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https://github.com/adi3042/diamond-price-prediction
🔮💎 Explore the Diamond Price Oracle! Predicting diamond values based on your preferences and market trends. Your ultimate tool for informed decisions in the diamond world. Join us for the future of pricing! 💎✨🚀 #DiamondPredictor
https://github.com/adi3042/diamond-price-prediction
anaconda datetime diamond flask functools html ipykernel jupytrnotebooks matplotlib numpy pandas price-prediction readme regression sckiit-learn setuptools venv
Last synced: 11 days ago
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🔮💎 Explore the Diamond Price Oracle! Predicting diamond values based on your preferences and market trends. Your ultimate tool for informed decisions in the diamond world. Join us for the future of pricing! 💎✨🚀 #DiamondPredictor
- Host: GitHub
- URL: https://github.com/adi3042/diamond-price-prediction
- Owner: Adi3042
- License: mit
- Created: 2023-12-07T06:05:39.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-08-23T08:48:25.000Z (3 months ago)
- Last Synced: 2024-08-23T09:59:38.261Z (3 months ago)
- Topics: anaconda, datetime, diamond, flask, functools, html, ipykernel, jupytrnotebooks, matplotlib, numpy, pandas, price-prediction, readme, regression, sckiit-learn, setuptools, venv
- Language: Jupyter Notebook
- Homepage:
- Size: 8.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### 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.
#### Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Technologies Used](#technologies-used)
- [Contributions](#contributions)
### 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.
### 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.
### 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:
```bash
git clone https://github.com/Adi3042/Diamond-Price-Prediction.git
cd Diamond-Price-Prediction
```
##### Step 2 - Create a conda environment after opening the repository
```bash
conda create -p venv python==3.8
conda activate venv/
```
##### Step 3 - Install the requirements
Open your terminal and execute the following command:
```bash
pip install -r requirements.txt
```
##### Step 4 - Run the application server
Open your terminal and execute the following command:
```bash
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.