This is the repository for Data Science for Marketing Analytics, 2nd Edition, published by Packt. It contains all the supporting project files necessary to work through the course from start to finish.
To get started with the project files, you'll need to:
- Install Anaconda
- Create a virtual environment using the
environment.ymlfile provided here. You can also use Binder for running the Jupyter Notebook files online.
Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of it based on the segments.
- Data pre-processing, exploration, and visualization techniques using pandas, Matplotlib, and Seaborn modules
- Clustering techniques, such as hierarchical and k-means clustering, and their use in customer segmentation
- Regression models for predictive modeling using scikit-learn
- Regularization techniques for reducing overfitting in regression models
- Hyperparameter tuning for optimizing model performance
- Application of multiclass classification models in customer choice modeling
If you've found this book useful, you might want to check out some of our other titles: