Loan approval Prediction

Developed a Machine Learning project to predict whether a loan application should be approved or rejected based on applicant and financial information.

The project included a complete Machine Learning workflow, starting from data preprocessing and exploratory data analysis to model training, evaluation, and comparison.

Key features included:

* Data cleaning and preprocessing

* Handling missing values and categorical features

* Exploratory Data Analysis (EDA)

* Feature scaling and selection

* Handling class imbalance using SMOTE

* Training multiple Machine Learning models

* Comparing model performance using Accuracy, Recall, F1-Score, Confusion Matrix, and Log Loss

* Analyzing feature importance to understand the factors affecting loan approval

* Evaluating and selecting suitable models based on their performance

The project provided practical experience in Python, data analysis, classification algorithms, data preprocessing, model evaluation, and applying Machine Learning to a real-world financial prediction problem.