Diamond Price Prediction ⯁

Overview

This project aims to predict the price of diamonds using regression algorithms. It leverages machine learning techniques to analyze various features of diamonds and build a predictive model.

Table of Contents

• Features
• Requirements
• Setup
• Usage
• Data
• Models
• Results
• Contributing
• License

Features :

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

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.

Requirements :

Ensure you have the following dependencies installed:

• Python (version 3.9)
• Jupyter Notebook
• Other dependencies (refer to the requirements.txt)

You can install the required Python packages using:

pip install -r requirements.txt

Setup :

• Clone the repository:

git clone https://github.com/SINGHxTUSHAR/diamond-price-prediction.git
cd diamond-price-prediction

• Create a virtual environment (optional but recommended):

python -m venv venv

• Activate the virtual environment:
  • On Windows:

  venv\Scripts\activate

  • On macOS/Linux:

  source venv/bin/activate

Usage :

• Open the Jupyter Notebook:

jupyter notebook

• Navigate to the Diamond_Price_Prediction.ipynb notebook and open it.
• Follow the instructions in the notebook to run the code cells.

Dataset Source Link :

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

Models :

• Linear Regression
• Lasso(L1 Regularization)
• Ridge(L2 Regularization)
• ElasticNet
• DecisionTreeRegressor
• RandomForestRegressor
• SVR(Support Vector Regression)

Results :

• LinearRegression Model Training Performance :

  • RMSE: 1013.9047094344004
  • MAE: 674.0255115796832
  • R2 score 93.68908248567512

• Lasso Model Training Performance :

  • RMSE: 1013.8784226767013
  • MAE: 675.0716923362161
  • R2 score 93.68940971841704

• Ridge Model Training Performance :

  • RMSE: 1013.9059272771647
  • MAE: 674.0555800798206
  • R2 score 93.68906732505937

• Elastic net Model Training Performance :

  • RMSE: 1533.4162456064048
  • MAE: 1060.7368759154729
  • R2 score 85.56494831165182

• DecisionTreeRegressor Model Training Performance :

  • RMSE: 838.7860494085679
  • MAE: 423.35310763649716
  • R2 score 95.68082679027064

• RandomForestRegressor Model Training Performance :

  • RMSE: 610.7311352754283
  • MAE: 311.5482746987208
  • R2 score 97.71019581207089

Contributing :

If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!

License :

This project is licensed under the MIT License - see the LICENSE file for details.