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/](http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/) 35 | 36 | # Screenshot of UI 37 | 38 | ![HomepageUI](./Screenshots/HomepageUI.jpg) 39 | 40 | # YouTube Video Link 41 | 42 | Link for YouTube Video : Click the below thumbnail to open 43 | 44 | [![https://youtu.be/Xvk5r0t_RQw](https://i.ytimg.com/vi/Xvk5r0t_RQw/hqdefault.jpg?sqp=-oaymwEcCNACELwBSFXyq4qpAw4IARUAAIhCGAFwAcABBg==&rs=AOn4CLBbp5SouquUm3Y3t-NYfOYsg4N4oQ)](https://youtu.be/Xvk5r0t_RQw) 45 | 46 | # AWS API Link 47 | 48 | API Link : [http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/predictAPI](http://gemstonepriceutkarshgaikwad-env.eba-7zp3wapg.ap-south-1.elasticbeanstalk.com/predictAPI) 49 | 50 | # Postman Testing of API : 51 | 52 | ![API Prediction](./Screenshots/APIPrediction.jpg) 53 | 54 | # Approach for the project 55 | 56 | 1. Data Ingestion : 57 | * In Data Ingestion phase the data is first read as csv. 58 | * Then the data is split into training and testing and saved as csv file. 59 | 60 | 2. Data Transformation : 61 | * In this phase a ColumnTransformer Pipeline is created. 62 | * for Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data. 63 | * for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler. 64 | * This preprocessor is saved as pickle file. 65 | 66 | 3. Model Training : 67 | * In this phase base model is tested . The best model found was catboost regressor. 68 | * After this hyperparameter tuning is performed on catboost and knn model. 69 | * A final VotingRegressor is created which will combine prediction of catboost, xgboost and knn models. 70 | * This model is saved as pickle file. 71 | 72 | 4. Prediction Pipeline : 73 | * This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python. 74 | 75 | 5. Flask App creation : 76 | * Flask app is created with User Interface to predict the gemstone prices inside a Web Application. 77 | 78 | # Exploratory Data Analysis Notebook 79 | 80 | Link : [EDA Notebook](./notebook/1_EDA_Gemstone_price.ipynb) 81 | 82 | # Model Training Approach Notebook 83 | 84 | Link : [Model Training Notebook](./notebook/2_Model_Training_Gemstone.ipynb) 85 | 86 | # Model Interpretation with LIME 87 | 88 | Link : [LIME Interpretation](./notebook/3_Explainability_with_LIME.ipynb) 89 | -------------------------------------------------------------------------------- /Screenshots/APIPrediction.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/Screenshots/APIPrediction.jpg -------------------------------------------------------------------------------- /Screenshots/HomepageUI.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/Screenshots/HomepageUI.jpg -------------------------------------------------------------------------------- /application.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, request, render_template,jsonify 2 | from flask_cors import CORS,cross_origin 3 | from src.pipeline.predict_pipeline import CustomData, PredictPipeline 4 | 5 | application = Flask(__name__) 6 | 7 | app = application 8 | 9 | @app.route('/') 10 | @cross_origin() 11 | def home_page(): 12 | return render_template('index.html') 13 | 14 | @app.route('/predict',methods=['GET','POST']) 15 | @cross_origin() 16 | def predict_datapoint(): 17 | if request.method == 'GET': 18 | return render_template('index.html') 19 | else: 20 | data = CustomData( 21 | carat = float(request.form.get('carat')), 22 | depth = float(request.form.get('depth')), 23 | table = float(request.form.get('table')), 24 | x = float(request.form.get('x')), 25 | y = float(request.form.get('y')), 26 | z = float(request.form.get('z')), 27 | cut = request.form.get('cut'), 28 | color= request.form.get('color'), 29 | clarity = request.form.get('clarity') 30 | ) 31 | 32 | pred_df = data.get_data_as_dataframe() 33 | 34 | print(pred_df) 35 | 36 | predict_pipeline = PredictPipeline() 37 | pred = predict_pipeline.predict(pred_df) 38 | results = round(pred[0],2) 39 | return render_template('index.html',results=results,pred_df = pred_df) 40 | 41 | @app.route('/predictAPI',methods=['POST']) 42 | @cross_origin() 43 | def predict_api(): 44 | if request.method=='POST': 45 | data = CustomData( 46 | carat = float(request.json['carat']), 47 | depth = float(request.json['depth']), 48 | table = float(request.json['table']), 49 | x = float(request.json['x']), 50 | y = float(request.json['y']), 51 | z = float(request.json['z']), 52 | cut = request.json['cut'], 53 | color = request.json['color'], 54 | clarity = request.json['clarity'] 55 | ) 56 | 57 | pred_df = data.get_data_as_dataframe() 58 | predict_pipeline = PredictPipeline() 59 | pred = predict_pipeline.predict(pred_df) 60 | 61 | dct = {'price':round(pred[0],2)} 62 | return jsonify(dct) 63 | 