Loan Approval

Project Overview

This notebook presents a machine learning pipeline for predicting loan approval outcomes based on applicant data. It includes data preprocessing, exploratory analysis, model training, evaluation, and interpretation. The goal is to build a reliable classifier that can assist financial institutions in automating and improving their loan approval decisions.

Contents

• Data Loading & Cleaning: Handling missing values, encoding categorical features, and preparing the dataset. • Exploratory Data Analysis (EDA): Visualizing distributions, correlations, and key patterns in the data. • Feature Engineering: Creating and selecting relevant features to improve model performance. • Modeling: Training and evaluating classification models (e.g., Logistic Regression, Decision Trees, Random Forest). • Performance Metrics: Accuracy, precision, recall, F1-score, and confusion matrix. • Interpretability: Feature importance analysis and insights for decision-making.

? Models Used

• Logistic Regression • Decision Tree Classifier

Evaluation Strategy

Models are evaluated using:

• Train-test split or cross-validation • Confusion matrix and classification report