This project aims to assist a Portuguese banking institution in effectively marketing its term deposit product by predicting which customers are more likely to subscribe. Leveraging machine learning models enables the bank to concentrate its marketing efforts on customers with a higher propensity to subscribe, thus optimizing resource allocation and time.
The primary objective is to enhance the efficiency of marketing campaigns by identifying potential subscribers to the bank's term deposit product. Implementing a targeted approach will reduce marketing costs and increase the conversion rate, creating a more sustainable and profitable marketing strategy.
The dataset originates from direct marketing campaigns (phone calls) of a Portuguese banking institution. The goal is to predict whether a client will subscribe to a term deposit, based on historical interaction data. The key challenge involves dealing with imbalanced data, necessitating specific techniques to ensure model accuracy and reliability.
EDA was conducted to understand the dataset's characteristics, including distribution of variables, presence of outliers, and relationships between features. The analysis focused on identifying patterns that could influence the subscription outcome.
Data cleaning involved handling missing values and removing outliers using the Z-score method. This step was crucial to prepare the dataset for modeling, ensuring that the input data would not skew the model's performance.
Several machine learning models were evaluated, including Logistic Regression, Ensemble methods, and Boosting algorithms. A particular focus was given to Gradient Boosting techniques, known for their effectiveness in handling imbalanced datasets and complex relationships within the data.
The models were assessed based on their ability to predict subscription outcomes accurately. Performance metrics were carefully selected to evaluate each model's effectiveness, especially considering the imbalanced nature of the dataset.
The final model was deployed to serve as a predictive tool for the bank's marketing team. This tool enables the identification of potential subscribers, allowing for more focused and cost-effective marketing campaigns.
The performance of the machine learning models was translated into business metrics, demonstrating the potential reduction in marketing costs and increase in subscription rates. This section aims to bridge the gap between technical outcomes and business benefits.
A presentation was prepared to explain the project's objectives, methodology, outcomes, and business impact to non-technical stakeholders, ensuring that the benefits of the machine learning model are clearly understood across the organization.
The project highlights the potential of machine learning in optimizing marketing strategies for financial products. Future directions could involve refining the model with additional data, exploring alternative algorithms, and expanding the model's application to other marketing channels.