Loan Approval Prediction

The project journey included:

Data preprocessing – handling missing values, encoding categorical variables, and normalizing data.

Exploratory data analysis – identifying key patterns and correlations that influence loan approval.

Feature engineering & selection – improving model performance by choosing the most relevant predictors.

Model building & evaluation – training and comparing multiple algorithms (e.g., Logistic Regression, Decision Trees, Random Forests) to achieve high accuracy and reliability.