Path Finding Visualization vs Art of Stat

Want to see various pathfinding algorithms in action? With Path Finding Visualization, you can visualize, in action, six different algorithms. This app allows you to select your own starting and ending points, select the algorithm you want to use, and add walls in order to create boundaries. This app includes the following algorithms: Dijkstra's - Works by expanding outwards until it reaches the target node. Guaranteed to find the shortest path as long as there are not any negative weights. Bidirectional Dijkstra's - Dijkstra's algorithm from both sides. A* - Like Dijkstra's in that it favors nodes close to the starting point, but also favors nodes close to the ending point (heuristic). It does not guarantee the shortest path. A tie-breaker that prefers straight lines was included in order to prevent multiple nodes from having the same F values. Bidirectional A* - The A* algorithm from both sides. Breadth-First Search - Traverses through a graph one level at a time until it finds the target node. Since there are no weights, this algorithm does guarantee the shortest path. Depth-First Search - Traverses through a graph by going as deep as possible and then backtracking until it finds the target node. It does not guarantee the shortest path. Learning about or want to visualize any of the above algorithms in action? Download Path Finding Visualization today and have a better understanding of how each algorithm works.

The Art of Stat: Machine Learning app includes methods for supervised and unsupervised learning, lets you split data into training and test sets, visualizes all methods, including predictions and heatmaps, and lets you assess the accuracy of your algorithm by showing the confusion matrix and more. Included ML algorithms so far include: - Multiple Linear Regression (including categorical predictors and interactions interactions) - Multiple Logistic Regression (including categorical predictors and interactions interactions) - Discriminant Analysis (Linear & Quadratic) - Naive Bayes - K-Means Clustering Functionality: - Provides various datasets (Palmer Penguins, Wine Quality, Heart Disease, Iris Flowers, Credit Card Defaults, ...) or lets user upload their own CSV file - Split data into training and test sets - Standardize Features - Select continuous and/or categorical features (where appropriate) - Visualize all methods (Scatterplots, Heatmaps), predicted labels or posterior probabilities - Assess accuracy by displaying Confusion Matrix and accuracy statistics (including precision and recall) in multiple ways - Make predictions for new observations Modules in preparation: - Decision Trees & Random Forests - Nearest Neighbor