Brief Introduction
Learn how to iteratively develop Data Science modelsDescription
You will learn how to apply Agile Data Science techniques to Classification problems through 3 projects – Predicting Credit Card Fraud, Predicting Customer Churn and Predicting Financial Distress.
Each project will have 5 iterations labelled ‘Day 1’ to ‘Day 5’ that will gently take you from a simple Random Forest Classifier to a tuned ensemble of 5 classifiers (XGBoost, LightGBM, Gradient Boosted Decision Trees, Extra Trees and Random Forest) evaluated on upsampled data.
This course is ideal for intermediate Data Scientists looking to expand their skills with the following:
Automated detection of bad columns in our raw data (Day 1)
Creating your own metric for imbalanced datasets (Day 1)
Four Data Resampling techniques (Day 2)
Handling Nulls (Day 2)
Two Feature Engineering techniques (Day 3)
Four Feature Reduction techniques (Day 3)
Memory footprint reduction (Day 3)
Setting a custom scoring function inside the GridSearchCV (Day 4)
Changing the default scoring metric for XGBoost (Day 5)
Building meta-model (Day 5)
Complete Jupyter notebooks with the source code and a library of reusable functions is given to the students to use in their own projects as needed!
Requirements
- Requirements
- Students need to have taken introductory Data Science courses to be familiar with running Jupyter notebooks in Python
- Familiarity with sklearn packages
- Student should be able to setup their own Jupyter environment either in the laptops or on the cloud
Knowledge
- Machine Learning
- Agile Data Science
- Data Science
- Imbalanced Data
- Credit Card Fraud Prediction
- Customer Churn Prediction
- Financial Distress Prediction
- Feature Engineering
- Hyperparameter Tuning
- Ensemble Models
- Binary Classification
- XGBoost
Shreesha Jagadeesh
5 Raiting
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