https://github.com/daniel1kp/diamond-price-predictor

💎Diamond Price Predictor is an user-friendly web application that simplifies the analysis and prediction of diamond prices.
https://github.com/daniel1kp/diamond-price-predictor

altair pandas python scikit-learn streamlit

Last synced: 18 days ago
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💎Diamond Price Predictor is an user-friendly web application that simplifies the analysis and prediction of diamond prices.

• Host: GitHub
• URL: https://github.com/daniel1kp/diamond-price-predictor
• Owner: daniel1kp
• Created: 2023-10-28T13:04:21.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-03T18:00:07.000Z (9 months ago)
• Last Synced: 2024-11-15T00:19:19.327Z (3 months ago)
• Topics: altair, pandas, python, scikit-learn, streamlit
• Language: Python
• Homepage:
• Size: 704 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Diamond Price Predictor

## Overview

The Diamond Price Predictor is a simple web application that allows users to upload diamond-related data, visualize the data, and predict the price of a diamond based on various features. It's a handy tool for exploring and analyzing diamond data.

## Features

- Upload CSV data files.

- Visualize data with scatter plots, bar plots, and line plots.

- Train a machine learning model on the data.

- Predict diamond prices based on user inputs (carat, depth, table, dimensions).

## Installation

1. Clone the repository to your local machine:

   ```bash

   git clone https://github.com/daniel1kp/diamond-price-predictor.git

2. Change to the project directory:

   ```bash

   cd diamond-price-predictor

3. Install the required Python libraries:

   ```bash

   pip install -r requirements.txt

## Usage

1. Run the web app using Streamlit:

   ```bash

   streamlit run main.py

2. Open the app in your web browser by following the link provided.

3. Upload your diamond data in CSV or JSON format.

4. Choose a case (Case 1, Case 2, or Case 3) to preview and analyze data.

5. Visualize data using the available options in the dropdown.

6. Use the sidebar to input diamond parameters and predict the price.

## Data Sources

The dataset used in this project can be found on Kaggle. You can access the dataset [here](https://www.kaggle.com/datasets/shivam2503/diamonds). 

## Deployment

The web application can be accessed [here](https://diamond-price-prediction-application.streamlit.app/).