Bank Loan Approval Prediction With Artificial Neural Nets

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In this Guided Project, you will:

Understand the theory and intuition behind Deep Neural Networks

Build and train a deep learning model using Keras with Tensorflow 2.0 as a backend.

Assess the performance of trained model and ensure its generalization using various Key performance indicators.

2 hours
Beginner
No download needed
Split-screen video
English
Desktop only

In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc. By the end of this project, you will be able to: - Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry - 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). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

  • Deep Learning

  • Artificial Intelligence (AI)

  • Machine Learning

  • Python Programming

  • classification

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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

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