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1: Getting Started with Predictive Modelling
Introducing predictive modelling
Applications and examples of predictive modelling
Python and its packages download and installation
Python and its packages for predictive modelling
IDEs for Python
Summary
2: Data Cleaning
Reading the data variations and examples
Various methods of importing data in Python
The read_csv method
Use cases of the read_csv method
Case 2 reading a dataset using the open method of Python
Case 3 reading data from a URL
Case 4 miscellaneous cases
Basics summary, dimensions, and structure
Handling missing values
Creating dummy variables
Visualizing a dataset by basic plotting
Summary
3: Data Wrangling
Subsetting a dataset
Generating random numbers and their usage
Grouping the data aggregation, filtering, and transformation
Random sampling splitting a dataset in training and testing datasets
Concatenating and appending data
Merging/joining datasets
Summary
4: Statistical Concepts for Predictive Modelling
Random sampling and the central limit theorem
Hypothesis testing
Chi-square tests
Correlation
Summary
5: Linear Regression with Python
Understanding the maths behind linear regression
Making sense of result parameters
Implementing linear regression with Python
Model validation
Handling other issues in linear regression
Summary
6: Logistic Regression with Python
Linear regression versus logistic regression
Understanding the math behind logistic regression
Implementing logistic regression with Python
Model validation and evaluation
Model validation
Summary
7: Clustering with Python
Introduction to clustering what, why, and how?
Mathematics behind clustering
Implementing clustering using Python
Fine-tuning the clustering
Summary
8: Trees and Random Forests with Python
Introducing decision trees
Understanding the mathematics behind decision trees
Implementing a decision tree with scikit-learn
Understanding and implementing regression trees
Understanding and implementing random forests
Summary
9: Best Practices for Predictive Modelling
Best practices for coding
Best practices for data handling
Best practices for algorithms
Best practices for statistics
Best practices for business contexts
Summary
Appendix A: A List of Links
Appendix B: Index