How to deal with missing values in python in scikit-learn | Data Preprocessing course in hindi
How to deal with missing data or null values is really very important task one should consider. In This video we will learn how we can deal the missing data with scikit-learn api. Scikit-learn is one of the most powerful library for machine learning and we will understand it in detail that how we can use Simple Imputer to replace the missing values in our dataset.
Link for Data Preprocessing Course Resources :
https://drive.google.com/drive/folder...
In this video we have also discussed about the types of missing values we can find in real world dataset and what are the strategy that can be use to replace that missing values. In our previous video we have discussed all the necessary theory which are required to understand the underlying consepts of missing values. And in this video we will heavily focus on how we can implement the consepts which we have learned in scikit-learn simpleImputer api.
About the Data Preprocessing Course :
We will cover all the topics which are necessary to perform on dirty data to make it clean and ready to use for the Machine learning model. The whole course is divided into six chapters and each chapter is explained through Pandas and Scikit-learn both So that your fundaments become stronger and it will definitely help you clean any kind of dirty real-world dataset. For learning purposes, we have also used a raw real-world Car dataset and we will apply our data preprocessing steps to real-world data so you get to know how to deal with the real-world dataset.
In this course,, we will start with Importing Dataset and How to get basic insights from it. then we will learn how to deal with missing values in a dataset followed by Data Formatting and Data Binning, How to Deal with categorical values, Splitting of Dataset and Feature Scaling, and Data Normalization.
About Bit ML :
Bit Ml is a youtube channel on which we upload tutorials regarding Machine Learning in python in the Hindi language so that students get detailed explanations about every topic of machine learning. Our mission is to explain complex topics of machine learning in such an easy manner that students can understand the concepts easily.
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Bit ML.