💎Gem Stone Price 💵 Prediction

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Problem Statement

There are 10 independent variables (including id):

id : unique identifier of each diamond

carat : Carat (ct.) refers to the unique unit of weight measurement used exclusively to weigh gemstones and diamonds.

cut : Quality of Diamond Cut

color : Color of Diamond

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.

depth : The depth of diamond is its height (in millimeters) measured from the culet (bottom tip) to the table (flat, top surface)

table : A diamond's table is the facet which can be seen when the stone is viewed face up.

x : Diamond X dimension

y : Diamond Y dimension

x : Diamond Z dimension

Target variable:

price: Price of the given Diamond.

Dataset Link : Regression with a Tabular Gemstone Price Dataset

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Solution Explaination

Click the below image to see vedio solution explaination

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Approch for the problem

1. Downloading data and loading into jupyter notebook.
2. Perfoming exploratory data analysis and feature engineering in jupyter notebook.
3. Performing model building in jupyer notebook using different machine learning algorithm.
4. Converting entire jupyter notebook experiments into modular coding with expection handling and logging.
5. Integration of airflow for continuos model training.
6. Ingeration of mlflow for logging performance metrics and comparsion of different models.
7. Building of prediction pipeline and falsk api to serve model.
8. Testing api using Postman.
9. Building ui using HTML and CSS for flask api.
10. Then dockering the application and testing in local environment.
11. Deploying complete working model on Azure for practising and learning azure cloud deployment.
12. Deploying complete working model on AWS cloud platform using CICD with github actions.

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Project UI

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API Testing Results

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Mlflow integration

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Airflow integration

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Deployment 1 - Using Azure

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Deployment 2 - Using AWS Elastic Beanstalk(EBS)

Step 1 : Created role for AWS EBS ('gem_stone_deploy_role') and AWS code pipeline ('gem_stone_pipeline_role').
Step 2 : Created AWS EBS application and launched EBS environment with certian configuration.
Step 3 : Created AWS Code pipeline to connect github to EBS.
Step 4 : Waiting for Deployment and then accessing application using domain given in AWS EBS.

Note : As there were so many conflicts in the branch i have deleted both the azure deployment and ebs deployment branch

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Project Structure

│
├── .github
│   │
│   └── workflow
│       │
│       └── main.yml                         <-- contains yml code to create CI-CD pipeline for github actions
│
├── artificats                               <-- Contains dataset(train, test and raw) and pickle files(preprocessor and model)
│
├── images                                   <-- contains images used in readme file
│
├── notebooks
│   │
│   └── experiment.ipynb                     <-- a jupyter notebook where eda and model training is performed
│
├── resources                                <-- folder contains some usefull commands and steps used while build project
│
├── src
│   │
│   ├── components
│   │   │
│   │   ├── data_ingestion.py             <-- module which reads data from different data source and do train test split
│   │   │                                        then save raw data, train data and test data inside artifact folder
│   │   │
│   │   ├── data_transformation.py        <-- module which takes training and test dataset and then do feature engineering
│   │   │                                        then save preprocessor as pickle file inside artifact folder
│   │   │
│   │   ├── model_training.py             <-- module which takes preprocessed training and test data and
│   │   │                                        this data is used to train different models and selects best model
│   │   │                                        it also perform hyperparameter tuning
│   │   │
│   │   │
│   │   └── model_evaluation.py           <-- module which calculate the performance metrics
│   │
│   ├── pipeline
│   │   │
│   │   ├── __init__.py
│   │   │
│   │   ├── training_pipeline.py          <-- module used to train the model using training components
│   │   │
│   │   └── prediction_pipeline.py        <-- module takes the input data given by user through flask web application and returns the prediction
│   │
│   ├── __init__.py
│   │
│   ├── exception.py                         <-- module to display the custom exception
│   │
│   ├── logger.py                            <-- module to create log folder for each execution and log the events whenever required.
│   │
│   └── utils.py                             <-- module to which contians functions that are commonly used.
│
├── static
│   │
│   └── css                                  <-- contains all css files
│
├── templates                                <-- contains all html files
│
├── .gitignore                               <-- used to ignore the unwanted file and folders
│
├── application.py                           <-- flask web application to take input from user and render output
│
├── init_setup.sh                            <-- file is likely a shell script used to initailize the setup
│
├── LICENSE                                  <-- copyright license for the github repository
│
├── README.md                                <-- used to display the information about the project
│
├── requirements_dev.txt                     <-- text file which contain the dependencies in development environment
│
├── requirements.txt                         <-- text file which contain the dependencies/packages used in project
│
├── setup.py                                 <-- python script used for building python packages of the project
│
└── template.py                              <-- program used to create the project structure

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