Data imputation using GMM KNN algorithm in matlab

In this code I implement imputation algorithm to different datasets which is used in the case that the dataset has some missing data, this algorithm can very accurately estimates these missing data. This step is usually prior to any machine learning algorithm using this dataset like classification or model training which can't be done if the dataset has missing data.

The algorithm combine both Gaussian mixture model (GMM) and K nearest neighbor (KNN) methods to get the best estimation to missing data as explained in the video. At the end the imputed dataset is compared to the complete and original dataset to measure how good is the estimation algorithm using normalized root mean square error (NRMS).

Contact me:

email: s-ahmed.saleh@zewailcity.edu.eg

List of all my codes: https://gumroad.com/ahmedsaleh

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Hire me directly on freelancer:

https://www.freelancer.com/u/AhmedSobhiSaleh

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Data imputation, GMM, Gaussian mixture model, KNN, K nearest neighbor, missing data estimation, Machine learning, data classification, training set, testing set, calssifiers preformance, NRMS error,