https://github.com/tolumie/loan-approval-prediction
Loan Approval Prediction using Machine Learning | EDA + Decision Tree, Random Forest & Logistic Regression | Automating loan eligibility for Dream Housing Finance by analyzing customer data and predicting loan approvals.
https://github.com/tolumie/loan-approval-prediction
classification credit-risk-analysis data-analysis decision-tree-classifier finance-analytics loan-approval logistic-regression-algorithm machine-learning predictive-modeling-techniques random-forest
Last synced: about 1 month ago
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Loan Approval Prediction using Machine Learning | EDA + Decision Tree, Random Forest & Logistic Regression | Automating loan eligibility for Dream Housing Finance by analyzing customer data and predicting loan approvals.
- Host: GitHub
- URL: https://github.com/tolumie/loan-approval-prediction
- Owner: Tolumie
- Created: 2025-03-07T17:32:39.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-07T18:21:08.000Z (about 2 months ago)
- Last Synced: 2025-03-07T18:33:52.162Z (about 2 months ago)
- Topics: classification, credit-risk-analysis, data-analysis, decision-tree-classifier, finance-analytics, loan-approval, logistic-regression-algorithm, machine-learning, predictive-modeling-techniques, random-forest
- Language: Jupyter Notebook
- Homepage:
- Size: 378 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# **Loan Approval Prediction: EDA + Decision Tree, Random Forest & Logistic Regression Modeling**
## **Project Overview**
This project focuses on **automating loan eligibility prediction** for **Dream Housing Finance**, a company that provides home loans across urban, semi-urban, and rural areas. The goal is to **identify customer segments eligible for loan approval** using machine learning models, enabling the company to target potential borrowers effectively.
## **Problem Statement**
The company wants to automate the loan approval process based on customer details submitted during the online application. These details include **income, loan amount, credit history, education, marital status, and more**. Given a dataset of past applicants, we analyze the data and build predictive models to classify whether a loan should be approved (`Loan_Status = Y`) or rejected (`Loan_Status = N`).
## **Dataset Description**
The dataset consists of customer details relevant to loan eligibility. The key features include:
| **Variable** | **Description** |
|----------------------|--------------------------------------------------|
| **Loan_ID** | Unique identifier for each loan application |
| **Gender** | Applicant's gender (Male/Female) |
| **Married** | Marital status (Yes/No) |
| **Dependents** | Number of dependents |
| **Education** | Education level (Graduate/Undergraduate) |
| **Self_Employed** | Whether the applicant is self-employed (Yes/No) |
| **ApplicantIncome** | Applicant's monthly income |
| **CoapplicantIncome** | Income of co-applicant |
| **LoanAmount** | Loan amount requested (in thousands) |
| **Loan_Amount_Term** | Loan term duration (months) |
| **Credit_History** | Whether credit history meets guidelines (1/0) |
| **Property_Area** | Area of the property (Urban/Semi-Urban/Rural) |
| **Loan_Status** | Loan approval status (Y/N - Target Variable) |
## **Project Workflow**
### **1. Exploratory Data Analysis (EDA)**
- Handling missing values and data inconsistencies
- Visualizing relationships between features and loan status
- Analyzing trends based on **income, credit history, and loan amount**
- Understanding the impact of categorical variables (e.g., Property Area, Education)
### **2. Feature Engineering & Data Preprocessing**
- Encoding categorical variables
- Handling skewness and outliers in numerical variables
- Scaling features for model compatibility
- Creating new features (if necessary) to improve predictions
### **3. Model Development & Evaluation**
Three machine learning models were implemented:
✅ **Logistic Regression** – Baseline model to understand linear relationships
✅ **Decision Tree** – Captures non-linear patterns in loan approval trends
✅ **Random Forest** – An ensemble model improving accuracy and reducing overfitting
### **4. Performance Metrics & Model Selection**
- **Accuracy Score**: Measures overall correctness of predictions
- **Precision & Recall**: Evaluates model effectiveness in detecting eligible borrowers
- **Confusion Matrix**: Analyzes classification errors
- **ROC-AUC Curve**: Assesses model performance in distinguishing approvals vs. rejections
## **Key Findings & Insights**
📌 **Credit History** is the strongest predictor of loan approval. Applicants with a **good credit history (1)** have a significantly higher approval rate.
📌 **Income** and **Loan Amount** show a relationship—higher income applicants tend to receive loan approvals more easily.
📌 **Marital Status & Dependents** have minor impacts, but self-employed applicants face slightly higher rejection rates.
📌 **Random Forest performed best**, delivering the highest accuracy and robust classification.
## **Next Steps**
🚀 **Hyperparameter tuning** to optimize Decision Tree & Random Forest models
🚀 **Feature Engineering** to incorporate additional financial indicators
🚀 **Deploying the model** via a web app for real-time loan approval predictions
## **Technologies Used**
🟢 **Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn)**
🟢 **Jupyter Notebook** for data analysis and model development
🟢 **Machine Learning (Logistic Regression, Decision Tree, Random Forest)**
## **Conclusion**
This project successfully **demonstrates the power of machine learning in loan approval prediction**. By leveraging **EDA, feature engineering, and predictive modeling**, we provide insights into customer eligibility, helping Dream Housing Finance **streamline loan processing and target high-potential borrowers efficiently**.