Algorithms in Python

AI Coding fundamentals and problem solving

Decision Tree Classifier with Scikit-Learn

Implemented and evaluated a Decision Tree Classifier using the scikit-learn library in Python. This project demonstrates applying supervised machine learning techniques to classify datasets, tuning model parameters, and analyzing performance. Delivered both the working code and a structured write-up to document methodology and results.

Key Contributions:

• Built a Decision Tree Classifier with scikit-learn.
• Preprocessed datasets and split into training/testing sets.
• Tuned hyperparameters (e.g., max depth, criterion) for performance optimization.
• Evaluated model accuracy and interpretability using confusion matrices and decision boundaries.
• Produced a professional report (see PDF) explaining process and results.
Skills Demonstrated:

• Machine Learning (Decision Trees, supervised classification)
• Python (pandas, scikit-learn, matplotlib)
• Model evaluation and validation techniques
• Technical writing & clear result communication
Links:

Decision Tree Classifier with Scikit-Learn

Decision-Tree-Classifier

Explored probabilistic models and reasoning under uncertainty using Python implementations from the AIMA codebase. This project demonstrates the ability to work with probability distributions, conditional independence, and algorithms that underpin Bayesian reasoning in AI.

Key Contributions:

  • Designed implementations of classical search algorithms (e.g., BFS, DFS, Uniform Cost, A*).
  • Applied algorithms to problem-solving scenarios such as graph traversal and pathfinding.
  • Compared performance trade-offs between uninformed and informed search strategies.
  • Documented methodology, implementation details, and results in a structured report.
Skills Demonstrated:

  • Python (algorithm design and debugging)
  • Artificial Intelligence foundations (search, heuristics, pathfinding)
  • Data structures (queues, stacks, priority queues, graphs)
  • Complexity analysis and performance comparison
  • Technical writing and communication of results
Links:

Applied reinforcement learning concepts by exploring Markov Decision Processes (MDPs) using the AIMA Python codebase. This project demonstrates understanding of probabilistic decision-making, policy evaluation, and the use of open-source frameworks to study artificial intelligence foundations.

Key Contributions:

  • Worked with AIMA Python implementations to explore decision-theoretic models.
  • Analyzed the structure of MDPs and their role in reinforcement learning.
  • Investigated policy generation and evaluation through provided code (mdp.py).
  • Produced a written report to connect theoretical concepts with practical code execution.
Skills Demonstrated:

  • Reinforcement Learning foundations (MDPs, policies, transitions)
  • Python (using and extending open-source code)
  • AI frameworks & algorithms (AIMA Python)
  • Technical writing & concept explanation
Links:

Markov-Decision-Process

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Implemented and tested fundamental AI search algorithms in Python as part of coursework in CSCI 3202. This project demonstrates understanding of state-space search, algorithm design, and performance evaluation.

Key Contributions:

  • Designed implementations of classical search algorithms (e.g., BFS, DFS, Uniform Cost, A*).
  • Applied algorithms to problem-solving scenarios such as graph traversal and pathfinding.
  • Compared performance trade-offs between uninformed and informed search strategies.
  • Documented methodology, implementation details, and results in a structured report.
Skills Demonstrated:

  • Python (algorithm design and debugging)
  • Artificial Intelligence foundations (search, heuristics, pathfinding)
  • Data structures (queues, stacks, priority queues, graphs)
  • Complexity analysis and performance comparison
  • Technical writing and communication of results
Links:

Search Algorithms for AI Problem Solving

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