K-Nearest Neighbors (KNN) Regression

Introduction

K-Nearest Neighbors (KNN) regression is a non-parametric algorithm used for predicting continuous outcomes. It belongs to the family of instance-based learning methods, where predictions are made based on the similarity of new data points to the training data. This repository provides an overview of KNN regression along with examples and implementations in Python.

How KNN Regression Works

In KNN regression, predictions are made by averaging the target values of the k nearest neighbors of the new data point. The distance metric used (e.g., Euclidean distance) determines the similarity between data points. KNN regression is simple to understand and implement, making it a popular choice for regression tasks.

Example Scenario:

Suppose you want to predict the price of a house based on features such as square footage and number of bedrooms. Given a new house with unknown price, KNN regression would find the k nearest neighbors (houses) in the training data based on their features and average their prices to make the prediction.

Key Parameters in KNN Regression

• K: The number of nearest neighbors to consider when making predictions. Choosing the appropriate value of K is crucial as it affects the bias-variance tradeoff of the model.
• Distance Metric: The metric used to compute the distance between data points, such as Euclidean distance, Manhattan distance, or Minkowski distance.

Datasets

This repository includes sample datasets in CSV format that can be used to practice KNN regression.

Getting Started

Requirements

Ensure you have the following dependencies installed on your system:

• JupyterNotebook

Installation

1. Clone the KNN_Regression repository:

git clone https://github.com/sumony2j/KNN_Regression.git

2. Change to the project directory:

cd KNN_Regression

3. Install the dependencies:

pip install -r requirements.txt

Running KNN_Regression

Use the following command to run KNN_Regression:

jupyter nbconvert --execute notebook.ipynb

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Contributing

Contributions are welcome! Here are several ways you can contribute:

• Submit Pull Requests: Review open PRs, and submit your own PRs.
• Join the Discussions: Share your insights, provide feedback, or ask questions.
• Report Issues: Submit bugs found or log feature requests for Knn_regression.

Contributing Guidelines

1. Fork the Repository: Start by forking the project repository to your GitHub account.
2. Clone Locally: Clone the forked repository to your local machine using a Git client.

   git clone https://github.com/sumony2j/KNN_Regression.git

3. Create a New Branch: Always work on a new branch, giving it a descriptive name.

   git checkout -b new-feature-x

4. Make Your Changes: Develop and test your changes locally.
5. Commit Your Changes: Commit with a clear message describing your updates.

   git commit -m 'Implemented new feature x.'

6. Push to GitHub: Push the changes to your forked repository.

   git push origin new-feature-x

7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.

Once your PR is reviewed and approved, it will be merged into the main branch.

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