Andrew Russell – CFI Education – Loan Default Prediction with Machine Learning

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Andrew Russell – CFI Education – Loan Default Prediction with Machine Learning download, Andrew Russell – CFI Education – Loan Default Prediction with Machine Learning review, Andrew Russell – CFI Education – Loan Default Prediction with Machine Learning free

Andrew Russell – CFI Education – Loan Default Prediction with Machine Learning

Loan Default Prediction with Machine Learning
Combine a data set with basic Machine Learning skills to predict which customers are likely to default on their loans.

Overview
Loan Default Prediction with Machine Learning Course Overview
Machine Learning is about making predictions using data. In this course, you’ll learn to use basic Machine Learning skills to predict which customers are likely to default on their loans.

Once your model classifies each loan, you’ll learn to visualize your predictions to see how well the model performed.

Predicting defaults and creditworthiness is hugely valuable to risk management and pricing decisions.

We will cover the entire Machine Learning process in Python, reinforcing concepts from Python fundamentals. You’ll learn how to create predictive classification models, fine-tune and test your process, and how to interpret the results.

Machine Learning is a hot topic in the world of data, particularly data science. At a basic level, Machine Learning is not as complex as it may sound. If you’ve ever done linear regression, you may be surprised to learn that you’ve already taken steps toward this exciting world.

Join Andrew for a comprehensive step-by-step walkthrough of the Machine Learning process.

Loan Default Prediction with Machine Learning Objectives
Upon completing this course, you will be able to:

• Explain and discuss the main steps of the Machine Learning cycle
• Load and clean data into a python notebook
• Use Exploratory Data Analysis to identify variables with likely predictive power
• Use Feature Engineering to transform data into a more useful format
• Build a logistic regression and random forest prediction model
• Evaluate and compare model performance using common evaluation metrics

Who should take this course?
The Machine Learning cycle is one of the most foundational aspects of Data Science. Using this process, we can learn to make predictions using all types of data and variables. Anyone looking to make predictions in a practical Python environment should absolutely be doing this course.

What you’ll learn

• Introduction
  Course Introduction
  What is Machine Learning?
  Case Study Overview
  Course Materials
  Student Downloads
  Course Outline
• Load & Clean Data
  Chapter Introduction – Load and Clean Data
  Vehicle Loans Data Set
  Import Libraries and Data Set
  Exploring Basic Data Parameters
  Exploring Our Target Variable
  Identifying Missing Data
  Dealing with Missing Data
  Dates – Exploring Date Columns
  Dates – Calculating Age
  Dates – Extracting Month Number
  Strings – Exploring String Columns
  Strings – Extract Numbers from Strings
  Strings – Strings Function Exercise
  Strings – Strings Function Exercise review
  Chapter Summary
• Exploratory Data Analysis
  Chapter Intro – Exploratory Data Analysis
  Categorical, Continuous and Binary Variables
  Identifying Features (Columns) of Interest
  Dealing with Category or ID Columns
  Grouping Data by Categories
  Looking at Default Frequency Within Groups
  Exercise – EDA Function for Categorical Variables
  Exercise – Review
  Plotting Continuous Variables
  Plotting Continuous Variables by Group
  Exercise – EDA Function for Continuous Variables
  Exercise – Review
  Exploring Binary Variables
  Chapter Summary
• Feature Engineering
  Chapter Introduction – Feature Engineering
  Exploring Outliers
  Creating Bins
  Combining Features into New Columns
  Exercise – Creating New Columns
  Exercise – Review and Calculate Percentages
  Dealing with Null Values
  Min and Max Scaling
  Chapter Summary
• Classification with Logistic Regression
  Chapter Introduction – Classification with Logistic Regression
  Linear vs. Logistic Regression
  Train and Test Split
  Import Data and Modify Column Types
  Exercise – Select Chosen Variables
  Exercise – Review
  Exercise – Separate the Target Variable
  Exercise – Review
  Splitting the Data into Train and Test
  Dummy Variables
  Variable Encoding (One-Hot-Encoding)
  Exercise – Train, Test and Split One-Hot-Encoded Data
  Exercise Review and Testing Our Logistic Regression
  Chapter Summary
• Model Evaluation
  Chapter Introduction – Model Evaluation
  Student Exercise
  Review Logistic Regression Model
  Evaluation Metrics Theory
  Evaluation – Creating a Confusion Matrix
  Evaluation – Precision, Recall and F1 Scores
  The ROC Curve
  ROC Curve – Extracting Predicted Probabilities
  ROC Curve – Plotting the Curve
  Advanced Evaluation – Prediction Percentages
  Advanced Evaluation – Class Probability Distributions
  Advanced Evaluation – Plotting Class Probabilities
  Advanced Evaluation – Creating an Evaluation Function
  Chapter Summary
• Classification with Random Forest
  Chapter Introduction – Classification with Random Forest
  Decision Tree Theory
  Random Forest Theory
  Creating a Function for Train and Test Split
  Review the Train Test Split Function
  Exercise – Create a Simple Random Forest
  Reviewing the Random Forest
  Identifying Overfitting in Our Results
  Hyperparameters
  Testing the Impact of Number of Trees
  Testing the Impact of Maximum Depth
  Chapter Summary
• Improving Accuracy
  Chapter Introduction – Improving Accuracy
  Recap and Theory
  Exercise
  Balancing Classes Automatically
  Manual Class Balancing
  Resampling – Upsampling
  Training a New Model Based on Resampled Data
  Evaluating the Downsampled Model
  SMOTE
  Chapter Summary
• Qualified Assessment
  Qualified Assessment

This Course is Part of the Following Programs
Why stop here? Expand your skills and show your expertise with the professional.

Business Intelligence & Data Analyst (BIDA)®
Loan Default Prediction with Machine Learning is part of the Business Intelligence & Data Analyst (BIDA)®, which includes 33 courses.

• Skills Learned
  Data visualization, data warehousing and transformation, data modeling and analysis
• Career Pre
  pBusiness intelligence analyst, data scientist, data visualization specialist

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