Welcome! This repository contains a series of interactive tutorials designed to help heritage professionals use machine learning techniques—specifically Factor Analysis and Feature Importance—to prioritize preservation decisions.

No programming experience is required. These tutorials run in your web browser using Google Colab.

To use these tutorials, you need:

1. A Google account (to run Google Colab).
2. The sample dataset (heritage_data.csv).

First, you need to download the sample dataset to your computer.

1. Click here to view heritage_data.csv on GitHub
2. Click the Download raw file button (the icon that looks like a tray with a downward arrow) on the right side of the screen.
3. Save the file to a known location on your computer (e.g., your Downloads folder or Desktop).

Note: You will need to upload this file to Google Colab each time you start a new notebook.

Click the links below to open each tutorial directly in Google Colab. We recommend going through them in order.

Open in Google Colab

• What it does: Teaches you how to "clean" your data so computers can understand it.
• Key skills: Handling missing values, converting text categories (like "Brick" or "Stone") into numbers, and standardizing different units.
• Output: A clean dataset ready for analysis.

Open in Google Colab

• What it does: Introduces basic statistical tools to explore your data before using complex models.
• Key skills: Correlation analysis (seeing which variables move together) and simple regression.
• When to use: When you have a small dataset (fewer than 50 buildings) or just a few variables.

Open in Google Colab

• What it does: Finds hidden patterns in your data by grouping related variables.
• Key skills: Identifying underlying drivers of deterioration (e.g., "Moisture Stress" vs. "Thermal Stress").
• Goal: Simplify complex data into meaningful themes.

Open in Google Colab

• What it does: Ranks your variables to see which ones best predict a specific outcome (like Condition Rating).
• Key skills: Using machine learning models (Random Forest) to answer "Which factor matters most?"
• Goal: Prioritize what to monitor or fix first.

Open in Google Colab

• What it does: Generates publication-quality charts and graphs.
• Key skills: Creating correlation heatmaps, boxplots, and scatter plots to communicate your findings.

When you open a notebook link, follow these steps:

1. Connect: Click the "Connect" button in the top right corner to start the server.
2. Upload Data:
   • Click the Folder icon 📁 on the left sidebar.
   • Click the Upload icon (a paper with an up arrow) at the top of the sidebar.
   • Select the heritage_data.csv file you downloaded in Step 1.
3. Run Code:
   • You can run each "cell" of code by clicking the Play button ▶️ next to it.
   • Or, go to the top menu and select Runtime > Run all to run the entire analysis at once.

Tip: If you get an error saying FileNotFoundError: [Errno 2] No such file or directory: 'heritage_data.csv', it means you haven't uploaded the data file yet! Just follow the "Upload Data" steps above and try again.