64 | if __name__ == '__main__': 65 | app.run(host='0.0.0.0', port=8000) -------------------------------------------------------------------------------- /artifacts/model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/artifacts/model.pkl -------------------------------------------------------------------------------- /artifacts/preprocessor.pkl: -------------------------------------------------------------------------------- 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598 561.380725 601 | 599 561.3701531 602 | -------------------------------------------------------------------------------- /catboost_info/time_left.tsv: -------------------------------------------------------------------------------- 1 | iter Passed Remaining 2 | 0 19 11850 3 | 1 38 11373 4 | 2 55 11104 5 | 3 73 10885 6 | 4 89 10706 7 | 5 106 10535 8 | 6 123 10424 9 | 7 138 10280 10 | 8 155 10184 11 | 9 170 10041 12 | 10 185 9914 13 | 11 199 9783 14 | 12 214 9697 15 | 13 229 9617 16 | 14 244 9522 17 | 15 257 9416 18 | 16 271 9310 19 | 17 286 9265 20 | 18 300 9181 21 | 19 313 9101 22 | 20 328 9056 23 | 21 342 9000 24 | 22 356 8946 25 | 23 371 8909 26 | 24 385 8859 27 | 25 399 8819 28 | 26 412 8754 29 | 27 427 8730 30 | 28 441 8687 31 | 29 454 8644 32 | 30 468 8605 33 | 31 484 8591 34 | 32 497 8544 35 | 33 511 8508 36 | 34 525 8479 37 | 35 538 8438 38 | 36 552 8403 39 | 37 566 8377 40 | 38 579 8343 41 | 39 594 8316 42 | 40 608 8293 43 | 41 622 8268 44 | 42 635 8235 45 | 43 650 8217 46 | 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| 247 572.6537661 250 | 248 572.5911506 251 | 249 572.5387443 252 | 250 572.470179 253 | 251 572.4111734 254 | 252 572.3322854 255 | 253 572.249312 256 | 254 572.2182848 257 | 255 572.1169932 258 | 256 572.0672913 259 | 257 572.023633 260 | 258 571.9547966 261 | 259 571.8637464 262 | 260 571.7539367 263 | 261 571.6783161 264 | 262 571.5687293 265 | 263 571.4765145 266 | 264 571.4139837 267 | 265 571.360572 268 | 266 571.2837911 269 | 267 571.1668311 270 | 268 571.1218488 271 | 269 571.0674768 272 | 270 571.0021238 273 | 271 570.9314642 274 | 272 570.8882658 275 | 273 570.8236604 276 | 274 570.732996 277 | 275 570.6405174 278 | 276 570.5997095 279 | 277 570.5359828 280 | 278 570.4557591 281 | 279 570.3545602 282 | 280 570.2941963 283 | 281 570.2547856 284 | 282 570.2088422 285 | 283 570.1795024 286 | 284 570.1266549 287 | 285 570.0574814 288 | 286 569.992202 289 | 287 569.9449442 290 | 288 569.8861055 291 | 289 569.7763267 292 | 290 569.7068424 293 | 291 569.6085796 294 | 292 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558.2910788 523 | 521 558.2477239 524 | 522 558.1940759 525 | 523 558.1739849 526 | 524 558.1338911 527 | 525 558.0922677 528 | 526 558.0419719 529 | 527 557.9985116 530 | 528 557.9530948 531 | 529 557.9239518 532 | 530 557.8492998 533 | 531 557.8083999 534 | 532 557.7760484 535 | 533 557.7684974 536 | 534 557.7619468 537 | 535 557.7004257 538 | 536 557.6486735 539 | 537 557.626508 540 | 538 557.5956204 541 | 539 557.5654328 542 | 540 557.5586874 543 | 541 557.5262248 544 | 542 557.5198654 545 | 543 557.5137084 546 | 544 557.5081901 547 | 545 557.4700211 548 | 546 557.4438271 549 | 547 557.4321949 550 | 548 557.3919825 551 | 549 557.3790314 552 | 550 557.3194753 553 | 551 557.3136365 554 | 552 557.2620632 555 | 553 557.2322824 556 | 554 557.2043103 557 | 555 557.1558035 558 | 556 557.116726 559 | 557 557.1057667 560 | 558 557.0973126 561 | 559 557.0719186 562 | 560 557.0485454 563 | 561 557.0382409 564 | 562 557.0329507 565 | 563 557.0187437 566 | 564 556.9851833 567 | 565 556.9195077 568 | 566 556.878009 569 | 567 556.8325693 570 | 568 556.8095689 571 | 569 556.7812357 572 | 570 556.73265 573 | 571 556.6912174 574 | 572 556.673083 575 | 573 556.6350399 576 | 574 556.6084822 577 | 575 556.5698953 578 | 576 556.5395446 579 | 577 556.5052164 580 | 578 556.4792098 581 | 579 556.377775 582 | 580 556.3479715 583 | 581 556.3314004 584 | 582 556.3046233 585 | 583 556.2584596 586 | 584 556.2534054 587 | 585 556.2267278 588 | 586 556.2055103 589 | 587 556.1527189 590 | 588 556.0865147 591 | 589 556.0584488 592 | 590 556.0221918 593 | 591 555.9834585 594 | 592 555.9620002 595 | 593 555.9432949 596 | 594 555.9196949 597 | 595 555.8832579 598 | 596 555.8401949 599 | 597 555.8196461 600 | 598 555.7848789 601 | 599 555.7680851 602 | -------------------------------------------------------------------------------- /notebook/catboost_info/time_left.tsv: -------------------------------------------------------------------------------- 1 | iter Passed Remaining 2 | 0 28 16778 3 | 1 52 15560 4 | 2 76 15322 5 | 3 99 14832 6 | 4 121 14505 7 | 5 146 14537 8 | 6 169 14338 9 | 7 191 14175 10 | 8 212 13963 11 | 9 233 13779 12 | 10 255 13669 