3.Dataset Missing Values & Imputation (Detailed Python Tutorial) | Impute Missing values in ML

This video "Dataset Missing Values & Imputation (Detailed Python Tutorial) | Impute Missing values in ML" explains how to preprocess data, what are some of the reasons we get this missing data, how to identify the missing values, and the various ways using which we can handle missing values. This is a very important step before we build machine learning models. One can impute missing values by replacing them with mean values, median values or using KNN algorithm. This is a machine learning & deep learning Bootcamp series of data science. You will also get some flavor of data engineering as well in this Bootcamp series. Through this series, you will be able to learn each aspect of the Data science lifecycle right from collecting data from disparate data sources, data preprocessing to doing visualization as well as model deployment in production. You will also see how to perform data preprocessing and build, regression, classification, clustering as well as a recurrent neural network, convolution neural network, autoencoders, etc. Through this series, you will be able to learn everything pertaining to Machine and Deep Learning in one place. Content & Playlist will be updated regularly to add videos with new topics. ********Git Hub Link for DataSet and Python Code********* https://github.com/nitinkaushik01/Machine_Learning_Data_Preprocessing_Python