Diamond Dataset Machine Learning Project

Built a machine learning workflow to analyze and model the Diamond dataset. Conducted exploratory data analysis (EDA) to detect null values, duplicates, and anomalies. Created 1D, 2D, and 3D visualizations to extract insights and understand feature relationships. Encoded categorical data using Label Encoding and One-Hot Encoding and scaled numeric features appropriately with Standard Scaler, Robust Scaler, or log/power transformations. This project demonstrates the application of ML techniques, data preprocessing, and feature engineering to deliver accurate and reliable predictions.