https://github.com/mnagaharshithrao/diamondpriceprediction
Diamond Price Prediction is a machine learning project that predicts the price of diamonds based on features like carat, cut, color, clarity, and more. Using models such as XGBoost, Random Forest, and Gradient Boosting, the project aims to accurately forecast diamond prices, helping buyers and sellers in the diamond industry make informed decisions
https://github.com/mnagaharshithrao/diamondpriceprediction
machine-learning numpy pandas sckiit-learn xgboost
Last synced: 3 months ago
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Diamond Price Prediction is a machine learning project that predicts the price of diamonds based on features like carat, cut, color, clarity, and more. Using models such as XGBoost, Random Forest, and Gradient Boosting, the project aims to accurately forecast diamond prices, helping buyers and sellers in the diamond industry make informed decisions
- Host: GitHub
- URL: https://github.com/mnagaharshithrao/diamondpriceprediction
- Owner: MNagaHarshithRao
- Created: 2025-02-19T18:05:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T18:16:40.000Z (over 1 year ago)
- Last Synced: 2025-02-19T19:23:21.638Z (over 1 year ago)
- Topics: machine-learning, numpy, pandas, sckiit-learn, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 260 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Diamond Price Prediction Project
## Overview
This project aims to predict the price of diamonds based on various features such as carat weight, cut, color, clarity, and other attributes. Using machine learning models, the goal is to create an accurate model to predict the price of diamonds for potential buyers, sellers, and traders in the diamond industry.
## Features
- Predict diamond prices based on key attributes.
- Utilize multiple machine learning models to compare prediction performance.
- Data preprocessing and feature engineering for improved model accuracy.
- Model evaluation using standard metrics (e.g., RMSE, MAE).
- **XGBoost** is used as one of the models to predict diamond prices due to its high performance in regression tasks.
## Dataset
The dataset used for this project contains information about diamonds and their attributes, including:
- **Carat**: Weight of the diamond.
- **Cut**: Quality of the cut (e.g., Ideal, Premium, Good, Fair, Poor).
- **Color**: Color of the diamond (graded from D to J, D being the best).
- **Clarity**: Clarity of the diamond (e.g., SI1, VS2, VVS1).
- **Depth**: Total depth percentage of the diamond.
- **Table**: Table percentage of the diamond.
- **Price**: Price of the diamond (target variable).
The dataset is sourced from the popular `diamonds` dataset available in the Seaborn library.
## Installation
To get started with the project, you will need to clone the repository and install the necessary dependencies:
1. Clone the repository:
```bash
git clone https://github.com/your-username/diamond-price-prediction.git