# Kaggle Community Competition: Predicting Preservation Score Performance

## Overview

score of ancient treasure items. The preservation score reflects how well an artifact can be expected to endure over time, taking into account factors like material composition, craftsmanship, and environmental exposure. This is a regression problem where students must draw on both structured and unstructured data to estimate the item's preservability. Success will require not only understanding the physical and historical features of each artifact, but also effectively integrating multimodal inputs such as images and text descriptions.

Each record in the dataset describes a unique treasure item, including its physical dimensions (weight, height, width, depth), materials used (primary and secondary materials, gem type, gem count), and aesthetic or symbolic features (color, surface texture, inscription, pattern type). Historical and contextual details such as origin culture, creation year, restoration status, and authentication status provide additional depth. Categorical variables like item type, rarity level, and environment type may correlate with preservation potential, while flags such as cursed status or magic enchanted add a layer of fantasy-themed complexity. Students are also provided with a free-text description and a photograph of each artifact, enabling them to experiment with computer vision or natural language processing techniques to extract predictive signals from unstructured data.

### What is the Preservation Score?

The **preservation_score** is a single numeric value (0–100) that estimates how well an ancient treasure item is likely to withstand the test of time. This score reflects a combination of environmental exposure, material resilience, and current condition. Items found in protected environments like tombs or caves tend to score higher, while those from corrosive or exposed locations such as oceans or battlefields may score lower. Additional factors influencing the score include the artifact’s primary material, level of oxidation, restoration efforts, craftsmanship quality, surface texture, and even size. While the exact scoring formula is not provided, students can assume it reflects a weighted combination of these attributes, with some random noise added to simulate real-world measurement variability.

Each treasure item in the dataset is described using a rich set of features. These include physical dimensions (weight, height, width, depth), material properties (e.g., gold, bronze, gem count), aesthetic qualities (surface texture, shine factor), and contextual data such as origin culture, creation year, and restoration status. Categorical fields like item_type, rarity_level, and magic_enchanted provide further nuance. To support multimodal modeling, each item also comes with a textual description and a photograph, enabling students to explore natural language processing and computer vision techniques alongside traditional tabular modeling.

## The Dataset

The competition dataset is rich and diverse, incorporating information from three distinct sources:

1. **Tabular Data:** A structured dataset containing detailed specifications for each item, including physical characteristics and other information.
2. **Item Images:** Each item is visually represented by an AI-generated image. However, please note that these images may not always reflect the most up-to-date specifications, as errors can occur.
3. **Descriptive Text:** Unstructured textual descriptions of each item, offering qualitative insights into its intended purpose and functionality. For example:

   *"Forged in the shadowy workshops of a legendary pirate stronghold, this rare bronze amulet whispers tales of maritime adventure and hidden treasures. Adorned with three emeralds and bearing intricate geometric patterns, the piece captures the raw elegance of maritime craftsmanship against the opulent backdrop of a forgotten palace."*

## Challenge Details

Your task is to analyze the provided dataset and build a predictive model that can accurately estimate the **preservation_score** for new items. The competition will test your ability to handle structured, unstructured, and visual data sources to make well-informed predictions.

### Key Considerations:
- **Feature Engineering:** The dataset includes both numerical and categorical features that impact performance. Exploring interactions between them may improve your model.
- **Multi-Modal Learning:** While the main target variable is derived from tabular data, leveraging image and text descriptions might enhance prediction accuracy.
- **Generalization:** Your model should perform well on unseen items, meaning it must capture general trends rather than overfitting to existing examples.

## Get Started
Explore the dataset, experiment with different modeling approaches, and see how well you can predict the **preservation_score**. This is your opportunity to apply machine learning techniques to item scoring problem!

We look forward to seeing your innovative solutions—good luck!