🏦 Loan Approval Classification Project

A machine learning system to predict bank loan approvals using customer data analysis.

📌 Overview

This project aims to build a Classification Model capable of predicting whether a loan application should be approved or rejected (Loan Status). The model relies on a set of financial and personal factors of the applicant, helping to reduce financial risks and accelerate the decision-making process.

📂 Dataset

The dataset used contains records of customers along with details of their loan applications. Key columns include:

• person_age: Applicant's age.
• person_income: Annual income.
• person_home_ownership: Housing status (Rent, Own, Mortgage...).
• person_emp_exp: Years of employment experience.
• loan_intent: Loan purpose (Education, Medical, Personal...).
• loan_amnt: Loan amount requested.
• loan_int_rate: Interest rate.
• loan_percent_income: Loan-to-income ratio.
• cb_person_cred_hist_length: Length of credit history.
• credit_score: Credit score.
• previous_loan_defaults_on_file: Any previous loan defaults?
• loan_status: (Target) 1 for Approved, 0 for Rejected.

🛠️ Technologies & Libraries

The project was implemented using Python and the following libraries:

• Pandas & NumPy: For data manipulation and analysis.
• Plotly (Express & Graph Objects): For creating interactive charts and Exploratory Data Analysis (EDA).
• Scikit-Learn:
  • Preprocessing: PowerTransformer, StandardScaler, OneHotEncoder.
  • Models: SVC (SVM), KNeighborsClassifier (KNN), GaussianNB (Naive Bayes).
  • Metrics: accuracy_score, confusion_matrix.

⚙️ Methodology

1. Exploratory Data Analysis (EDA):
   • Understanding data distribution and studying relationships between variables (Correlation Matrix).
2. Data Preprocessing:
   • Handling missing values and encoding categorical data.
   • Using Power Transformation (Yeo-Johnson) to improve data distribution and reduce skewness, ensuring better model performance.
3. Modeling:
   • Splitting data into Training and Testing sets to prevent Data Leakage.
   • Training three different models: SVM, KNN, and Naive Bayes.

📊 Model Results

Three different models were trained and tested to evaluate their performance. The table below shows the final results:

Model	Training Accuracy	Testing Accuracy
Support Vector Machine (SVM)	92.30%	92.08%
K-Nearest Neighbors (KNN) n=5	93.25%	90.18%
Gaussian Naive Bayes	81.05%	80.85%

🏆 Selected Model

Based on the results, the SVM model was selected as the final model for this project.

• Reason: It achieved the highest Testing Accuracy (92.08%).
• It demonstrated excellent Generalization, with a very small gap between Training and Testing accuracy, indicating no overfitting issues.

🚀 How to Run

1. Install the required libraries:

   pip install pandas numpy plotly scikit-learn

2. Open the Loan_Approval_Classification.ipynb file using Jupyter Notebook or Google Colab.
3. Ensure the data file is in the same directory and run the cells sequentially.

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✍️ Author

Developed by : Samir Mohamed