Predictive Modeling in Marketing Analytics

Predictive Modeling in Marketing Analytics

Example Dataset:

Objective:

Students will work on a project that involves building a predictive model for a marketing analytics problem, such as predicting customer churn or identifying high-value customers. The goal is to apply regularization methods to improve the model’s performance and interpretability.

Instructions:

1. Data Collection: Obtain a dataset that includes customer demographics, purchase history, and other relevant features.
2. Preprocessing: Preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features.
3. Model Selection: Choose a predictive modeling technique, such as linear regression or logistic regression, and apply regularization methods such as Lasso or Ridge regression.
4. Training: Train the model on the preprocessed dataset, using cross-validation to tune the regularization parameters.
5. Evaluation: Evaluate the model’s performance using appropriate metrics, such as accuracy, precision, recall, or mean squared error.
6. Interpretation: Interpret the model’s coefficients, discussing the impact of regularization on feature selection and model interpretability.
7. Reporting: Document the entire process, including the methodology, results, and insights gained from the project, adhering to the Regularization Methods Project Rubric.

Submission Requirements:

Submit a written report that documents your entire project. The report should be structured and include the following sections:

• File type: PDF or Word (.docx)
• Introduction (brief overview of the problem and objective)
• Data Collection (description and source of the dataset)
• Data Preprocessing (explanation of how missing data, categorical variables, and scaling were handled)
• Model Selection & Regularization (description of the chosen model(s) and regularization techniques used)
• Training & Hyperparameter Tuning (cross-validation strategy and tuning process)
• Evaluation (metrics used and interpretation of model performance)
• Interpretation (analysis of feature importance and the impact of regularization on interpretability)
• Conclusion (summary of findings and potential next steps)
• References (cite any tools, libraries, or academic sources used)

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