13 | 11 277 13575 14 | 12 298 13470 15 | 13 321 13445 16 | 14 344 13427 17 | 15 365 13339 18 | 16 388 13328 19 | 17 411 13292 20 | 18 431 13197 21 | 19 455 13199 22 | 20 476 13129 23 | 21 498 13101 24 | 22 521 13084 25 | 23 544 13071 26 | 24 564 12978 27 | 25 588 12986 28 | 26 608 12917 29 | 27 628 12848 30 | 28 649 12782 31 | 29 667 12682 32 | 30 689 12659 33 | 31 712 12642 34 | 32 737 12672 35 | 33 762 12694 36 | 34 786 12693 37 | 35 811 12707 38 | 36 839 12771 39 | 37 862 12753 40 | 38 884 12717 41 | 39 907 12699 42 | 40 929 12671 43 | 41 951 12646 44 | 42 973 12611 45 | 43 995 12580 46 | 44 1018 12562 47 | 45 1040 12531 48 | 46 1058 12450 49 | 47 1079 12409 50 | 48 1101 12388 51 | 49 1124 12368 52 | 50 1147 12351 53 | 51 1168 12313 54 | 52 1190 12285 55 | 53 1210 12242 56 | 54 1233 12219 57 | 55 1254 12190 58 | 56 1275 12155 59 | 57 1298 12138 60 | 58 1320 12107 61 | 59 1341 12072 62 | 60 1363 12047 63 | 61 1384 12009 64 | 62 1405 11976 65 | 63 1427 11952 66 | 64 1450 11935 67 | 65 1470 11896 68 | 66 1491 11864 69 | 67 1514 11845 70 | 68 1532 11792 71 | 69 1548 11724 72 | 70 1566 11671 73 | 71 1588 11648 74 | 72 1608 11614 75 | 73 1630 11591 76 | 74 1651 11557 77 | 75 1672 11529 78 | 76 1694 11507 79 | 77 1714 11475 80 | 78 1734 11440 81 | 79 1757 11422 82 | 80 1777 11390 83 | 81 1798 11363 84 | 82 1820 11342 85 | 83 1841 11313 86 | 84 1862 11282 87 | 85 1881 11243 88 | 86 1897 11191 89 | 87 1914 11139 90 | 88 1931 11087 91 | 89 1951 11056 92 | 90 1971 11030 93 | 91 1993 11008 94 | 92 2011 10967 95 | 93 2031 10934 96 | 94 2050 10898 97 | 95 2069 10863 98 | 96 2090 10842 99 | 97 2113 10825 100 | 98 2138 10822 101 | 99 2163 10815 102 | 100 2182 10783 103 | 101 2201 10746 104 | 102 2220 10712 105 | 103 2240 10684 106 | 104 2257 10642 107 | 105 2277 10616 108 | 106 2297 10587 109 | 107 2317 10556 110 | 108 2338 10533 111 | 109 2357 10499 112 | 110 2374 10460 113 | 111 2395 10438 114 | 112 2415 10408 115 | 113 2436 10387 116 | 114 2456 10358 117 | 115 2475 10328 118 | 116 2496 10304 119 | 117 2517 10284 120 | 118 2536 10252 121 | 119 2557 10229 122 | 120 2576 10201 123 | 121 2596 10173 124 | 122 2615 10144 125 | 123 2633 10109 126 | 124 2650 10072 127 | 125 2668 10039 128 | 126 2687 10010 129 | 127 2706 9981 130 | 128 2725 9950 131 | 129 2743 9917 132 | 130 2761 9884 133 | 131 2779 9855 134 | 132 2798 9825 135 | 133 2817 9796 136 | 134 2836 9768 137 | 135 2853 9736 138 | 136 2873 9711 139 | 137 2891 9680 140 | 138 2910 9651 141 | 139 2929 9626 142 | 140 2949 9601 143 | 141 2969 9577 144 | 142 2987 9546 145 | 143 3005 9516 146 | 144 3022 9483 147 | 145 3040 9453 148 | 146 3058 9425 149 | 147 3077 9398 150 | 148 3095 9370 151 | 149 3111 9335 152 | 150 3129 9305 153 | 151 3145 9270 154 | 152 3164 9244 155 | 153 3183 9220 156 | 154 3200 9189 157 | 155 3217 9158 158 | 156 3236 9132 159 | 157 3254 9105 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210 | 208 4150 7765 211 | 209 4169 7742 212 | 210 4187 7720 213 | 211 4204 7695 214 | 212 4220 7667 215 | 213 4238 7645 216 | 214 4254 7618 217 | 215 4272 7595 218 | 216 4289 7570 219 | 217 4306 7546 220 | 218 4325 7524 221 | 219 4340 7497 222 | 220 4357 7472 223 | 221 4374 7447 224 | 222 4393 7427 225 | 223 4412 7407 226 | 224 4429 7383 227 | 225 4450 7364 228 | 226 4468 7342 229 | 227 4488 7323 230 | 228 4506 7300 231 | 229 4525 7279 232 | 230 4544 7258 233 | 231 4559 7232 234 | 232 4575 7207 235 | 233 4595 7187 236 | 234 4611 7162 237 | 235 4629 7139 238 | 236 4649 7120 239 | 237 4666 7097 240 | 238 4686 7078 241 | 239 4702 7053 242 | 240 4718 7028 243 | 241 4736 7007 244 | 242 4755 6986 245 | 243 4774 6965 246 | 244 4794 6946 247 | 245 4811 6923 248 | 246 4830 6904 249 | 247 4849 6883 250 | 248 4865 6859 251 | 249 4884 6838 252 | 250 4903 6817 253 | 251 4923 6798 254 | 252 4941 6776 255 | 253 4960 6756 256 | 254 4980 6738 257 | 255 4995 6713 258 | 256 5012 6690 259 | 257 5028 6665 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557 10376 781 560 | 558 10391 762 561 | 559 10404 743 562 | 560 10419 724 563 | 561 10434 705 564 | 562 10449 686 565 | 563 10464 667 566 | 564 10479 649 567 | 565 10493 630 568 | 566 10508 611 569 | 567 10524 592 570 | 568 10538 574 571 | 569 10553 555 572 | 570 10566 536 573 | 571 10582 518 574 | 572 10599 499 575 | 573 10614 480 576 | 574 10629 462 577 | 575 10645 443 578 | 576 10659 424 579 | 577 10673 406 580 | 578 10689 387 581 | 579 10701 369 582 | 580 10716 350 583 | 581 10732 331 584 | 582 10745 313 585 | 583 10760 294 586 | 584 10775 276 587 | 585 10789 257 588 | 586 10804 239 589 | 587 10817 220 590 | 588 10831 202 591 | 589 10844 183 592 | 590 10859 165 593 | 591 10873 146 594 | 592 10886 128 595 | 593 10901 110 596 | 594 10917 91 597 | 595 10931 73 598 | 596 10946 55 599 | 597 10960 36 600 | 598 10974 18 601 | 599 10989 0 602 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | pandas 3 | matplotlib 4 | seaborn 5 | scikit-learn 6 | ipykernel 7 | catboost 8 | xgboost 9 | dill 10 | flask 11 | flask_cors 12 | lime 13 | #-e . -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import find_packages, setup 2 | from typing import List 3 | 4 | HYPHEN_E_DOT = '-e .' 