Skip to content

This is a repo comprising of S5 AI and ML lab Programs. Programs are designed for educational purposes and demonstrate AI and ML concepts.

Notifications You must be signed in to change notification settings

joemathew2004/S5_AI-Lab-and-ML-Lab-Programs

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AI & ML Programs for S5 Lab Experiments

A collection of artificial intelligence and machine learning algorithms implemented in Python for academic coursework.

Search Algorithms

  • 8puzzle.py - 8-puzzle solver implementation
  • eight_puzzle_challenge.py - Enhanced 8-puzzle with additional challenges
  • bfs.py - Breadth-First Search algorithm
  • best_fs_and_A_star.py - Best-First Search and A* search algorithms
  • water_jug.py - Water jug problem solver
  • travelling_salesman.py - Traveling Salesman Problem implementation

Game Theory & Adversarial Search

  • alpha_beta.py - Alpha-Beta pruning algorithm
  • min_max.py - Minimax algorithm implementation

Constraint Satisfaction Problems

  • map_colouringCSP.py - Map coloring using constraint satisfaction

Local Search

  • localsearch.py - Local search algorithms implementation

Machine Learning Algorithms

Classification

  • decision_tree_computers.py - Decision tree for computer purchase prediction
  • decisiontree1.py - Basic decision tree implementation
  • naive_bayes.py - Naive Bayes classifier
  • naive_withput.py - Naive Bayes without libraries
  • randomforest.py - Random Forest classifier
  • SVM_____.py - Support Vector Machine implementation
  • svm_without.py - SVM without external libraries

Regression

  • linear_regresson.py - Linear regression implementation
  • regression_iris.py - Regression analysis on Iris dataset
  • regressions.py - Multiple regression techniques

Clustering

  • k_means.py - K-Means clustering algorithm
  • kmeans_without.py - K-Means implementation without libraries

Neural Networks

  • Neural_.py - Neural network implementation

Dimensionality Reduction

  • PCA.py - Principal Component Analysis

Datasets

  • Buy_Computer.csv - Computer purchase dataset
  • PlayTennis.csv - Tennis playing conditions dataset
  • Titanic.csv - Titanic passenger dataset

🛠️ Prerequisites

Required Libraries

python>=3.7
numpy
pandas
scikit-learn
matplotlib

🤝 Contributing

This is an academic project for S5 AI & ML lab. Programs are designed for educational purposes and demonstrate AI and ML concepts.

📄 License

Educational use only - S5 AI & ML Laboratory Programs