This is a Jupyter project demonstrate on classification model training. You may visit the presentation slide below.

Presentation Slide Link: https://docs.google.com/presentation/d/1hGG3yMfugKGOfFKQ-XcSUYOL7bIE1J2XIRnxq0ROVEQ/edit#slide=id.g274072545a7_0_1

The data model study below is based on Kaggle open dataset: https://www.kaggle.com/datasets/thedevastator/bank-term-deposit-predictions

This dataset, titled Direct Marketing Campaigns for Bank Term Deposits, is a collection of data related to the direct marketing campaigns conducted by a Portuguese banking institution. These campaigns primarily involved phone calls with customers, and the objective was to determine whether or not a customer would subscribe to a term deposit offered by the bank. Most of the outcomes of the dataset is 'unsuccessful' (i.e. unbalanced data), therefore the study will prioritize on balanced accuracy (i.e. simple average accuracy of successful and unsuccessful outcome, instead of weight average accuracy of the two outcomes) and recall.

The goal is to predict the outcome of the marketing campaign by training model with provided data, and study what are the key factors affecting the result.

Data Cleaning & Exploration

Data Transformation

Model Selection

Base Model Training

Feature Engineering and Model Finetune

Conclusion