Presented as part of PyCloud on 30th May “Identifying defaulters using Machine Learning", Ritesh Modi, Hon. Microsoft Regional Director People take credit and they repay to lenders however at times they default on their payments. This can happen due to multiple reasons. In this session, we will take up a dataset consisting of customer profile, preferences, past history and payment details and using classification try to identify if he/she is going to default on her payments. This session will contain the entire machine learning process of loading, cleaning data alongside a classifier to predict the next set of defaulters using Python.

1. Identifying credit Defaulters
using machine learning
Ritesh Modi

2. A bit about me
• Architect, Evangelist,
• Worked with largest software and consulting company
• Mentor at multiple startup incubators
• Blockchain, AI, Infrastructure, Cloud and DevOps Specialist
• Author for MSDN and Open Source India Magazine
• Speaker at national and international conferences
• Technology enthusiast and avid blogger

3. Process for Machine learning
Pre-processing
Data cleaning
Exploratory
analysis
Scaling
Feature
encoding
Train-Test split
TrainModel
Feature
selection and
Engineering
Learning
algorithm
Evaluation
Model Scoring
Model
evaluation
F(x)

4. Types of algorithms
Supervised
• Regression
• Classification
Unsupervised • Clustering

5. Understanding Classification

6. Classification
Label A/ Class A
Label B/ Class B

7. Types of classification
Logistic
Regression
Naïve Bayes Decision Tress
Random
Forest
Support
Vector
Machines
K-Nearest
Neighbours

8. Weather
today
Sunny
Humidity
Yes no
Windy
Yes no
raining
Humidity
Yes no
Windy
Yes no
Root
Level 1
Node
Level 2
Node
Leaf Node
Prob % > 75 Prob % < 75

10. Confusion
Matrix

11. https://interviewbubble.com/accuracy-recall-precision-f1-score/

12. advantages
• Simple intuitive way of classification
• Interpretations of complex models can be easily visualized
• Less number of hyperparameters to fine tune

13. disadvantages
• Can be time consuming depending on complexity
• High changes for overfitting
• Small change in data can change the model itself

14. Overfitting vs underfitting
Source: https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76

15. Other concepts
• Imputation
• Finding missing values and replacing with mean, median, mode etc
• Feature scaling
• Standardization
• Regularization
• Feature categorical encoding
• One Hot encoding
• Label encoding

16. Normalization and Standardization

17. Imputation and Feature encoding
• Lets look at them in code

18. Questions

19. Thank you