Diamond Price Prediction Machine Learning Model

Introduction

This repository contains a machine learning model for predicting the price of diamonds based on various features. The model is built using Python and popular libraries such as scikit-learn and pandas.

Dataset

The dataset used for training and testing the model is sourced from Kaggle. It consists of approximately 54,000 rows and 10 columns, including features like carat weight, cut, color, clarity, and depth.

Model Development

The machine learning model is developed using the following steps:

1. Data Preprocessing: Cleaning and transforming the dataset to handle missing values, categorical variables, and feature scaling.
2. Feature Engineering: Creating new features and selecting relevant features to improve model performance.
3. Model Selection: Trying different machine learning algorithms such as linear regression, random forest, and gradient boosting to find the best-performing model.
4. Model Evaluation: Evaluating the performance of each model using metrics like mean squared error, mean absolute error, and R-squared score.
5. Hyperparameter Tuning: Fine-tuning the hyperparameters of the selected model to optimize its performance.

Model Deployment

Once the model is trained and evaluated, it can be deployed for real-world use. Possible deployment options include:

• Hosting the model as a web service using platforms like Flask or FastAPI.
• Integrating the model into existing applications through APIs.
• Deploying the model on cloud platforms such as AWS, Google Cloud, or Microsoft Azure.

Repository Structure

• data/: Contains the dataset used for training and testing the model.
• notebooks/: Jupyter notebooks for data exploration, preprocessing, model development, and evaluation.
• src/: Python scripts for data preprocessing, model training, and evaluation.
• models/: Saved trained models in serialized format for future use.
• requirements.txt: List of Python dependencies required to run the code.

Usage

To use the model:

1. Clone the repository to your local machine.
2. Install the required dependencies listed in requirements.txt.
3. Run the notebooks or Python scripts in the appropriate order to preprocess the data, train the model, and evaluate its performance.
4. Deploy the trained model using the deployment options mentioned above.

Contributors

• Damodar Yadav

License

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