Module 1: Introduction 1. Course Introduction Free Preview 2. What is Machine Learning? 3. Case Study Overview 4. Course Materials 5. Student Downloads 6. Course OutlineModule 2: Load & Clean Data 1. Chapter Introduction - Load and Clean Data Free Preview 2. Vehicle Loans Data Set 3. Import Libraries and Data Set 4. Exploring Basic Data Parameters Free Preview 5. Exploring Our Target Variable 6. Identifying Missing Data 7. Dealing with Missing Data 8. Dates - Exploring Date Columns 9. Dates - Calculating Age 10. Dates - Extracting Month Number 11. Strings - Exploring String Columns 12. Strings - Extract Numbers from Strings 13. Strings - Strings Function Exercise 14. Strings - Strings Function Exercise review 15. Chapter SummaryModule 3: Exploratory Data Analysis 1. Chapter Intro - Exploratory Data Analysis Free Preview 2. Categorical, Continuous and Binary Variables 3. Identifying Features (Columns) of Interest 4. Dealing with Category or ID Columns 5. Grouping Data by Categories 6. Looking at Default Frequency Within Groups 7. Exercise - EDA Function for Categorical Variables 8. Exercise - Review 9. Plotting Continuous Variables 10. Plotting Continuous Variables by Group 11. Exercise - EDA Function for Continuous Variables 12. Exercise - Review 13. Exploring Binary Variables 14. Chapter SummaryModule 4: Feature Engineering 1. Chapter Introduction - Feature Engineering Free Preview 2. Exploring Outliers 3. Creating Bins 4. Combining Features into New Columns 5. Exercise - Creating New Columns 6. Exercise - Review and Calculate Percentages 7. Dealing with Null Values 8. Min and Max Scaling 9. Chapter SummaryModule 5: Classification with Logistic Regression 1. Chapter Introduction - Classification with Logistic Regression Free Preview 2. Linear vs. Logistic Regression 3. Train and Test Split 4. Import Data and Modify Column Types 5. Exercise - Select Chosen Variables 6. Exercise - Review 7. Exercise - Separate the Target Variable 8. Exercise - Review 9. Splitting the Data into Train and Test 10. Dummy Variables 11. Variable Encoding (One-Hot-Encoding) 12. Exercise - Train, Test and Split One-Hot-Encoded Data 13. Exercise Review and Testing Our Logistic Regression 14. Chapter SummaryModule 6: Model Evaluation 1. Chapter Introduction - Model Evaluation Free Preview 2. Student Exercise 3. Review Logistic Regression Model 4. Evaluation Metrics Theory 5. Evaluation - Creating a Confusion Matrix 6. Evaluation - Precision, Recall and F1 Scores 7. The ROC Curve 8. ROC Curve - Extracting Predicted Probabilities 9. ROC Curve - Plotting the Curve 10. Advanced Evaluation - Prediction Percentages 11. Advanced Evaluation - Class Probability Distributions 12. Advanced Evaluation - Plotting Class Probabilities 13. Advanced Evaluation - Creating an Evaluation Function 14. Chapter SummaryModule 7: Classification with Random Forest 1. Chapter Introduction - Classification with Random Forest Free Preview 2. Decision Tree Theory Free Preview 3. Random Forest Theory 4. Creating a Function for Train and Test Split 5. Review the Train Test Split Function 6. Exercise - Create a Simple Random Forest 7. Reviewing the Random Forest 8. Identifying Overfitting in Our Results 9. Hyperparameters 10. Testing the Impact of Number of Trees 11. Testing the Impact of Maximum Depth 12. Chapter SummaryModule 8: Improving Accuracy 1. Chapter Introduction - Improving Accuracy Free Preview 2. Recap and Theory 3. Exercise 4. Balancing Classes Automatically 5. Manual Class Balancing 6. Resampling - Upsampling 7. Training a New Model Based on Resampled Data 8. Evaluating the Downsampled Model 9. SMOTE 10. Chapter SummaryModule 9: Qualified Assessment 1. Qualified Assessment 2. Share Your Feedback 3. Course Completion

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Andrew will guide you through the Machine Learning process step-by-step.


 Machine Learning is a hot topic within the data science and data mining worlds. Machine Learning isn't as complicated as it sounds at a fundamental level. You may be surprised to discover that you have already made steps towards this exciting new world if you have ever done linear regression.


 The entire Machine Learning process will be covered in Python. This will reinforce concepts from Python basics. Learn how to create and fine-tune predictive classification models, test them, and how to interpret their results.


 It is extremely valuable for pricing and risk management to predict defaults and creditworthiness.


 After your model has classified each loan, you can visualize your predictions and see how the model performed.


 Machine Learning is the ability to make predictions using data. This course will teach you how to use Machine Learning skills to predict the likelihood of customers defaulting on loans.

Learn "Loan Default Prediction with Machine Learning" "Certification" by "Corporate Finance Institute (CFI)" and upskill your career by acquiring skills like Linear Regression etc with Careervira.

Loan Default Prediction with Machine Learning

Loan Default Prediction with Machine Learning

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