Python: Working with Predictive Analytics
2h 13mIntermediate2025-03-10
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 data scientist Isil Berkun in this course 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. Isil describes what models to use and when, and explains the concepts in such a way that you can immediately apply them to your own work. Check out this course and learn 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.
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 DevelopmentOne-Off
Concepts
0. Introduction
- 01 - Predict data in Python
- 02 - What you should know and course road map
- 03 - How to use Codespaces and the exercise files
- 04 - CoderPad tour
1. Data Preprocessing
- 05 - Differentiate data types
- 06 - Python libraries and data import
- 07 - Handling missing values
- 08 - Solution - Handling missing values
- 09 - Convert categorical data into numbers
- 10 - Divide the data into test and train
- 11 - Feature scaling
- 12 - Solution - Feature scaling
2. Predictive Models
- 13 - Introduction to predictive models
- 14 - Linear regression
- 15 - Polynomial regression
- 16 - Solution - Polynomial regression
- 17 - Support Vector Regression (SVR)
- 18 - Decision tree regression
- 19 - Random forest regression
- 20 - Solution - Random forest regression
- 21 - Evaluation of predictive models
- 22 - Hyperparameter optimization
- 23 - Solution - Hyperparameter optimization
Conclusion
- 24 - Next steps in Python and predictive analytics
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