Dijkstra's algorithm is a popular graph traversal algorithm that finds the shortest path between nodes in a weighted graph. It was developed by Dutch computer scientist Edsger W. Dijkstra in 1956 and has numerous applications in various fields, including transportation networks, computer networks, and routing protocols.

The project involves building an XGBoost (Extreme Gradient Boosting) algorithm for multi-class classification. The goal is to train a model using the provided training data and evaluate its performance using cross-validation. The trained model is then used to make predictions on the test data, and the feature importance is analyzed.

In this project, we delve into the realm of predictive modeling, pitting Decision Trees against Random Forests. By exploring their strengths, weaknesses, and performance, we aim to uncover which model reigns supreme in solving complex problems.

The k-NN algorithm is a type of supervised learning algorithm that can be used for classification and regression tasks. It works by finding the k-nearest neighbors of a test instance in a labeled training set, and then assigning the test instance the label of the majority class among its k-nearest neighbors.