💳 Credit Scoring Model — CodeAlpha Internship Task

This project is developed as part of the CodeAlpha Machine Learning Internship.
The objective is to build a Credit Scoring Model that predicts an individual's creditworthiness based on historical financial data.

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🚀 Project Overview

Credit scoring plays a crucial role in determining whether a customer is eligible for a loan or credit.
This project leverages Machine Learning to classify customers as "Good Credit" or "Bad Credit",
helping financial institutions make informed decisions.

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📂 Files in the Repository

• codaalpha_credit_scoring_model.ipynb → Jupyter Notebook containing the full code (data preprocessing, modeling, evaluation).
• loan_data.csv → Dataset used for training and testing the model.
• README.md → Documentation for the project.

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🛠️ Tech Stack

• Programming Language: Python 🐍
• Libraries & Tools:
  • pandas, numpy → Data handling
  • matplotlib, seaborn → Visualization
  • scikit-learn → Preprocessing, Modeling, Evaluation

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🔍 Workflow

1. Data Preprocessing

   • Handle missing values
   • Encode categorical features
   • Feature scaling (Normalization/Standardization)

2. Outlier Detection & Handling

   • IQR & Z-Score methods

3. Feature Engineering

   • Feature Selection (Chi-Square test)
   • Dimensionality Reduction (PCA)

4. Modeling

   • Logistic Regression
   • Decision Tree
   • Random Forest

5. Evaluation Metrics

   • Accuracy
   • Precision, Recall, F1-Score
   • ROC-AUC Curve

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📊 Results

• The best-performing model was Random Forest Classifier,
  achieving high accuracy and balanced performance across precision & recall.

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📌 How to Run

1. Clone this repository:

   git clone https://github.com/YourUsername/CodeAlpha_CreditScoring.git
   cd CodeAlpha_CreditScoring