TensorFlow, Keras, Python, Deep Learning
Overview
This project develops a Loan Grade Prediction system using TensorFlow, aimed at predicting the grade of loans based on various features. Through comprehensive data preprocessing, feature selection, and the application of machine learning algorithms, the system provides insights into loan grading which can be crucial for financial institutions in assessing credit risk.
Features
- Data Preprocessing: Includes handling missing values and preparing the dataset for model training.
- Feature Selection: Identifies the most relevant features that impact loan grading.
- Model Training: Utilizes TensorFlow to create and train models for predicting loan grades.
Getting Started
Follow these instructions to set up the project and run the notebook in your local environment.
Prerequisites
- Python 3.x
- Jupyter Notebook
- TensorFlow
Installation
-
Install the required libraries using pip. Ensure you have Python installed on your system.
pip install tensorflow numpy pandas matplotlib sklearn
- Clone the repository or download the Jupyter notebook file to your local machine.
Running the Notebook
- Navigate to the notebook directory.
-
Open Jupyter Notebook or JupyterLab.
jupyter notebook
- Run each cell in the notebook sequentially to process the data, train the model, and evaluate its performance
Libraries Used
- TensorFlow (for building and training neural network models)
- NumPy and pandas (for data manipulation)
- Matplotlib (for data visualization)
- scikit-learn (for preprocessing and model evaluation)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
import tensorflow as tf