https://github.com/singhxtushar/diamondpriceprediction
This is a DiamondPricePrediction project with fully automated pipelines, logging, exception handling and with setup module.
https://github.com/singhxtushar/diamondpriceprediction
automation machine-learning modular-programming pipelines regression-models
Last synced: 8 months ago
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This is a DiamondPricePrediction project with fully automated pipelines, logging, exception handling and with setup module.
- Host: GitHub
- URL: https://github.com/singhxtushar/diamondpriceprediction
- Owner: SINGHxTUSHAR
- License: mit
- Created: 2023-11-16T04:08:43.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-19T10:40:49.000Z (almost 2 years ago)
- Last Synced: 2023-11-20T10:25:15.971Z (almost 2 years ago)
- Topics: automation, machine-learning, modular-programming, pipelines, regression-models
- Language: Jupyter Notebook
- Homepage: http://127.0.0.1:5000/predict
- Size: 8.17 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#features)
- [Requirements](#requirements)
- [Setup](#setup)
- [Usage](#usage)
- [Data](#data)
- [Models](#models)
- [Results](#results)
- [Contributing](#contributing)
- [License](#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:
```bash
pip install -r requirements.txt
```
## Setup :
- Clone the repository:
```bash
git clone https://github.com/SINGHxTUSHAR/diamond-price-prediction.git
cd diamond-price-prediction
```
- Create a virtual environment (optional but recommended):
```bash
python -m venv venv
```
- Activate the virtual environment:
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS/Linux:
```bash
source venv/bin/activate
```
## Usage :
- Open the Jupyter Notebook:
```bash
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](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.