Diamonds ML Project

A data analysis and machine learning project focused on bulding regression model to predict diamond prices.

dataset overview

The dataset contains 10 attributes of almost 54000 diamonds.

• price: price in US dollars (numeric)
• carat: weight of the diamond (numeric)
• cut: quality of the cut (categorical)
• color: diamond color (categorical)
• clarity: diamond clarity (categorical)
• depth total depth percentage (numeric)
• table: width of top of diamond relative widest point (numeric)
• x,y,z: length, width, depth in mm (numeric)

table of content

• EDA
• Preprocessing
• ML models
• Performance comparison
• Visualizations and results

results

Model	R² Score	MAE	RMSE
Linear Regression	0.915	802	1160
Decision Tree	0.976	319	619
XGBoost	0.980	279	559

project structure

• figures/ – plots and visualizations
• results – performance metrics
• Diamonds_analysis.ipynb – project notebook