Loan Prediction Project - README

Project Overview

This project is a machine learning model to predict loan approval status based on applicant details. The model is built using Python and Jupyter Notebook.

Files Included

• LOAN PREDICTION.ipynb: Jupyter Notebook containing data analysis, model building, and evaluation.
• requirements.txt: List of required Python packages (e.g., pandas, numpy, scikit-learn, matplotlib, seaborn).
• README.md: Project documentation (this file).

Prerequisites

• Python 3.9 or above
• Jupyter Notebook
• Install required packages using:

  pip install -r requirements.txt

Dataset

• The dataset contains information about loan applicants including gender, marital status, income, loan amount, credit history, and loan status.

Steps Implemented in the Notebook

1. Data Loading: Load dataset using Pandas.
2. Exploratory Data Analysis (EDA): Analyze data distribution and identify patterns using Matplotlib and Seaborn.
3. Data Preprocessing: Handle missing values, encode categorical variables, and scale numerical features.
4. Model Building: Train a machine learning model (e.g., Logistic Regression) using scikit-learn.
5. Model Evaluation: Evaluate model performance using metrics such as accuracy, precision, recall, and confusion matrix.
6. Results and Insights: Present key findings and insights from the model's performance.

How to Run the Project

1. Clone the repository or download the project files.
2. Install dependencies with pip install -r requirements.txt.
3. Open LOAN PREDICTION.ipynb in Jupyter Notebook.
4. Run all cells to execute the project workflow.

Results

• The model achieved an accuracy score of accuracy_score.
• Important features influencing loan approval were NUMBER_OF_INSTALLMENTS, SANCTION_AMT, OVER_DUE_AMT, INSTALMENT_LOAN_TYPE_ConsumerLoan,INSTALMENT_LOAN_TYPE_OtherInstalmentOperation, loan_status_Existing

Future Improvements

• Experiment with different models (e.g., Random Forest, XGBoost).
• Perform hyperparameter tuning.
• Enhance data preprocessing techniques.

Author

• Developed by: Shalini