Book Image

Data Science Projects with Python

By : Stephen Klosterman
Book Image

Data Science Projects with Python

By: Stephen Klosterman

Overview of this book

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.
Table of Contents (9 chapters)
Data Science Projects with Python
Preface

Final Thoughts on Delivering the Predictive Model to the Client


We have now completed modeling activities and also created a financial analysis to indicate to the client how they can use the model. While we have created the essential intellectual contributions that are the data scientists' responsibility, it is necessary to agree with the client on the form in which all these contributions will be delivered.

A key contribution is the predictive capability embodied in the trained model. Assuming the client has the capability to work with the trained model object we created in scikit-learn, this model could be saved to disk and sent to the client. Then, the client would be in a position to use it within their workflow. Alternatively, it may be necessary to express the model as a mathematical equation (i.e. logistic regression) or a set of if-then statements (i.e. decision tree or random forest) that the client could use to implement the predictive capability in SQL. While expressing random...