A Small End-to-End Project

Start

Books and courses are frustrating. They give you lots of recipes and snippets, but you never get to see how they all fit together.

When you are applying machine learning to your own datasets, you are working on a project.

A machine learning project may not be linear, but it has a number of well known steps:

1. Define Problem.
2. Prepare Data.
3. Evaluate Algorithms.
4. Improve Results.
5. Present Results.

The best way to really come to terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps. Namely, from loading data, summarizing data, evaluating algorithms and making some predictions.

If you can do that, you have a template that you can use on dataset after dataset. You can fill in the gaps such as further data preparation and improving result tasks later, once you have more confidence.

Machine Learning in Python: Step-By-Step Tutorial

Downloading, Installing and Starting Python SciPy

Install SciPy Libraries

There are 5 key libraries that you will need to install. Below is a list of the Python SciPy libraries required for this tutorial:

• scipy
• numpy
• matplotlib
• pandas
• sklearn

Start Python and Check Versions

# Check the versions of libraries
 
# Python version
import sys
print('Python: {}'.format(sys.version))
# scipy
import scipy
print('scipy: {}'.format(scipy.__version__))
# numpy
import numpy
print('numpy: {}'.format(numpy.__version__))
# matplotlib
import matplotlib
print('matplotlib: {}'.format(matplotlib.__version__))
# pandas
import pandas
print('pandas: {}'.format(pandas.__version__))
# scikit-learn
import sklearn
print('sklearn: {}'.format(sklearn.__version__))

Ouput:

Python: 2.7.13 |Anaconda 4.4.0 (x86_64)| (default, Dec 20 2016, 23:05:08)
[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]
scipy: 0.19.0
numpy: 1.12.1
matplotlib: 2.0.2
pandas: 0.20.1
sklearn: 0.18.1

Load the Data

Import libraries

Load Dataset

Summarize the Dataset

In this step we are going to take a look at the data a few different ways:

1. Dimensions of the dataset.
2. Peek at the data itself.
3. Statistical summary of all attributes.
4. Breakdown of the data by the class variable.

Dimensions of Dataset

Peek at the Data

Statistical Summary

Class Distribution

Data Visualization

We now have a basic idea about the data. We need to extend that with some visualizations.

We are going to look at two types of plots:

1. Univariate plots to better understand each attribute.
2. Multivariate plots to better understand the relationships between attributes.

Univariate Plots

Multivariate Plots

Evaluate Some Algorithms

Now it is time to create some models of the data and estimate their accuracy on unseen data.

Here is what we are going to cover in this step:

1. Separate out a validation dataset.
2. Set-up the test harness to use 10-fold cross validation.
3. Build 5 different models to predict species from flower measurements
4. Select the best model.

Create a Validation Dataset

Test Harness

Build Models

Let’s evaluate 6 different algorithms: (Classification)

1. Logistic Regression (LR)
2. Linear Discriminant Analysis (LDA)
3. K-Nearest Neighbors (KNN).
4. Classification and Regression Trees (CART).
5. Gaussian Naive Bayes (NB).
6. Support Vector Machines (SVM).

Select Best Model

Make Predictions

Reference

1. Your First Machine Learning Project in Python Step-By-Step