Predictive Analytics in Marketing and Retail Competency (Intermediate Level)

Topics covered

• calculate recency, frequency, and monetary (RFM) value of customers
• classify e-commerce data using logistic regression
• compare and contrast different regression models to predict Walmart sales
• create a pipeline for performing classification
• encode data for performing market basket analysis
• explore how to perform market basket analysis
• form shopping carts in grocery store order data
• import and process marketing campaign data
• perform agglomerative clustering on RFM data
• perform cross-validation and feature selection on a CLV prediction model
• perform DBSCAN clustering on RFM data
• perform feature selection using recursive feature elimination (RFE), lasso regression, and support vector regression (SVR) on a CLV prediction model
• perform market basket analysis
• predict CLV using linear regression
• predict Walmart sales using a regression model
• read in and explore grocery store data
• remove outliers from and visualize RFM data
• select features for predicting marketing responses
• split and encode e-commerce data for machine learning
• use feature selection with different types of models to predict responses to marketing
• use ridge regression, kNN, decision trees, extra tree regressors, and random forests to predict Walmart sales
• view the results for market basket analysis
• visualize campaign data using charts
• visualize CLV data