During the Machine Learning Data Cleaning process, you will often need to figure out whether you have missing values in the data set, and if so, how to deal with it. In this video,
I have demonstrated using python hands-on with below steps how will you use the prediction model to impute the missing values :-
1. We create a predictive model to estimate values that will substitute the missing data.
2. We divide our data set into two sets: One set with no missing values for the variable and another one with missing values.
3. First data set become training data set of the model while second data set with missing values is test data set and variable with missing values is treated as target variable.
4. We create a model to predict target variable based on other attributes of the training data set and populate missing values of test data set.
5. The regression or classification model can be used for the prediction of missing values depending on nature (categorical or continuous) of the feature having missing value.
#DataScience #MachineLearning #ArtificialIntelligence
Python Notebook Link : https://github.com/atulpatelDS/Youtube/blob/main/Data_Cleaning/Hands-on%20Missing%20value%20handling%20using%20Prediction%20Model%20in%20Machine%20LearningData%20Cleaning%20Tutorial%2010.ipynb
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