🧠 Bank Marketing Campaign Classification using AI

📌 Project Overview

This project analyzes the effectiveness of a direct marketing campaign from a Portuguese bank, with the goal of predicting whether a client will subscribe to a term deposit. We also explore how IBM Granite can support data analysis and business insight generation.

📂 Dataset

• Source: Bank Marketing Dataset on Kaggle
• Local file used: dataset/bank-direct-marketing-campaigns.csv

⚙️ Methods

• Data preprocessing & feature engineering
• Classification using Random Forest
• AI-supported insight generation using IBM Granite

💡 Insight & Findings

• Most influential features include: duration, contact, pdays, month, and previous.
• The model achieved ~X% accuracy in predicting client subscription.
• LLM was able to summarize performance and suggest marketing strategies based on model results.

🤖 AI Support

We used IBM Granite to:

• Analyze model results
• Generate natural language insight
• Suggest marketing recommendations automatically

🔗 Output

• Notebook (Google Colab)
• Slides (Presentation)