The Diamond Price Prediction project aims to build a machine learning model that can accurately predict the price of diamonds based on various features such as carat weight, cut, color, clarity, and depth. This README file provides an overview of the project, the dataset used, the model pipeline, and instructions to run the pipeline for diamond price prediction.

The dataset used for this project contains information about diamonds, including their carat weight, cut, color, clarity, depth, table, and price. The dataset has been preprocessed and cleaned to remove any missing or irrelevant data. It is split into training and testing sets for model evaluation.

The project has been structured into a pipeline that consists of the following steps:

1. Data Loading: The dataset is loaded from a CSV file into the pipeline for further processing.

2. Data Preprocessing: The pipeline performs data preprocessing, which includes feature scaling, one-hot encoding categorical variables, and splitting the data into training and testing sets.

3. Feature Engineering: Additional features might be created or selected based on domain knowledge to improve the model's performance.

4. Model Training: The pipeline includes the selection and training of a machine learning model for diamond price prediction. Commonly used models for regression tasks like Linear Regression, Lasso Regression, Ridge Regression, ElasticNet Regression are employed.

5. Model Evaluation: The trained model is evaluated using appropriate metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to measure the model's performance.

6. Model Deployment: After successful training and evaluation, the model can be deployed to make real-time predictions on new data.

The Diamond Price Prediction project demonstrates the development of a machine learning model to predict diamond prices based on relevant features. The pipeline encapsulates all the necessary steps, making it easy to reproduce and deploy the model in real-world scenarios.