![](images/wine2.jpg)
Wine Retailer Email Marketing Evaluation
2020.2
- Goal: Evaluate whether an email promotion is effective & who to target with email campaign
- Data: Experiment data on email promotion, customers’ past purchase behavior data
- Methodology:
- Use linear regression to evaluate the average causal effects (ACE) of email treatment on purchase;
- Perform slice and dice analysis to evaluate the evaluate the conditional causal effects (CCE) of email treatment on purchase;
- Build a causal forest model to predict the individual-level customer purchase-lift caused by email campaign
Key Words: R, Regression, Causal Forests, Slice and Dice, Incrementality, Experiments, Email Marketing
![](images/heloc1.jpg)
HELOC Risk Prediction using Machine Learning
2019.12
- Goal: Develop a predictive model and a decision support system (DSS) that evaluates the risk of Home Equity Line of Credit (HELOC) applications
- Data: An anonymized dataset of HELOC applications made by real homeowners, provided by FICO
- Methodology:
- Use Maching Learning approch to build a predictive model to assess credit risk, models tested including Logistic Regression, Decision Tree, Random Forest, KNN, Linear Discriminant Analysis(LDA), BaggingClassifier, AdaBoostClassifier.
- Design and develop a running prototype of an interactive interface that sales representatives in a bank/credit card company can use to decide on accepting or rejecting applications.
Key Words: Python, Machine Learning, Logistic Regression, Decision Trees, Random Forest, KNN
![](images/toyhorse1.jpg)
Toy Horse Product Line Revitalization via Conjoint Analysis
2020.2
- Goal: Provide product portfolio revitalization recommendations to a toy horse company
- Data: Conjoint data of 12 product profiles responded by 200 individuals
- Methodology:
- Use Regression to estimate the conjoint model at the individual level ;
- Conduct Benefit Segmentation via Cluster Analysis of Conjoint Part-Utilities;
- Conduct a priori segmentation using the variables gender and age;
- Simulate market shares for different product-line scenarios.
Key Words: R, Conjoint Analysis, Cluster Analysis, Benefit Segmentation, Market Simulation
![](images/cc1.jpg)
Country Club Data Warehouse Design in MySQL
2020.4
- Goal: Design and create a data warehouse for Blue Hill Country Club’s database
- Data: Membership and transactional data related to one year of operations of Blue Hill Country Club
- Tool: MySQL
- Steps:
- Build two versions of data warehouses both containing relevant membership and revenue data, one in long shape and the other in wide shape
- Create ad-hoc analytics queries using the data warehouse built in the first step to answer relevant business questions the management team may be interested in.
Key Words: SQL, Data Warehouse, ETL, Database Management, Ad-hoc Querying