💎 Diamond Price & Value Prediction with XGBoost

This project builds a machine learning pipeline that predicts diamond prices and evaluates their value-for-money score.

🚀 Project Workflow

• 📊 Exploratory Data Analysis (EDA)
• 🔧 Feature Engineering (OneHotEncoding)
• 📈 Model Training with XGBoost
• 🎯 Hyperparameter Optimization with Optuna
• 🧠 Model Explainability using SHAP
• 🗂️ Value Score Calculation: Predicted / Actual

📌 Main File: diamond_price_pipeline.pkl

You can load the model like this:

import pickle
with open("diamond_price_pipeline.pkl", "rb") as f:
    pipeline = pickle.load(f)

predictions = pipeline.predict(X_new)

## 📂 Folders
├── data/ # Исходные данные
├── model/ # Jupyter-ноутбуки
├── model/ # Готовая модель
└── README.md # Описание проекта