Python: Working with Predictive Analytics (2019)

1h 22mAdvanced2019-08-12

Authors

Isil Berkun

Data Scientist at Intel Corp.

Course details

Data can tell many stories: where it came from and where it’s going. Predictive analytics gives programmers a tool to tell stories about the future: to extract usable information and make accurate predictions. These predictions, in turn, allow business to make more informed, impactful decisions. Join Isil Berkun, data scientist, to explore predictive analytics with Python. Discover how to prepare data—fill in missing values, perform feature scaling, and more—and use prebuilt Python libraries to make and evaluate prediction models. She describes what models to use when, and explains the concepts in such a way that you can immediately apply them to your own work. By the end of the course, you’ll be able to leverage Python libraries like pandas and NumPy and choose the right prediction models for your projects.

Learning objectives
Explain how predictive analytics can assist with decision-making.
Differentiate between the types of data that are used.
Apply the correct functions to Python code to produce optimal results.
Explain why data needs to be preprocessed before using predictive models.
Distinguish between the different predictive models available.

Skills covered

Data ModelingPythonProgramming LanguagesData ScienceOpen SourceSoftware DevelopmentDeep Dive (X:Y)

Concepts

Introduction

Predict data in Python
Road map

Data Preprocessing

Differentiate data types
Python libraries and data import
Handling missing values
Convert categorical data into numbers
Divide the data into test and train
Feature scaling

Prediction Models

Introduction to predictive models
Linear regression
Polynomial regression
Support Vector Regression (SVR)
Decision tree regression
Random forest regression
Evaluation of predictive models
Hyperparameter optimization
Challenge - Hyperparameter optimization
Solution - Hyperparameter optimization

Conclusion

Next steps