💳🏦 Credit Card Approval Predictor Project 🏦💳

Project Overview: In this project, I will automate the credit card approval process using machine learning. The goal is to predict whether an applicant should be granted a credit card based on various features.

Dataset Description: The dataset contains information on applicants, including their credit history, debt-to-income ratio, delinquent credit lines, major derogatory reports, property value, and age of oldest tradeline.

Features:

1. BAD: Binary (1 = Client defaulted on previous loan; 0 = loan repaid) 🚫✔️
2. DEBTINC: Debt-to-income ratio 💵📊
3. DELINQ: Number of delinquent credit lines 📊
4. DEROG: Number of major derogatory reports 📊
5. VALUE: Value of Current Property 🏠💰
6. CLAGE: Age of oldest tradeline in months ⌛📆

Approach: I'll employ a classification model to predict credit card approval. The process will involve data preprocessing, model selection, training, and evaluation.

Steps:

1. Data Preprocessing:

   • Handle missing values ✨
   • Remove duplicates 🗑️
   • Taking care of outliers 📊🧐
   • Normalize/standardize features 📏

2. Model Selection:

   • Decision Tree 🔄
   • Evaluate the model's performance 📈

3. Model Training:

   • Split data into training and testing sets 🧩
   • Train selected model on training data 🚀

4. Model Evaluation:

   • Evaluate the model's performance m accuracy metric 📊
   • Analyze confusion matrix 📉

5. Hyperparameter Tuning (if needed):

   • Optimize model parameters for better performance ⚙️