Diamonds Price Prediction

Overview

This repository contains a project focused on predicting diamond prices using the Diamonds Price Dataset. With features ranging from carat weight to clarity, the goal is to develop a robust machine learning model that accurately forecasts diamond valuations.

Dataset

The dataset includes the following columns:

• carat: Diamond weight in carat
• cut: Diamond cutting quality
• color: Diamond color ranging from J (worst) to D (best)
• clarity: Clarity measure from I1 (worst) to IF (best)
• x: Diamond length in mm
• y: Diamond width in mm
• z: Diamond depth in mm
• depth: Percentage depth (z / mean(x,y))
• table: Width of the widest point at the top of the diamond
• price: Target variable representing diamond price

Objectives

1. Data Exploration: Analyze diamond attributes and their correlation with prices.
2. Model Development: Train machine learning models to predict diamond prices.
3. Evaluation: Assess model performance using appropriate metrics.
4. Insights: Extract meaningful insights for diamond valuation.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

How to Use

1. Clone the repository: git clone https://github.com/wonderakwei/diamonds-price-prediction.git

2. Navigate to the project directory: cd diamonds-price-prediction

3. Install required packages: pip install -r requirements.txt

4. Run the Jupyter Notebook or Python scripts to explore and train models.