Building Your First Machine Learning Model and Model Validation - Day 9

Dive into Day 9 of Kaggle's 30 Days of ML Challenge, focusing on building your first machine learning model and understanding model validation. Learn to create a ML model using scikit-learn, work with large datasets, and discover patterns in big data. Explore techniques for measuring model quality through validation, including mean absolute error calculation. Follow along with tutorials and exercises from Kaggle's Intro to ML course, covering topics such as specifying prediction targets, creating predictive features, and evaluating model performance. Gain practical experience in exploratory data analysis, handling missing values, and choosing relevant features for your model. Perfect for aspiring data scientists and ML enthusiasts looking to develop a daily coding habit and enhance their Python-based machine learning skills.

Intro
What is the first ML model
What is model fitting
Picking the variables
Example
Columns
Exploratory Data Analysis
Missing Values
Drop Data Points
Drop Missing Values
Dot Notation
Choosing Features
Build Model
Steps in Building a Model
Step 1 Specify Prediction Target
Step 2 Create Predictive Features
How good is the model
What is model validation
What is mean absolute error
How to check mean absolute error
In sample score
Model validation