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Homeworks and assignments for CSIS 260 (Introduction to Artificial Intelligence) at the University of Balamand (Spring 2025–2026). Includes dataset preparation, data visualization, classification, linear regression, and decision tree/random forest tasks implemented in Python using scikit-learn.

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AI Course Assignments Repository

This repository contains my coursework deliverables:

  • assignment1/
  • assignment2/
  • midtermproject/
  • finalproject/

Each folder includes the related datasets and Jupyter notebooks (.ipynb).


Folder Structure

. ├── assignment1/ │ ├── *.ipynb │ └── dataset files ├── assignment2/ │ ├── *.ipynb │ └── dataset files ├── midtermproject/ │ ├── *.ipynb │ └── dataset files └── finalproject/ ├── *.ipynb └── dataset files


How to Run (Google Colab)

  1. Open any notebook (.ipynb) from one of the folders.

  2. Upload it to Google Colab (or open it directly from GitHub in Colab).

  3. Make sure the dataset path matches your notebook:

    • If your dataset is inside the repo folder (example: assignment1/data/...), keep the same structure in Colab.
    • You can also upload the dataset to Colab or mount Google Drive if needed.
  4. Click Runtime → Run all.


Notes

  • Notebooks are the main deliverables for each folder.
  • Plots and results are generated directly inside the notebooks.
  • If a notebook needs an extra library, it will install it inside the notebook (only when necessary).

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Homeworks and assignments for CSIS 260 (Introduction to Artificial Intelligence) at the University of Balamand (Spring 2025–2026). Includes dataset preparation, data visualization, classification, linear regression, and decision tree/random forest tasks implemented in Python using scikit-learn.

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