Judging the effectiveness of a machine learning model requires in-depth analysis. This quick liveProject builds on the work you have completed in Machine Learning for Classification. You’ll assess your early models and consider better alternatives. You’ll plot the ROC curves of the model and compare it to multiple dummy models, and tune your hyperparameters to deliver the most accurate results possible.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for Python data scientists who want to expand their capabilities in evaluating and tuning machine learning models. To begin this liveProject you will need to be familiar with:
- Basic Python
- Basic pandas
- Basic NumPy
- Basic Matplotlib
- Basic seaborn
- Basic scikit-learn
- Basic Jupyter Notebook
Basics of machine learning
Basics of exploratory analysis
you will learn
In this liveProject, you’ll learn skills for cleaning and exploring data in preparation for training a machine learning model.
- Manipulating data and exploratory analysis
- Evaluating predictions, accuracy, and other metrics
- Visualizing patterns with correlation pair plots and heatmaps
- Hyperparameter tuning with classes and other methods
- Preparing a model for production deployment