#### Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Preface
1. Fundamentals
Free Chapter
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

# Multiple Linear Regression

We have already covered regular linear regression, as well as linear regression with polynomial and other terms, and considered training them with both the least squares method and gradient descent. This section of the chapter considers an additional type of linear regression: multiple linear regression, where more than one variable (or feature) is used to construct the model. In fact, we have already used multiple linear regression without calling it as such—when we added dummy variables, and again when we added the sine and cosine terms, we were fitting multiple x variables to predict the single y variable.

Let's consider a simple example of where multiple linear regression naturally arises as a modeling solution. Suppose you were shown the following chart, which is the total annual earnings of a hypothetical tech worker over a long career. You can see that over time, their pay increased, but there are some odd jumps and changes in the data...