Python: Working with Predictive Analytics

Find out how to use prebuilt Python libraries for predictive analytics and discover insights about the future.

Introduction

• Predict data in Python
• Road map

1. 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

2. 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