Welcome to the Diamond Price Prediction project repository! This project aims to develop a machine learning model to predict the prices of diamonds based on various features such as carat weight, cut, color, clarity, and depth.

• About
• Dataset
• Getting Started

The Diamond Price Prediction project utilizes a dataset containing information about thousands of diamonds. By analyzing this dataset and training a machine learning model, the project aims to provide accurate predictions of diamond prices based on their characteristics.

The goal of this project is to showcase the process of building a predictive model using machine learning techniques. It serves as a learning resource for individuals interested in understanding the steps involved in data preprocessing, feature engineering, model training, and evaluation.

The dataset used in this project is available in the dataset directory. It includes various attributes of diamonds, such as carat weight, cut, color, clarity, depth, table, and price. The dataset is in CSV (Comma-Separated Values) format.

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

To get started with the Diamond Price Prediction project, follow these steps: (Create your own virtual environment)

1. Clone the repository to your local machine:

   git clone https://github.com/Rishi2403/Diamond-Price-Prediction.git
   

2. Install the required dependencies by running the following command:

   pip install -r requirements.txt
   

3. Load the model:(Could not upload here because the model is too large and github does not support it)

   python src/pipelines/prediction_pipeline.py
   

4. python application.py

5. Go to localhost:5000

5. Click On Get Started

6. Enter Required Information:

Future_work: Shall deploy on aws cloud platform.

Note: The Diamond Price Prediction project is for educational and demonstration purposes only. The provided predictions should not be considered as financial or investment advice. Always consult with domain experts or professionals before making any decisions based on the predictions generated by this project.