payal-cst / loan-approval-prediction

LOAN-APPROVAL-PREDICTION

AIM :- The goal of this project is to develop a robust machine learning model that predicts whether an applicant is eligible for a loan based on various features and historical data. By automating this process, we aim to improve the efficiency of loan approval decisions while reducing the risk of human bias.

PROBLEM STATEMENT :- The primary challenge in this project is to create a predictive model that accurately classifies loan applicants into two categories: those who are eligible for a loan and those who are not. This binary classification problem is essential for financial institutions to make informed lending decisions.

DATASET :- We leverage a comprehensive dataset containing historical loan application information, including applicant demographics, financial details, credit history, and loan outcomes. This dataset serves as the foundation for training and testing our machine learning model.

APPROACH :- Our approach involves the following steps:

Data Preprocessing: We perform extensive data cleaning, handle missing values, and encode categorical variables to prepare the dataset for modeling.

Feature Engineering: We extract relevant features from the dataset and engineer new features when necessary to improve model performance.

Model Selection: We experiment with various machine learning algorithms such as nav-base, decision trees, random forests, and K-neighbor.

Model Training: We split the dataset into training and testing sets and train the selected machine learning models on the training data.

Model Evaluation: We assess the models' performance using appropriate evaluation metrics such as accuracy and predictions

RESULTS :- Our machine learning model provides accurate loan approval predictions, helping financial institutions streamline the loan approval process, reduce manual intervention, and minimize the risk of biased decision-making.