This GitHub repository is a collection of various artificial intelligence algorithms implemented in Python. These implementations were assigned as part of the curriculum at University-United International University aiming to enhance understanding and practical application of fundamental AI techniques. Each algorithm serves a specific purpose and demonstrates the principles of AI problem-solving in different domains.
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A Search Algorithm
A* (A-star) is a widely-used graph traversal algorithm that finds the shortest path from a starting point to a goal node in a weighted graph. The algorithm efficiently combines the cost of reaching a node from the start and the estimated cost to reach the goal guiding the search towards the most promising paths first.
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K-Means Clustering
K-means is an unsupervised machine learning algorithm used for clustering data into 'K' distinct groups based on similarity. The algorithm iteratively assigns data points to the nearest cluster centroid and recalculates the centroids until convergence effectively partitioning the data into compact and well-separated clusters.
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Linear Regression
Linear regression is a simple yet powerful supervised learning algorithm used for predicting continuous numeric values. This implementation demonstrates how to find the best-fitting line through a given dataset using the least squares method making it possible to make predictions based on new input data.
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Logistic Regression
Logistic regression is a binary classification algorithm used to predict the probability of an event occurring. It models the relationship between the dependent variable and one or more independent variables by fitting a logistic curve to the data and making probabilistic predictions.
- K-Nearest Neighbors (KNN)
KNN is a straightforward and effective supervised classification algorithm used for pattern recognition and regression tasks. Given a new data point KNN identifies the 'K' closest data points in the training set and classifies or predicts the output based on the majority class or average value of the neighbors.
Each algorithm implementation is organized into separate directories containing the Python script(s) along with necessary datasets or example inputs. Users can clone or download this repository to their local machine and run the Python scripts directly. Furthermore the repository is equipped with clear documentation and comments within the code to aid in understanding each algorithm's logic and functionality.
While the implementations strive for accuracy and correctness they are primarily intended for educational purposes. As such the authors and contributors are not liable for any consequences resulting from the direct use of these implementations in critical or production environments. Users are advised to thoroughly understand the algorithms and conduct appropriate testing before applying them in real-world scenarios.