This project aims to predict the eligibility of individuals for loans based on various factors using machine learning techniques. Predicting loan eligibility is a common problem faced by banks and financial institutions, and this project provides a solution using Python programming language and popular machine learning algorithms.
- Visualising different features using countplot -
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Checking loan amount and total income through graph -
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Visualising correlation between diffrent features -
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Confusion Matrix
The dataset used for this project contains information about individuals including their gender, marital status, education, employment, income, loan amount, loan term, and credit history. The target variable is whether the individual's loan application was approved or not.
Download dataset from here - loan_data
- Data Preprocessing: The dataset is preprocessed to handle missing values, encode categorical variables, and scale numerical features.
- Model Building: Several machine learning models such as Logistic Regression, Random Forest, and Decision Tree Classifier are trained on the preprocessed data to predict loan eligibility.
- Model Evaluation: The models are evaluated using various performance metrics such as accuracy, precision, recall, and score to assess their effectiveness.
- Python 3.11s
- Pandas
- NumPy
- Scikit-learn