Python: Working with Predictive Analytics

Python: Working with Predictive Analytics
Python: Working with Predictive Analytics
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 22m | 233 MB

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.

Topics include:

  • Differentiating data types
  • Importing data
  • Converting data
  • Test vs. train data
  • Comparing prediction models
  • Linear regression
  • Decision tree regression
  • Hyperparameter optimization
Table of Contents

1 Predict data in Python
2 Road map
3 Differentiate data types
4 Python libraries and data import
5 Handling missing values
6 Convert categorical data into numbers
7 Divide the data into test and train
8 Feature scaling
9 Introduction to predictive models
10 Linear regression
11 Polynomial regression
12 Support Vector Regression (SVR)
13 Decision tree regression
14 Random forest regression
15 Evaluation of predictive models
16 Hyperparameter optimization
17 Challenge Hyperparameter optimization
18 Solution Hyperparameter optimization
19 Next steps