Bank Loan Approval Prediction With Artificial Neural Nets

Sponsored by Foundation For Excellence through Coursera Project Works

Welcome to the "Bank Loan Approval Prediction With Artificial Neural Nets" project! This hands-on project is designed to provide you with practical experience in building and training a deep neural network model to predict the approval of personal loans based on various features.

What You'll Learn

1. Understand the theory and intuition behind Deep Neural Networks.
2. Build and train a deep learning model using Keras with Tensorflow 2.0 as a backend.
3. Assess the performance of the trained model and ensure its generalization using various Key Performance Indicators.

Skills You'll Practice

• Artificial Neural Networks
• Deep Learning
• Machine Learning

About this Guided Project

In this project sponsored by Foundation For Excellence through Coursera Project Works, you will:

• Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry.
• Import key Python libraries, datasets, and perform Exploratory Data Analysis.
• Perform data visualization using Seaborn.
• Standardize the data and split them into train and test datasets.
• Build a deep learning model using Keras with Tensorflow 2.0 as a backend.
• Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs).

Learning Objectives

By the end of this project, you will be able to:

• Understand the theory and intuition behind Deep Neural Networks.
• Import key Python libraries, dataset, and perform Exploratory Data Analysis.
• Perform data visualization using Seaborn.
• Standardize the data and split them into train and test datasets.
• Build a deep learning model using Keras with Tensorflow 2.0 as a back-end.
• Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs).

Learn Step-by-Step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

1. Task 1: Understand the problem statement and business case
2. Task 2: Import Datasets and Libraries
3. Task 3: Exploratory Data Analysis
4. Task 4: Perform Data Visualization
5. Task 5: Prepare the data to feed the model
6. Task 6: Understand the theory and intuition behind Artificial Neural Networks
7. Task 7: Build a simple Multi-Layer Neural Network
8. Task 8: Compile and train a Deep Learning Model
9. Task 9: Assess the performance of the trained model

Project Images

Acknowledgements

I would like to express our gratitude to Coursera for providing the platform and resources for this project.

Contact

For inquiries or collaborations, connect with me through 📬:

• Email: [email protected]
• LinkedIn: vijaisuria
• Twitter: vijaisuria
• GitHub: Vijai Suria