K-Nearest Neighbors (KNN) Implementation

This repository contains three Jupyter notebooks demonstrating the implementation of the K-Nearest Neighbors (KNN) algorithm on 2D and 3D artificial datasets. The notebooks follow the guide provided by DigitalOcean's community tutorial on KNN in Python.

Notebooks

1. knn_2d.ipynb
2. knn_3d.ipynb
3. knn_no_sklearn.ipynb

Dependencies

The notebooks require the following Python libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

You can install the required libraries using pip:

pip install -r requirements.txt

Description

knn_2d.ipynb

This notebook covers:

• Data Creation: Generate a 2D dataset with 500 samples.
• Visualization: Scatter plot of the dataset.
• Data Splitting: Split into training and testing sets (80-20).
• Model Building: Instantiate KNN models with different neighbor values (1, 5, 10, 100).
• Evaluation: Train and evaluate model accuracy.

knn_3d.ipynb

This notebook covers:

• Data Creation: Generate a 3D dataset with 500 samples.
• Visualization: 3D scatter plot of the dataset.
• Data Splitting: Split into training and testing sets (80-20).
• Model Building: Instantiate KNN models with different neighbor values (1, 5, 10, 100).
• Evaluation: Train and evaluate model accuracy.

knn_no_sklearn.ipynb

This notebook covers:

• Data Creation: Generate a dataset with 500 samples.
• Visualization: 2D and 3D scatter plots of the dataset.
• Data Splitting: Split into training and testing sets (80-20).
• Custom KNN Implementation: Implement the KNN algorithm from scratch without using scikit-learn.
• Evaluation: Train and evaluate model accuracy using the custom KNN implementation.

Usage

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the directory:

   cd <repository-directory>

3. Open the Jupyter notebooks:

   jupyter notebook knn_2d.ipynb
   jupyter notebook knn_3d.ipynb
   jupyter notebook knn_no_sklearn.ipynb

4. Run the cells in each notebook to execute the KNN implementation steps.

References

• DigitalOcean Community Tutorial on KNN in Python

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This README provides a concise guide to understanding and running the provided notebooks. Feel free to modify it according to your needs.