Using Sklearn Package for KNN

Using Sklearn Package for KNN | Python Machine Learning Tutorial
In this video, we demonstrate how to implement the K-Nearest Neighbors (KNN) algorithm using the Sklearn (Scikit-learn) package in Python. Sklearn provides a simple and efficient way to build both KNN classifiers and KNN regressors, making it a popular choice for machine learning tasks.
Topics covered in this tutorial include:
Introduction to Sklearn: Overview of the Scikit-learn library and how it simplifies the process of building machine learning models.
KNN Classifier with Sklearn: Step-by-step guide to implementing K-Nearest Neighbors for classification problems using the KNeighborsClassifier class from Sklearn.
KNN Regressor with Sklearn: How to use KNeighborsRegressor for regression tasks to predict continuous values based on the nearest neighbors.
Choosing the Optimal K: How to select the best value of K for your dataset and its impact on model performance. We’ll demonstrate how to use cross-validation to find the optimal K.
Distance Metrics in Sklearn: Understanding how different distance metrics, such as Euclidean and Manhattan, are used to measure the similarity between data points.
Model Training and Fitting: How to train your KNN model with a given dataset and fit the model to make predictions.
Scaling Features: Why feature scaling is essential in KNN and how to use StandardScaler or MinMaxScaler to standardize your features before training the model.
Evaluating Model Performance: How to evaluate the performance of your KNN model using metrics such as accuracy, precision, recall, confusion matrix, or mean squared error (MSE).
Hyperparameter Tuning: Introduction to tuning KNN hyperparameters such as weights, algorithm, and leaf size to improve model performance.
By the end of this video, you'll have a clear understanding of how to implement KNN for classification and regression tasks using the Sklearn package and how to optimize the model for better results.
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