• About
• Dataset Description
• Impact
• Model Development and Evaluation
  • Model Selection
  • Feature Engineering
  • Model Training
  • Model Evaluation
• Environment Setup and Usage
• User Interface and Visualizations
  • Prediction Form Interface
  • Detailed Result Page
• MIT License

This project develops a predictive model for estimating diamond prices based on their characteristics such as carat, cut, color, and clarity. It includes data preprocessing, feature engineering, model selection, training, and evaluation, culminating in a web application for users to input diamond attributes and receive price predictions.

The application of this predictive model aims to democratize access to accurate pricing information, empowering stakeholders across the diamond industry to make informed decisions. Whether buying, selling, or managing inventory, users benefit from enhanced transparency and predictive insights, thereby driving efficiency and competitiveness within the market.

The dataset utilized in this project encompasses comprehensive attributes of diamonds, crucial for predictive analysis and modeling. These attributes include:

This table summarizes the dataset attributes used in the project, providing a clear overview of each attribute's description and significance in the analysis of diamond pricing.

Accurate diamond price prediction aids buyers, sellers, and jewelers in making informed decisions. This model helps buyers find fair prices, assists sellers in setting competitive rates, and enables jewelers to manage inventory effectively.

Moreover, by leveraging data-driven insights, stakeholders can anticipate market trends and fluctuations, thereby optimizing their business strategies. This predictive capability fosters transparency and trust within the diamond industry, enhancing overall market efficiency and consumer satisfaction.

In this section, we delve into the process of developing and evaluating the diamond price prediction model.

The project explores various machine learning models to determine the most suitable for predicting diamond prices. Models considered include:

• Linear Regression: A baseline model that establishes a linear relationship between diamond attributes and price.

• Random Forest: A more complex ensemble model that can capture nonlinear relationships and interactions between features.

The selection is based on metrics such as training time, prediction accuracy, and the ability to handle the dataset's complexity.

Feature engineering plays a crucial role in enhancing model performance by extracting meaningful insights from the diamond dataset. Key techniques applied include:

• Normalization: Scaling numeric features like carat weight and dimensions to a standard range.

• Encoding Categorical Variables: Transforming categorical variables such as cut quality and color grade into numerical representations suitable for modeling.

• Feature Selection: Identifying and selecting the most relevant features that contribute significantly to price prediction.

Once the model and features are selected, the next step involves training the model on the prepared dataset. The training process includes:

• Splitting Data: Dividing the dataset into training and testing sets to evaluate model performance.

• Training the Model: Using the training set to fit the chosen model on diamond attributes and their corresponding prices.

• Hyperparameter Tuning: Optimizing model parameters to achieve better performance and generalization.

After training, the model's effectiveness is evaluated using various evaluation metrics, including:

• Mean Absolute Error (MAE): Average absolute difference between predicted and actual diamond prices.

• Root Mean Squared Error (RMSE): Square root of the average squared differences between predicted and actual prices, providing a measure of model accuracy.

• R-squared (R^2): Indicates how well the model explains the variability in diamond prices.

These metrics help gauge the model's accuracy, reliability, and suitability for practical applications in the diamond industry.

This repository provides a comprehensive machine learning project for diamond price prediction. Follow these steps to set up the environment and use the Diamond Price Estimator:

• Clone the repository:

  git clone https://github.com/virajbhutada/Diamond-Price-Estimator
  cd Diamond-Price-Estimator

• Create and activate a virtual environment:

  conda create -p venv python==3.8
  conda activate venv

• Install necessary libraries:

  pip install -r requirements.txt

• Run the application:

  python app.py

• Access the prediction interface: Open your web browser and go to http://localhost:5000 to use the diamond price prediction form.

This streamlined approach ensures a smooth setup and usage process for the Diamond Price Estimator project, facilitating seamless interaction with the prediction model. Adjust commands and paths as necessary based on your specific environment and requirements.

Explore the intuitive user interface and detailed visualizations of the Diamond Price Estimator project, showcasing its functionality and usability.

This interface facilitates easy input of carat weight, cut quality, color grade, and clarity rating for precise price predictions. Designed for intuitive interaction, it features clear labels and responsive design, ensuring accessibility across devices and empowering informed decision-making.

After submitting the diamond attributes via the prediction form, users access a detailed result page showcasing the predicted diamond price. This transparent presentation clarifies the factors influencing the price estimate, empowering users with insightful understanding.

These visuals not only demonstrate the functionality of the Diamond Price Estimator but also highlight its user-friendly design and practical application in the diamond industry.

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