5 | 6 | def get_requirements(file_path:str)->List[str]: 7 | ''' 8 | This function will return list of requirements 9 | from requirements.txt file 10 | ''' 11 | # Initializing blank requirements list 12 | requirements = [] 13 | 14 | # Opening the file 15 | with open(file_path) as file_obj: 16 | requirements = file_obj.readlines() 17 | requirements = [req.replace("\n","") for req in requirements] 18 | 19 | # Remove hyphen_e_dot if present in requirements 20 | if HYPHEN_E_DOT in requirements: 21 | requirements.remove(HYPHEN_E_DOT) 22 | 23 | return requirements 24 | 25 | setup( 26 | name = 'mlproject_regression', 27 | version= '0.0.1', 28 | author='Utkarsh Gaikwad', 29 | author_email='gaikwadujg@gmail.com', 30 | packages = find_packages(), 31 | install_requires = get_requirements('requirements.txt') 32 | ) -------------------------------------------------------------------------------- /src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/src/__init__.py -------------------------------------------------------------------------------- /src/components/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/src/components/__init__.py -------------------------------------------------------------------------------- /src/components/data_ingestion.py: -------------------------------------------------------------------------------- 1 | # Import all the required libraries 2 | import os 3 | import sys 4 | from src.exception import CustomException 5 | from src.logger import logging 6 | import pandas as pd 7 | from sklearn.model_selection import train_test_split 8 | from dataclasses import dataclass 9 | 10 | from src.components.data_transformation import DataTransformation, DataTransformationConfig 11 | from src.components.model_trainer import ModelTrainer, ModelTrainerConfig 12 | 13 | # Initialize Data Ingestion Configuration 14 | @dataclass 15 | class DataIngestionConfig: 16 | train_data_path: str = os.path.join('artifacts','train.csv') 17 | test_data_path: str = os.path.join('artifacts','test.csv') 18 | raw_data_path: str = os.path.join('artifacts','data.csv') 19 | 20 | # Create a class for Data Ingestion 21 | class DataIngestion: 22 | def __init__(self): 23 | self.ingestion_config = DataIngestionConfig() 24 | 25 | def initate_data_ingestion(self): 26 | logging.info('Data ingestion method Started') 27 | try: 28 | df = pd.read_csv('notebook/data/gemstone.csv') 29 | logging.info('Dataset read as pandas Dataframe') 30 | 31 | os.makedirs(os.path.dirname(self.ingestion_config.raw_data_path),exist_ok=True) 32 | 33 | df.to_csv(self.ingestion_config.raw_data_path,index=False) 34 | 35 | logging.info('Train Test Split Initiated') 36 | train_set, test_set = train_test_split(df, test_size=0.2, random_state=42) 37 | 38 | train_set.to_csv(self.ingestion_config.train_data_path,index=False,header=True) 39 | test_set.to_csv(self.ingestion_config.test_data_path,index=False,header=True) 40 | 41 | logging.info('Ingestion of Data is completed') 42 | 43 | return( 44 | self.ingestion_config.train_data_path, 45 | self.ingestion_config.test_data_path 46 | ) 47 | 48 | except Exception as e: 49 | logging.info('Exception occured at Data Ingestion stage') 50 | raise CustomException(e, sys) 51 | 52 | # Run Data ingestion 53 | if __name__ == '__main__': 54 | obj = DataIngestion() 55 | train_data, test_data = obj.initate_data_ingestion() 56 | 57 | data_transformation = DataTransformation() 58 | train_arr, test_arr, _ = data_transformation.initate_data_transformation(train_data,test_data) 59 | 60 | modeltrainer = ModelTrainer() 61 | modeltrainer.initate_model_training(train_arr, test_arr) -------------------------------------------------------------------------------- /src/components/data_transformation.