Table of Contents

• About The Project
• About the Data
  • Target Variable
  • Dataset Source Link
• Technology Stack
• Getting Started
• Installation Steps
  • Option 1: Installation from GitHub
  • Option 2: Installation from DockerHub
• Setup
  • MLflow Tracking
• Usage and Configuration
• Contributing
• License
• Contact
• Acknowledgements

About The Project

The Diamond Price Prediction project is an end-to-end machine learning initiative designed to forecast the prices of diamonds. Diamonds are highly valuable gemstones, and their prices can vary significantly based on various factors. This project aims to leverage machine learning techniques to provide accurate predictions of diamond prices, thereby offering valuable insights to both buyers and sellers in the diamond market.

About the Data

The dataset The goal is to predict the price of a given diamond (Regression Analysis).

There are 10 independent variables (including id):

• id: the 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 the diamond is its height (in millimetres) 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
• z: Diamond Z dimension

Target variable:

• price: Price of the given Diamond.

Dataset Source Link : https://www.kaggle.com/competitions/playground-series-s3e8/data?select=train.csv

Technology Stack

• Pandas
• Numpy
• Scikit-learn
• Flask
• DVC
• MLFlow
• Seaborn
• Matplotlib

Getting Started

This will help you understand how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Installation Steps

Option 1: Installation from GitHub

Follow these steps to install and set up the project directly from the GitHub repository:

1. Clone the Repository

   • Open your terminal or command prompt.
   • Navigate to the directory where you want to install the project.
   • Run the following command to clone the GitHub repository:

     git clone <Repo_Name>
     

2. Create a Virtual Environment (Optional but recommended)

   • It's a good practice to create a virtual environment to manage project dependencies. Run the following command:

     conda create -p <Environment_Name> python==<python version> -y
     

3. Activate the Virtual Environment (Optional)

   • Activate the virtual environment based on your operating system:

     conda activate <Environment_Name>/
     

4. Install Dependencies

   • Navigate to the project directory:

     cd [project_directory]
     

   • Run the following command to install project dependencies:

     pip install -r requirements.txt
     

5. Run the Project

   • Start the project by running the appropriate command.

     python app.py
     

6. Access the Project

   • Open a web browser or the appropriate client to access the project.

Option 2: Installation from DockerHub

If you prefer to use Docker, you can install and run the project using a Docker container from DockerHub:

1. Pull the Docker Image

   • Open your terminal or command prompt.
   • Run the following command to pull the Docker image from DockerHub:

     docker pull shubhamprajapati7748/diamond-app:v2
     

2. Run the Docker Container

   • Start the Docker container by running the following command, and mapping any necessary ports:

     docker run -p 5000:5000 shubhamprajapati7748/diamond-app:v2
     

3. Access the Project

   • Open a web browser or the appropriate client to access the project.

Setup

MLflow Tracking

We use MLflow to log and track our machine learning experiments. The MLFLOW_TRACKING_URI environment variable is set to the DagsHub repository's MLflow tracking URI.

export MLFLOW_TRACKING_URI=<MLFLOW_TRACKING_URI>

export MLFLOW_TRACKING_USERNAME=<MLFLOW_TRACKING_USERNAME>

export MLFLOW_TRACKING_PASSWORD=<MLFLOW_TRACKING_PASSWORD>

Usage and Configuration

This project requires Amazon Web Services Access Key ID and Secret Access Key for interacting with AWS services. Follow these steps to configure your project to use AWS keys:

1. Obtain Your AWS Access Key ID and Secret Access Key:

   • Log in to the AWS Management Console.
   • Open the IAM (Identity and Access Management) dashboard.
   • Create a new IAM user or use an existing one.
   • Attach the necessary policies to the user.
   • Generate an access key for the user. Save these keys securely.

2. Configuration:

   • Store your AWS Access Key ID and Secret Access Key securely. Do not hardcode them directly in your code or expose them in public repositories. Instead, use environment variables or a configuration file to manage them securely.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch
3. Commit your Changes
4. Push to the Branch
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE.txt for more information.

Contact

Shubham Prajapati - @shubhamprajapati7748@gmail.com

Acknowledgements

We'd like to extend our gratitude to all individuals and organizations who have played a role in the development and success of this project. Your support, whether through contributions, inspiration, or encouragement, has been invaluable. Thank you for being a part of our journey.