Handling Missing Values - Machine Learning - Videos | GeeksforGeeks

In this video, we are going to see how to handle missing data in machine learning. If you have some missing values then there are some xgboost or LightGBM that might handle these missing data but there is some algorithm like KNN model, Linear Regression, or Logistic Regression where it is must to handle missing value before putting the data into the machine learning model.

The K-Nearest Neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. The KNN algorithm assumes that similar things exist in close proximity. In other words, similar things are near to each other.

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Linear regression assumes that the data follows a linear function.

Logistic regression is basically a supervised classification algorithm. Contrary to popular belief, logistic regression IS a regression model. The model builds a regression model to predict the probability that a given data entry belongs to the category numbered as “1”. Logistic regression models the data using the sigmoid function.

Related article: https://www.geeksforgeeks.org/ml-handling-missing-values/ https://www.geeksforgeeks.org/k-nearest-neighbors-with-python-ml/ https://www.geeksforgeeks.org/ml-linear-regression/ https://www.geeksforgeeks.org/understanding-logistic-regression/