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from dataclasses import dataclass 3 | 4 | import numpy as np 5 | import pandas as pd 6 | from sklearn.compose import ColumnTransformer 7 | from sklearn.impute import SimpleImputer 8 | from sklearn.pipeline import Pipeline 9 | from sklearn.preprocessing import OrdinalEncoder,StandardScaler 10 | 11 | from src.exception import CustomException 12 | from src.logger import logging 13 | import os 14 | 15 | from src.utils import save_object 16 | 17 | @dataclass 18 | class DataTransformationConfig: 19 | preprocessor_obj_file_path = os.path.join('artifacts','preprocessor.pkl') 20 | 21 | class DataTransformation: 22 | def __init__(self): 23 | self.data_transformation_config = DataTransformationConfig() 24 | 25 | def get_data_transformation_object(self): 26 | ''' 27 | This function is responsible for data transformation 28 | ''' 29 | try: 30 | 31 | # Define which columns should be ordinal-encoded and which should be scaled 32 | categorical_cols = ['cut', 'color','clarity'] 33 | numerical_cols = ['carat', 'depth','table', 'x', 'y', 'z'] 34 | 35 | # Define the custom ranking for each ordinal variable 36 | cut_categories = ['Fair', 'Good', 'Very Good','Premium','Ideal'] 37 | color_categories = ['D', 'E', 'F', 'G', 'H', 'I', 'J'] 38 | clarity_categories = ['I1','SI2','SI1','VS2','VS1','VVS2','VVS1','IF'] 39 | 40 | # Numerical Pipeline 41 | num_pipeline = Pipeline( 42 | steps = [ 43 | ('imputer',SimpleImputer(strategy='median')), 44 | ('scaler',StandardScaler()) 45 | ] 46 | ) 47 | 48 | # Categorical Pipeline 49 | cat_pipeline = Pipeline( 50 | steps=[ 51 | ('imputer',SimpleImputer(strategy='most_frequent')), 52 | ('ordinal_encoder',OrdinalEncoder(categories=[cut_categories,color_categories,clarity_categories])), 53 | ('scaler',StandardScaler()) 54 | ] 55 | ) 56 | 57 | logging.info(f'Categorical Columns : {categorical_cols}') 58 | logging.info(f'Numerical Columns : {numerical_cols}') 59 | 60 | preprocessor = ColumnTransformer( 61 | [ 62 | ('num_pipeline',num_pipeline,numerical_cols), 63 | ('cat_pipeline',cat_pipeline,categorical_cols) 64 | ] 65 | ) 66 | 67 | return preprocessor 68 | 69 | except Exception as e: 70 | logging.info('Exception occured in Data Transformation Phase') 71 | raise CustomException(e,sys) 72 | 73 | def initate_data_transformation(self,train_path,test_path): 74 | 75 | try: 76 | # Reading train and test data 77 | train_df = pd.read_csv(train_path) 78 | test_df = pd.read_csv(test_path) 79 | 80 | logging.info('Read train and test data completed') 81 | logging.info(f'Train Dataframe Head : \n{train_df.head().to_string()}') 82 | logging.info(f'Test Dataframe Head : \n{test_df.head().to_string()}') 83 | 84 | logging.info('Obtaining preprocessing object') 85 | 86 | preprocessing_obj = self.get_data_transformation_object() 87 | 88 | target_column_name = 'price' 89 | drop_columns = [target_column_name,'id'] 90 | 91 | input_feature_train_df = train_df.drop(columns=drop_columns,axis=1) 92 | target_feature_train_df=train_df[target_column_name] 93 | 94 | input_feature_test_df=test_df.drop(columns=drop_columns,axis=1) 95 | target_feature_test_df=test_df[target_column_name] 96 | 97 | logging.info("Applying preprocessing object on training and testing datasets.") 98 | 99 | input_feature_train_arr=preprocessing_obj.fit_transform(input_feature_train_df) 100 | input_feature_test_arr=preprocessing_obj.transform(input_feature_test_df) 101 | 102 | train_arr = np.c_[input_feature_train_arr, np.array(target_feature_train_df)] 103 | test_arr = np.c_[input_feature_test_arr, np.array(target_feature_test_df)] 104 | 105 | save_object( 106 | 107 | file_path=self.data_transformation_config.preprocessor_obj_file_path, 108 | obj=preprocessing_obj 109 | 110 | ) 111 | logging.info('Preprocessor pickle file saved') 112 | 113 | return ( 114 | train_arr, 115 | test_arr, 116 | self.data_transformation_config.preprocessor_obj_file_path, 117 | ) 118 | 119 | except Exception as e: 120 | logging.info('Exception occured in initiate_data_transformation function') 121 | raise CustomException(e,sys) -------------------------------------------------------------------------------- /src/components/model_trainer.py: -------------------------------------------------------------------------------- 1 | # Basic Import 2 | import numpy as np 3 | import pandas as pd 4 | 5 | # Modelling 6 | from sklearn.neighbors import KNeighborsRegressor 7 | from sklearn.tree import DecisionTreeRegressor 8 | from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor, GradientBoostingRegressor 9 | from sklearn.svm import SVR 10 | from sklearn.linear_model import LinearRegression, Ridge,Lasso 11 | from sklearn.model_selection import RandomizedSearchCV, GridSearchCV 12 | from catboost import CatBoostRegressor 13 | from xgboost import XGBRegressor 14 | from sklearn.ensemble import VotingRegressor 15 | from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error 16 | 17 | from src.exception import CustomException 18 | from src.logger import logging 19 | from src.utils import save_object 20 | from src.utils import evaluate_models 21 | from src.utils import print_evaluated_results 22 | from src.utils import model_metrics 23 | 24 | from dataclasses import dataclass 25 | import sys 26 | import os 27 | 28 | @dataclass 29 | class ModelTrainerConfig: 30 | trained_model_file_path = os.path.join('artifacts','model.pkl') 31 | 32 | class ModelTrainer: 33 | def __init__(self): 34 | self.model_trainer_config = ModelTrainerConfig() 35 | 36 | def initate_model_training(self,train_array,test_array): 37 | try: 38 | logging.info('Splitting Dependent and Independent variables from train and test data') 39 | xtrain, ytrain, xtest, ytest = ( 40 | train_array[:,:-1], 41 | train_array[:,-1], 42 | test_array[:,:-1], 43 | test_array[:,-1] 44 | ) 45 | 46 | models = { 47 | "Linear Regression": LinearRegression(), 48 | "Lasso": Lasso(), 49 | "Ridge": Ridge(), 50 | "K-Neighbors Regressor": KNeighborsRegressor(), 51 | "Decision Tree": DecisionTreeRegressor(), 52 | "Random Forest Regressor": RandomForestRegressor(), 53 | "XGBRegressor": XGBRegressor(), 54 | "CatBoosting Regressor": CatBoostRegressor(verbose=False), 55 | "GradientBoosting Regressor":GradientBoostingRegressor(), 56 | "AdaBoost Regressor": AdaBoostRegressor() 57 | } 58 | 59 | model_report:dict = evaluate_models(xtrain,ytrain,xtest,ytest,models) 60 | 61 | print(model_report) 62 | print('\n====================================================================================\n') 63 | logging.info(f'Model Report : {model_report}') 64 | # To get best model score from dictionary 65 | best_model_score = max(sorted(model_report.values())) 66 | 67 | best_model_name = list(model_report.keys())[ 68 | list(model_report.values()).index(best_model_score) 69 | ] 70 | best_model = models[best_model_name] 71 | 72 | if best_model_score < 0.6 : 73 | logging.info('Best model has r2 Score less than 60%') 74 | raise CustomException('No Best Model Found') 75 | 76 | print(f'Best Model Found , Model Name : {best_model_name} , R2 Score : {best_model_score}') 77 | print('\n====================================================================================\n') 78 | logging.info(f'Best Model Found , Model Name : {best_model_name} , R2 Score : {best_model_score}') 79 | logging.info('Hyperparameter tuning started for catboost') 80 | 81 | # Hyperparameter tuning on Catboost 82 | # Initializing catboost 83 | cbr = CatBoostRegressor(verbose=False) 84 | 85 | # Creating the hyperparameter grid 86 | param_dist = {'depth' : [4,5,6,7,8,9, 10], 87 | 'learning_rate' : [0.01,0.02,0.03,0.04], 88 | 'iterations' : [300,400,500,600]} 89 | 90 | #Instantiate RandomSearchCV object 91 | rscv = RandomizedSearchCV(cbr , param_dist, scoring='r2', cv =5, n_jobs=-1) 92 | 93 | # Fit the model 94 | rscv.fit(xtrain, ytrain) 95 | 96 | # Print the tuned parameters and score 97 | print(f'Best Catboost parameters : {rscv.best_params_}') 98 | print(f'Best Catboost Score : {rscv.best_score_}') 99 | print('\n====================================================================================\n') 100 | 101 | best_cbr = rscv.best_estimator_ 102 | 103 | logging.info('Hyperparameter tuning complete for Catboost') 104 | 105 | logging.info('Hyperparameter tuning started for KNN') 106 | 107 | # Initialize knn 108 | knn = KNeighborsRegressor() 109 | 110 | # parameters 111 | k_range = list(range(2, 31)) 112 | param_grid = dict(n_neighbors=k_range) 113 | 114 | # Fitting the cvmodel 115 | grid = GridSearchCV(knn, param_grid, cv=5, scoring='r2',n_jobs=-1) 116 | grid.fit(xtrain, ytrain) 117 | 118 | # Print the tuned parameters and score 119 | print(f'Best KNN Parameters : {grid.best_params_}') 120 | print(f'Best KNN Score : {grid.best_score_}') 121 | print('\n====================================================================================\n') 122 | 123 | best_knn = grid.best_estimator_ 124 | 125 | logging.info('Hyperparameter tuning Complete for KNN') 126 | 127 | logging.info('Voting Regressor model training started') 128 | 129 | # Creating final Voting regressor 130 | er = VotingRegressor([('cbr',best_cbr),('xgb',XGBRegressor()),('knn',best_knn)], weights=[3,2,1]) 131 | er.fit(xtrain, ytrain) 132 | print('Final Model Evaluation :\n') 133 | print_evaluated_results(xtrain,ytrain,xtest,ytest,er) 134 | logging.info('Voting Regressor Training Completed') 135 | 136 | save_object( 137 | file_path=self.model_trainer_config.trained_model_file_path, 138 | obj = er 139 | ) 140 | logging.info('Model pickle file saved') 141 | # Evaluating Ensemble Regressor (Voting Classifier on test data) 142 | ytest_pred = er.predict(xtest) 143 | 144 | mae, rmse, r2 = model_metrics(ytest, ytest_pred) 145 | logging.info(f'Test MAE : {mae}') 146 | logging.info(f'Test RMSE : {rmse}') 147 | logging.info(f'Test R2 Score : {r2}') 148 | logging.info('Final Model Training Completed') 149 | 150 | return mae, rmse, r2 151 | 152 | except Exception as e: 153 | logging.info('Exception occured at Model Training') 154 | raise CustomException(e,sys) 155 | 156 | 157 | 158 | -------------------------------------------------------------------------------- /src/exception.