Book Image

Machine Learning with Python

By : Oliver Theobald
Book Image

Machine Learning with Python

By: Oliver Theobald

Overview of this book

The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.
Table of Contents (18 chapters)
Free Chapter
1
FOREWORD
2
DATASETS USED IN THIS BOOK
3
INTRODUCTION
4
DEVELOPMENT ENVIRONMENT
5
MACHINE LEARNING LIBRARIES
6
EXPLORATORY DATA ANALYSIS
7
DATA SCRUBBING
8
PRE-MODEL ALGORITHMS
9
SPLIT VALIDATION
10
MODEL DESIGN
11
LINEAR REGRESSION
12
LOGISTIC REGRESSION
13
SUPPORT VECTOR MACHINES
14
k-NEAREST NEIGHBORS
15
TREE-BASED METHODS
16
NEXT STEPS
APPENDIX 1: INTRODUCTION TO PYTHON
APPENDIX 2: PRINT COLUMNS

APPENDIX 2: PRINT COLUMNS

 

A code shortcut for printing columns with the necessary formatting to use as input for model prediction, as discussed in Chapter 10, can be generated using the following code.

 

cols = df.columns.tolist()

print("new_project = [")

for item in cols:

print("\t0, "+"#"+item)

print("]")

 

Run the temporary code in Jupyter Notebook.

 

Image

 

Now copy and paste the code output you have generated back into the notebook for the next section of your code. Also note that this temporary code prints all variables (including X and y variables) and you may need to remove the dependent variable (y) from the code, which in this case is State_successful.