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Hands on KNN using Scikit-learn

Hands-on KNN Classification Using Scikit-Learn in Python In this comprehensive tutorial, learn how to perform K-nearest neighbors (KNN) classification using Python and Scikit-Learn. We start by setting up the environment in Visual Studio Code and installing necessary libraries, including Seaborn, Pandas, and Scikit-Learn. The video then dives into exploratory data analysis (EDA) using the Wine dataset, examining feature names, target names, and performing data frame manipulations. We generate pair plots and a correlation heat map to identify and drop redundant or low-correlation features. After cleaning the data, we build and train a KNN model, tuning hyperparameters with an elbow plot, and discuss best practices to avoid data leakage. Finally, we evaluate the model using confusion matrices and classification reports. Don't forget to stay until the end to grasp the full process from data preparation to model evaluation. 00:00 Introduction to KNN Classification with Scikit-Learn 00:15 Setting Up the Environment 01:15 Loading and Exploring the Wine Dataset 03:28 Data Preparation and EDA 07:52 Feature Selection with Correlation Heatmap 10:43 Implementing K-Nearest Neighbors (KNN) 14:40 Hyperparameter Tuning with Elbow Plot 21:16 Conclusion and Best Practices