astrrokid/loanprediction-tf-keras

Loan Repayment Prediction using Neural Networks

This project aims to predict loan repayment using a neural network model built with TensorFlow and Keras. The dataset used contains various features related to loan applications, such as loan amount, interest rate, employment length, etc.

About LendingClub

LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.

Dataset

The dataset used in this project is a sample from a lending club loan dataset. It contains information about loan applicants and whether they repaid the loan or not.

Code Overview

• loan_prediction.ipynb: Jupyter Notebook containing the Python code for data preprocessing, model building, training, and evaluation.
• loan_prediction.h5: Saved model file after training.

Data Preprocessing

• Loaded the dataset and performed exploratory data analysis.
• Handled missing values, categorical variables, and feature engineering.
• Split the dataset into training and testing sets.
• Normalized the feature data using MinMaxScaler.

Model Building

• Built a sequential neural network model using TensorFlow and Keras.
• Added Dense layers with ReLU activation functions and Dropout layers.
• Compiled the model with binary crossentropy loss and Adam optimizer.

Model Training

• Fitted the model to the training data for 25 epochs with early stopping based on validation loss.
• Monitored model performance using validation data during training.

Model Evaluation

• Evaluated the model's performance on the test set using classification report and confusion matrix.
• Made predictions on a new customer's data and compared with the actual label.

Results

The trained neural network model achieved an accuracy of approximately 86% on the test set. It demonstrated the ability to predict loan repayment with reasonable accuracy.

Requirements

• Python 3.x
• TensorFlow
• Keras
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

Usage

1. Ensure all required libraries are installed.
2. Run the loan_prediction.ipynb Jupyter Notebook to preprocess data, build, train, and evaluate the model.
3. Save the trained model (loan_prediction.h5) for future use.

Author

Nsobundu Chukwudalu C

License

This project is licensed under the MIT License.