utquarsh027 / diamond_price_prediction

Diamond Price Prediction Project

Introduction

This project aims to predict the price of diamonds using regression analysis. The dataset contains various attributes of diamonds that can influence their price.

About the Data

The dataset includes the following independent variables:

• id: Unique identifier for each diamond.
• carat: Weight measurement exclusive to gemstones and diamonds (Carat - ct.).
• cut: Quality of the diamond cut.
• color: Color grade of the diamond.
• clarity: Measure of the purity and rarity of the diamond.
• depth: Height of the diamond (in millimeters) from the culet to the table.
• table: Facet visible when the stone is viewed face up.
• x: Diamond X dimension.
• y: Diamond Y dimension.
• z: Diamond Z dimension.

Target Variable

• price: Price of the given diamond.

Data Source

The dataset used in this project is sourced from kaggle.

Project Workflow

1. Data Exploration: Understanding the dataset, checking for missing values, and exploring statistical summaries.
2. Data Preprocessing: Handling missing values, encoding categorical variables, and scaling numerical features if required.
3. Feature Engineering: Creating new features or transforming existing ones to improve model performance.
4. Model Building: Training and evaluating regression models to predict diamond prices.
5. Model Evaluation: Assessing model performance using appropriate metrics and validation techniques.
6. Deployment: The model is deployed and available here.

Instructions

1. Clone the repository.
2. Install the required dependencies (requirements.txt).
3. Run the app.py file.