Credit Risk Analysis Project 📊💳

Welcome to the Credit Risk Analysis project! This repository contains the code and documentation for predicting credit risk using machine learning techniques.

Project Overview 📝

Credit risk analysis involves evaluating the likelihood that a borrower will default on their debt obligations. This project includes data collection, preprocessing, model training, evaluation, and deployment.

Table of Contents 📚

• Objective
• Data Collection and Preparation
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Selection
• Model Training and Validation
• Model Evaluation
• Model Interpretation
• Implementation
• Documentation and Reporting
• Setup

Objective 🎯

The objective of this project is to predict the likelihood of loan applicants defaulting on their loans, thereby aiding financial institutions in making informed lending decisions.

Data Collection and Preparation 📂

• Data Sources: Financial institution databases, credit bureaus, public financial statements.
• Data Types: Borrower information (demographics, employment), credit history, loan characteristics, financial ratios.
• Data Cleaning: Handle missing values, outliers, and inconsistent data.

Exploratory Data Analysis (EDA) 🔍

• Descriptive Statistics: Summarize data to understand distribution, mean, median, etc.
• Visualization: Use charts (e.g., histograms, box plots) to identify patterns and correlations.
• Correlation Analysis: Identify relationships between variables.

Feature Engineering ⚙️

• Transform Variables: Create new features that may better capture the risk (e.g., debt-to-income ratio).
• Encoding: Convert categorical variables into numerical format (e.g., one-hot encoding).

Model Selection 🤖

• Supervised Learning: Use classification algorithms (e.g., Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks).
• Unsupervised Learning: Techniques like clustering if you want to segment borrowers.

Model Training and Validation 🧠

• Split Data: Divide data into training and testing sets.
• Train Model: Fit the model on the training data.
• Validate Model: Use cross-validation to tune hyperparameters and avoid overfitting.

Model Evaluation 📈

• Metrics: Use evaluation metrics such as accuracy, precision, recall, F1-score, ROC-AUC to assess model performance.
• Confusion Matrix: Helps in understanding the performance in terms of true/false positives and negatives.

Model Interpretation 🔑

• Feature Importance: Determine which features have the most influence on predictions.
• SHAP Values: Explain individual predictions for complex models.

Implementation 🚀

• Integration: Implement the model in the financial institution’s decision-making process.
• Monitoring: Regularly monitor the model’s performance and retrain it with new data to maintain accuracy.

Documentation and Reporting 📝

• Document the process, findings, and model performance.
• Present insights to stakeholders in an understandable format.

Setup 💻

1. Clone the repository:

   git clone https://github.com/yourusername/credit-risk-analysis.git
   cd credit-risk-analysis

2. Install the required libraries:

   pip install -r requirements.txt

3. Run the Jupyter Notebook:

   jupyter notebook

Example Libraries and Tools 🛠️

• Python Libraries:
  • Data Handling: pandas, numpy
  • Visualization: matplotlib, seaborn
  • Machine Learning: scikit-learn, xgboost, lightgbm
  • Model Interpretation: shap, `lime'

Website Summarization for this project 🛜

• Link : https://swarupsahu08.github.io/Credit-Risk-Analysis/