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from src.logger import logging 3 | 4 | def error_message_detail(error,error_detail:sys): 5 | _,_,exc_tb = error_detail.exc_info() 6 | file_name = exc_tb.tb_frame.f_code.co_filename 7 | 8 | error_message = "Error occured in python script name [{0}] line number [{1}] error message [{2}]".format( 9 | file_name, exc_tb.tb_lineno, str(error) 10 | ) 11 | 12 | return error_message 13 | 14 | class CustomException(Exception): 15 | 16 | def __init__(self, error_message, error_detail:sys): 17 | super().__init__(error_message) 18 | self.error_message = error_message_detail(error_message, error_detail=error_detail) 19 | 20 | def __str__(self): 21 | return self.error_message -------------------------------------------------------------------------------- /src/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | from datetime import datetime 4 | 5 | LOG_FILE=f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log" 6 | logs_path=os.path.join(os.getcwd(),"logs",LOG_FILE) 7 | os.makedirs(logs_path,exist_ok=True) 8 | 9 | LOG_FILE_PATH=os.path.join(logs_path,LOG_FILE) 10 | 11 | logging.basicConfig( 12 | filename=LOG_FILE_PATH, 13 | format="[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s", 14 | level=logging.INFO 15 | ) -------------------------------------------------------------------------------- /src/pipeline/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/src/pipeline/__init__.py -------------------------------------------------------------------------------- /src/pipeline/predict_pipeline.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import pandas as pd 3 | from src.exception import CustomException 4 | from src.logger import logging 5 | from src.utils import load_object 6 | 7 | class PredictPipeline: 8 | def __init__(self): 9 | pass 10 | 11 | def predict(self, features): 12 | try: 13 | preprocessor_path = 'artifacts/preprocessor.pkl' 14 | model_path = 'artifacts/model.pkl' 15 | preprocessor = load_object(file_path=preprocessor_path) 16 | model = load_object(file_path=model_path) 17 | data_scaled = preprocessor.transform(features) 18 | pred = model.predict(data_scaled) 19 | return pred 20 | except Exception as e: 21 | logging.info('Exception occured in prediction pipeline') 22 | raise CustomException(e,sys) 23 | 24 | 25 | class CustomData: 26 | def __init__(self, 27 | carat:float, 28 | depth:float, 29 | table:float, 30 | x:float, 31 | y:float, 32 | z:float, 33 | cut:str, 34 | color:str, 35 | clarity:str): 36 | 37 | self.carat = carat 38 | self.depth = depth 39 | self.table = table 40 | self.x = x 41 | self.y = y 42 | self.z = z 43 | self.cut = cut 44 | self.color = color 45 | self.clarity = clarity 46 | 47 | def get_data_as_dataframe(self): 48 | try: 49 | custom_data_input_dict = { 50 | 'carat':[self.carat], 51 | 'depth':[self.depth], 52 | 'table':[self.table], 53 | 'x':[self.x], 54 | 'y':[self.y], 55 | 'z':[self.z], 56 | 'cut':[self.cut], 57 | 'color':[self.color], 58 | 'clarity':[self.clarity] 59 | } 60 | df = pd.DataFrame(custom_data_input_dict) 61 | logging.info('Dataframe Gathered') 62 | return df 63 | except Exception as e: 64 | logging.info('Exception Occured in prediction pipeline') 65 | raise CustomException(e,sys) 66 | -------------------------------------------------------------------------------- /src/pipeline/train_pipeline.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/src/pipeline/train_pipeline.py -------------------------------------------------------------------------------- /src/utils.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | import numpy as np 5 | import pandas as pd 6 | import dill 7 | from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error 8 | 9 | from src.exception import CustomException 10 | from src.logger import logging 11 | 12 | def save_object(file_path, obj): 13 | try: 14 | dir_path = os.path.dirname(file_path) 15 | 16 | os.makedirs(dir_path, exist_ok=True) 17 | 18 | with open(file_path, "wb") as file_obj: 19 | dill.dump(obj, file_obj) 20 | 21 | except Exception as e: 22 | raise CustomException(e, sys) 23 | 24 | def evaluate_models(xtrain,ytrain,xtest,ytest,models): 25 | try: 26 | report = {} 27 | for i in range(len(models)): 28 | model = list(models.values())[i] 29 | # Train model 30 | model.fit(xtrain,ytrain) 31 | 32 | # Predict Training data 33 | y_train_pred = model.predict(xtrain) 34 | 35 | # Predict Testing data 36 | y_test_pred =model.predict(xtest) 37 | 38 | # Get R2 scores for train and test data 39 | train_model_score = r2_score(ytrain,y_train_pred) 40 | test_model_score = r2_score(ytest,y_test_pred) 41 | 42 | report[list(models.keys())[i]] = test_model_score 43 | 44 | return report 45 | 46 | except Exception as e: 47 | logging.info('Exception occured during model training') 48 | raise CustomException(e,sys) 49 | 50 | def model_metrics(true, predicted): 51 | try : 52 | mae = mean_absolute_error(true, predicted) 53 | mse = mean_squared_error(true, predicted) 54 | rmse = np.sqrt(mse) 55 | r2_square = r2_score(true, predicted) 56 | return mae, rmse, r2_square 57 | except Exception as e: 58 | logging.info('Exception Occured while evaluating metric') 59 | raise CustomException(e,sys) 60 | 61 | 62 | def print_evaluated_results(xtrain,ytrain,xtest,ytest,model): 63 | try: 64 | ytrain_pred = model.predict(xtrain) 65 | ytest_pred = model.predict(xtest) 66 | 67 | # Evaluate Train and Test dataset 68 | model_train_mae , model_train_rmse, model_train_r2 = model_metrics(ytrain, ytrain_pred) 69 | model_test_mae , model_test_rmse, model_test_r2 = model_metrics(ytest, ytest_pred) 70 | 71 | # Printing results 72 | print('Model performance for Training set') 73 | print("- Root Mean Squared Error: {:.4f}".format(model_train_rmse)) 74 | print("- Mean Absolute Error: {:.4f}".format(model_train_mae)) 75 | print("- R2 Score: {:.4f}".format(model_train_r2)) 76 | 77 | print('----------------------------------') 78 | 79 | print('Model performance for Test set') 80 | print("- Root Mean Squared Error: {:.4f}".format(model_test_rmse)) 81 | print("- Mean Absolute Error: {:.4f}".format(model_test_mae)) 82 | print("- R2 Score: {:.4f}".format(model_test_r2)) 83 | 84 | except Exception as e: 85 | logging.info('Exception occured during printing of evaluated results') 86 | raise CustomException(e,sys) 87 | 88 | def load_object(file_path): 89 | try: 90 | with open(file_path,'rb') as file_obj: 91 | return dill.load(file_obj) 92 | except Exception as e: 93 | logging.info('Exception Occured in load_object function utils') 94 | raise CustomException(e,sys) -------------------------------------------------------------------------------- /static/Image/your_profile_pic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/krishnaik06/Studentmlprojectregression/e83fecfc92981a63f12ed73aa89a13a602cbdb76/static/Image/your_profile_pic.jpg -------------------------------------------------------------------------------- /static/css/style.css: -------------------------------------------------------------------------------- 1 | body { 2 | font-family: Arial, sans-serif; 3 | background-color: #BFE6FF; 4 | } 5 | 6 | .top-left-link { 7 | position: absolute; 8 | top: 20px; /* Add some top margin */ 9 | left: 20px; /* Add some left margin */ 10 | padding: 10px; /* Add some padding */ 11 | background-color: #DCAE96; /* Add a background color */ 12 | border-radius: 5px; /* Add some rounded corners */ 13 | font-size: 16px; /* Increase the font size */ 14 | color: black; /* Change the font color */ 15 | text-decoration: none; /* Remove the underline */ 16 | font-weight: bold; 17 | } 18 | 19 | .profile 20 | { 21 | display: block; 22 | margin-left: auto; 23 | margin-right: auto; 24 | width: 10%; 25 | padding: 20px; /* adds 30 pixels of padding around the image */ 26 | } 27 | 28 | .form-box { 29 | max-width: 800px; 30 | margin: 0 auto; 31 | background-color: #FFFFE0; 32 | padding: 20px; 33 | border: 1px solid #ccc; 34 | border-radius: 4px; 35 | } 36 | 37 | h1 { 38 | text-align: center; 39 | } 40 | 41 | h2 { 42 | text-align: center; 43 | } 44 | 45 | label { 46 | display: block; 47 | margin-bottom: 10px; 48 | font-weight: bold; 49 | } 50 | 51 | input[type="text"], select { 52 | padding: 10px; 53 | border: 1px solid #ccc; 54 | border-radius: 4px; 55 | box-sizing: border-box; 56 | margin-bottom: 10px; 57 | width: 100%; 58 | } 59 | 60 | input[type="submit"] { 61 | background-color: #4CAF50; 62 | color: white; 63 | padding: 10px 20px; 64 | border: none; 65 | border-radius: 4px; 66 | cursor: pointer; 67 | margin-top: 10px; 68 | display: block; 69 | margin: 0 auto; 70 | } 71 | 72 | input[type="submit"]:hover { 73 | background-color: #45a049; 74 | } 75 | 76 | .form-group { 77 | display: inline-block; 78 | vertical-align: top; 79 | margin-right: 20px; 80 | width: 30%; 81 | } 82 | 83 | .form-group label { 84 | margin-bottom: 5px; 85 | } -------------------------------------------------------------------------------- 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Gemstone Price Prediction - Utkarsh Gaikwad

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37 | 38 |
39 | 40 | 41 |
42 | 43 |
44 | 45 | 52 |
53 | 54 |
55 | 56 | 65 |
66 | 67 |
68 | 69 | 79 |
80 | 81 |
82 | 83 |
84 |
85 |
86 |

87 | Predicted Gemstone Price is : {{ results }} 88 |

89 | 90 | --------------------------------------------------------------------------------