Credit Card Approval

📝 Task Requirements

To complete this task, each student is expected to go through the full machine learning workflow using the “Credit Card Approval” dataset from Kaggle. The following components must be included in your submission

Problem Understanding
Clearly define the objective: predicting whether a credit card application will be approved based on applicant data.

Data Preprocessing
Handle missing values, encode categorical features, and scale numerical ones appropriately.

Exploratory Data Analysis (EDA)
Perform EDA to explore patterns, distributions, correlations, and potential issues in the data.

Feature Engineering
Select, transform, or create features that help improve model performance.

Modeling
Apply the full ML Lifecycle and choose the most suitable ML or DL algorithm (e.g., Logistic Regression, Random Forest, XGBoost, or a Neural Network).
Justify your model choice based on performance and interpretability.

Evaluation
Evaluate the model using metrics like Accuracy, Precision, Recall, F1 Score, and ROC-AUC. Include relevant charts (e.g., confusion matrix, ROC curve).

Documentation
Submit a short report (PDF or Word) explaining your methodology, results, and insights.

Presentation
Each student must prepare a clear and professional PowerPoint presentation (PPT) to showcase their entire process and findings.