A collection of artificial intelligence and machine learning algorithms implemented in Python for academic coursework.
- 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
- alpha_beta.py - Alpha-Beta pruning algorithm
- min_max.py - Minimax algorithm implementation
- map_colouringCSP.py - Map coloring using constraint satisfaction
- localsearch.py - Local search algorithms implementation
- 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
- linear_regresson.py - Linear regression implementation
- regression_iris.py - Regression analysis on Iris dataset
- regressions.py - Multiple regression techniques
- k_means.py - K-Means clustering algorithm
- kmeans_without.py - K-Means implementation without libraries
- Neural_.py - Neural network implementation
- PCA.py - Principal Component Analysis
- Buy_Computer.csv - Computer purchase dataset
- PlayTennis.csv - Tennis playing conditions dataset
- Titanic.csv - Titanic passenger dataset
python>=3.7
numpy
pandas
scikit-learn
matplotlib
This is an academic project for S5 AI & ML lab. Programs are designed for educational purposes and demonstrate AI and ML concepts.
Educational use only - S5 AI & ML Laboratory Programs