Stock Price Prediction using Machine Learning - Videos | GeeksforGeeks

In this video, we are going to cover KNN implementation using Python with examples to predict the stocks. We will cover stock data prediction using classification and regression problems. One of the most significant classification methods in Machine Learning is KNN, or K-Nearest Neighbors.

In Machine Learning, supervised learning is utilised to discover extensive applications in pattern classification, data mining, and intrusion detection. It's a simple, straightforward supervised machine learning technique that can solve both classification and regression problems.

The K-Nearest Neighbors algorithm assumes that similar things exist in close or nearness in space. In other words. KNN captures the idea of similarity (sometimes called distance, proximity, or closeness) with some mathematics like calculating the distance between points on a graph, There are other ways of calculating distance, and one way might be preferable depending on the problem we are solving.

Related Article: https://www.geeksforgeeks.org/k-nearest-neighbours/ https://www.geeksforgeeks.org/k-nearest-neighbors-with-python-ml/ https://www.geeksforgeeks.org/k-nearest-neighbor-algorithm-in-python/