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Please note that this a recorded webinar. It was recorded during live presentation.
Building ML model is easy if you know your data well. According to a survey in Forbes, data scientists spend 80% of their time on data preparation. The features in your data are important to the predictive models you use and will influence the results you are going to achieve. Though Deep learning takes care of feature engineering process by itself, but many ML model still require to go through this Feature Engineering phase. In this session I will be presenting on various feature engineering techniques such as Imputation, Handling Outliers, Binning, Log Transform, One-Hot Encoding, Grouping Operations, Feature Split, Scaling, Handling Date. It will be a demo heavy session where we will explore different techniques of Feature engineering.
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