Project - Predictive Modeling Project Template

Applied machine learning is an empirical skill. You cannot get better at it by reading books and articles. You have to practice. In this lesson you will discover the simple six-step machine learning project template that you can use to jump-start your project in Python. After completing this lesson you will know:

1. How to structure an end-to-end predictive modeling project.

2. How to best use the structured project template to ensure an accurate result for your dataset.

A. Practice Machine Learning With Projects

Working through machine learning problems from end-to-end is critically important. You can read about machine learning. You can also try out small one-off recipes. But applied machine learning will not come alive for you until you work through a dataset from beginning to end.

Working through a project forces you to think about how the model will be used, to challenge your assumptions and to get good at all parts of a project, not just your favorite parts.

The best way to practice predictive modeling machine learning projects is to use standardized datasets from the UCI Machine Learning Repository. Once you have a practice dataset and a bunch of Python recipes, how do you put it all together and work through the problem end-to-end?

1. Use A Structured Step-By-Step Process

Any predictive modeling machine learning project can be broken down into six common tasks:

• Define Problem.
• Summarize Data.
• Prepare Data.
• Evaluate Algorithms.
• Improve Results.
• Present Results.

B. Machine Learning Project Template in Python 

This section presents a project template that you can use to work through machine learning problems in Python end-to-end.

1. Template Summary

Below is the project template that you can use in your machine learning projects in Python.

# Python Project Template
# 1. Prepare Problem
# a) Load libraries
# b) Load dataset

 

# 2. Summarize Data
# a) Descriptive statistics
# b) Data visualizations

 

# 3. Prepare Data
# a) Data Cleaning
# b) Feature Selection
# c) Data Transforms

# 4. Evaluate Algorithms
# a) Split-out validation dataset
# b) Test options and evaluation metric
# c) Spot Check Algorithms
# d) Compare Algorithms

 

# 5. Improve Accuracy
# a) Algorithm Tuning
# b) Ensembles

 

# 6. Finalize Model
# a) Predictions on validation dataset
# b) Create standalone model on entire training dataset
# c) Save model for later use