Hello World,
Welcome to #MachineLearningMondays
- Case Study: Data Preprocessing
- DataSet: 'data.csv'
When it comes to creating a Machine Learning model, data preprocessing is the first step marking the initiation of the process. Typically, real-world data is incomplete, inconsistent, inaccurate (contains errors or outliers), and often lacks specific attribute values/trends. This is where data preprocessing enters the scenario – it helps to clean, format, and organise the raw data, thereby making it ready-to-go for Machine Learning models.
Hello World,
Welcome back to #MachineLearningMondays
- Case Study: Simple Linear Regression
- Use Case: Salary vs Work Experience
- DataSet: 'Salary_Data.csv'
Does our salary really grow with our years of working experience?
- In this week’s topic we will use a simple data-driven approach to verify the fact. We will use linear regression to model the relationship between the amount of salary with the years of working experience. If you are new to linear regression, read this article for getting a clear idea about the implementation of simple linear regression. This post will help you to understand how simple linear regression works step-by-step.