"Comprehensive guide to the K-Nearest Neighbors (KNN) algorithm for both classification and regression tasks, featuring in-depth analysis, hyperparameter tuning, and evaluation using Python’s sklearn library."
This repository contains two Jupyter notebooks demonstrating the use of the K-Nearest Neighbors (KNN) algorithm for both classification and regression tasks. It offers a thorough exploration of the algorithm's capabilities, including data preprocessing, model tuning, and performance evaluation.
The KNN algorithm is versatile, being applicable to both categorical target variables (classification) and continuous target variables (regression). These notebooks provide a detailed guide on how to effectively implement and optimize KNN for different types of data and objectives.
KNN_Classifier_Model.ipynb: Focuses on using KNN for classification tasks.KNN_Regressor_Model.ipynb: Focuses on using KNN for regression tasks.
- Data preprocessing techniques tailored to KNN requirements
- Hyperparameter tuning with GridSearchCV to find optimal settings
- Evaluation of model performance using appropriate metrics (e.g., accuracy and F1 score for classification; MSE and R-squared for regression)
- Visualizations to aid in understanding model behavior and results
- pandas and numpy for data handling and operations
- sklearn for model building, tuning, and evaluation
- matplotlib and seaborn for plotting and data visualization
These notebooks are intended as educational tools for those looking to deepen their understanding of machine learning applications or for practitioners needing a reference for implementing KNN in real-world scenarios.
Contributions to improve or expand these notebooks are highly welcome. Suggestions could include more advanced preprocessing methods, exploration of different distance metrics for KNN, or addition of more complex datasets.