Data Preprocessing for Machine Learning

Data Preprocessing for Machine Learning

In this guide, we will learn how to do data preprocessing for machine learning. Data Preprocessing is a very vital step in Machine Learning. Most of the real-world data that we get is messy, so we need to clean this data before feeding it into our Machine Learning Model.

In this guide, we will learn how to do data preprocessing for machine learning.

Data Preprocessing is a very vital step in Machine Learning. Most of the real-world data that we get is messy, so we need to clean this data before feeding it into our Machine Learning Model. This process is called Data Preprocessing or Data Cleaning. At the end of this guide, you will be able to clean your datasets before training a machine learning model with it.

Prerequisites:

  • A laptop
  • Jupyter Notebook
  • Basic Python Programming knowledge
  • Sample Dataset (click here to download)

I will be using Jupyter Notebook. To get Jupyter Notebook, you need to install Anaconda. You can follow this tutorial video on how to install Anaconda by clicking here.

In this article, I will cover the following:

  • Importing libraries
  • Importing dataset
  • Handling Duplicate Values
  • Handling Missing values
  • Encoding Categorical data
  • Splitting the dataset
  • Feature Scaling

machine learning python data pre-processing

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