Machine Learning is one of the most popular approaches in Artificial Intelligence. Over the past decade, Machine Learning has become one of the integral parts of our life. It is implemented in a task as simple as recognizing human handwriting or as complex as self-driving cars. By Vibhu Singh.

In this blog, we will give you an overview of the K-Nearest Neighbors (KNN) algorithm and understand the step by step implementation of trading strategy using K-Nearest Neighbors in Python.

K-Nearest Neighbors (KNN) is one of the simplest algorithms used in Machine Learning for regression and classification problem. KNN algorithms use data and classify new data points based on similarity measures (e.g. distance function). Classification is done by a majority vote to its neighbors. The data is assigned to the class which has the nearest neighbors. As you increase the number of nearest neighbors, the value of k, accuracy might increase.

The article is split into:

- Import the libraries
- Fetch the data - the S&P 500 data from Yahoo finance
- Define predictor variable
- Define target variables
- Split the dataset
- Instantiate KNN model
- Create trading strategy using the model
- Sharpe Ratio

Now that you know how to implement the KNN Algorithm in Python, you can start to learn how logistic regression works in machine learning and how you can implement the same to predict stock price movement in Python. Nice one!

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