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Diamond_Price_Prediction 💍

Overview

Diamond_Price_Prediction is an open-source project that enables you to predict diamond prices based on various attributes. With advanced machine learning models and a user-friendly web application, this project provides a reliable solution for estimating diamond prices.

Diamond Image

Table of Contents

Introduction 🌟

Welcome to Diamond_Price_Prediction! This project is designed to assist you in estimating the prices of diamonds based on their attributes. Whether you're a gem enthusiast or a jeweler, our project offers a reliable way to predict diamond prices.

Features 🚀

Data Ingestion and Transformation 📊

We've introduced robust data ingestion and transformation components to preprocess raw data efficiently. This ensures data quality and reliability in our predictive models.

Data Transformation

Advanced Machine Learning Models 🤖

Our project now incorporates advanced machine learning models for diamond price prediction. These models offer improved accuracy and generalization.

Web Application 🌐

We are thrilled to present our web-based user interface for easy input and prediction of diamond prices. You can now interact with our model through a user-friendly web application.

  • Input Form: Users can input various attributes of a diamond, including carat, depth, table, dimensions (x, y, z), cut, color, and clarity.
  • Machine Learning Prediction: The application uses a trained machine learning model to predict the price of the diamond based on the provided attributes.
  • User-Friendly Interface: The web app features an attractive and intuitive interface, making it easy for users to enter data and receive predictions.
  • Background Image: The app uses a captivating background image to enhance the visual appeal and engagement of users.

Getting Started 🛠️

The dataset used in the Diamond Price Prediction Web App project is a collection of diamond attributes and their corresponding prices. The dataset is utilized to train a machine learning model that can predict the price of a diamond based on its various characteristics. The dataset provides a valuable resource for understanding the relationships between diamond attributes and their market values.

Attributes in the Dataset:

  • 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 millimeters) measured from the culet (bottom tip) to the table (flat, top surface).
  • table: A diamond's table is the facet that can be seen when the stone is viewed face up.
  • x: Diamond X dimension.
  • y: Diamond Y dimension.
  • z: Diamond Z dimension.

Diamond Anatomy

Prerequisites 📋

Before using this project, ensure you have the following prerequisites in place:

  • Python (3.7 or higher)
  • Required dependencies (install with pip install -r requirements.txt)
  • Access to a web browser 🌐

Technologies Used:

  • Front-End: HTML, CSS
  • Back-End: Python (Flask framework)
  • Machine Learning: Linear Regression, Lasso Regression, Ridge Regression, Decision Tree

Installation 💻

Step 1 - Clone the repository to your local machine using Git:
git clone https://github.com/Adi3042/Diamond-Price-Prediction.git
cd Diamond-Price-Prediction
Step 2 - Create a conda environment after opening the repository
conda create -p venv python==3.8
conda activate venv/
Step 3 - Install the requirements

Open your terminal and execute the following command:

pip install -r requirements.txt
Step 4 - Run the application server

Open your terminal and execute the following command:

python application.py
Step 5 -
  1. Visit the web app. :- http://127.0.0.1:5000/
  2. Enter the attributes of the diamond in the input form.
  3. Click the "Predict" button.
  4. Receive the predicted price of the diamond.

Contributions:

Contributions to this project are welcome! If you have ideas for improvement, bug fixes, or additional features, feel free to create a pull request or open an issue.