chapter 10

ch10/README.md

@@ -0,0 +1,57 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 10: Predicting Continuous Target Variables with Regression Analysis
+
+### Chapter Outline
+
+- Introducing linear regression
+  - Simple linear regression
+  - Multiple linear regression
+- Exploring the Housing dataset
+  - Loading the Housing dataset into a data frame
+  - Visualizing the important characteristics of a dataset
+  - Looking at relationships using a correlation matrix
+- Implementing an ordinary least squares linear regression model
+  - Solving regression for regression parameters with gradient descent
+  - Estimating coef cient of a regression model via scikit-learn
+- Fitting a robust regression model using RANSAC
+- Evaluating the performance of linear regression models
+- Using regularized methods for regression 
+- Turning a linear regression model into a curve – polynomial
+regression
+  - Adding polynomial terms using scikit-learn
+  - Modeling nonlinear relationships in the Housing dataset
+- Dealing with nonlinear relationships using random forests
+  - Decision tree regression
+  - Random forest regression
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch10.ipynb`](ch10.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.