# Loan Eligibility Prediction Using Machine Learning
> Learn how to automate loan approvals with machine learning. Explore hardware/software requirements, data preprocessing, and model accuracy results.

Tags: machine-learning, loan-prediction, fintech, python, data-science, logistic-regression, random-forest
## Loan Eligibility Prediction
- **Objective:** Automating financial decisions using machine learning to reduce manual effort.
- **Overview:** Analyzes income, credit history, and employment status to predict loan outcomes.

## Hardware and Software requirements
- **Hardware:** Intel i5/Ryzen 5 CPU, 8GB-16GB RAM, 256GB SSD.
- **Software:** Python 3.8+, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
- **Tools:** Jupyter Notebook, VS Code, or PyCharm.

## Implementation & Methodology
- **Workflow:** Raw input -> Cleaning/Preprocessing -> Prediction Model -> status output.
- **Modules:** Data Collection, Preprocessing (handling missing values), Model Training (Logistic Regression), and Evaluation.
- **Dataset Attributes:** ApplicantIncome, Credit\_History, LoanAmount, and Property\_Area.

## Model Performance & results
- **Comparison:** Evaluated Logistic Regression, Decision Tree, Random Forest, and SVM.
- **Winner:** Random Forest achieved the highest accuracy at 85%.
- **Sample Output:** System provides binary classification (Approved/Rejected) based on specific input parameters like Income ($5000) and Credit (1).
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