Loan Approval Prediction

📌 Project Overview

This project predicts the likelihood of loan approval based on applicants' financial and demographic data. The goal is to assist financial institutions in making informed loan approval decisions using machine learning techniques.

🚀 Features

• Data preprocessing and exploratory data analysis (EDA)
• Feature engineering and selection
• Machine learning model training and evaluation
• Model interpretability and visualization

🛠 Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Jupyter Notebook

📂 Dataset

The dataset includes:

• Applicant Income
• Credit History
• Loan Amount
• Property Area
• Employment Status
• Other Financial Indicators

💊 Machine Learning Models Used

• Logistic Regression
• Decision Trees
• Random Forest Classifier
• XGBoost

🔥 Results

The models are evaluated based on accuracy, precision, recall, and F1-score. The best model provides accurate predictions for loan approval likelihood.

📁 Repository Structure

📂 Loan-Approval-Prediction
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)

🖼 Code and Results

Include images of code and results in the images folder. Example:

🐟 How to Run the Project

1. Clone the repository:

   git clone https://github.com/rohitinu6/Loan-Approval-Prediction.git

2. Navigate to the project folder:

   cd Loan-Approval-Prediction

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook or Python scripts to train and test models.

📡 Links

• GitHub Repository: Loan Approval Prediction
• Portfolio: Rohit Dubey
• GitHub Profile: rohitinu6
• LinkedIn: Rohit Dubey
• Twitter/X: @rohitdubey003

💖 Tags

Machine Learning Loan Prediction Data Science Finance Python EDA

📝 License

This project is licensed under the MIT License.

---

💡 For any queries or collaboration opportunities, feel free to connect! 🚀