Home
Journey into Machine Learning with Sklearn and Tensorflow
Feature Engineering for Machine Learning
Explore feature engineering using UCI's Abalone Dataset in this course. Enhance your skills in feature extraction, selection, and transformation to boost machine learning model performance. Learn to craft valuable features, apply different selection strategies, and use feature combinations to uncover data patterns.
Lessons and practices
Lesson 1: Exploring Feature Engineering with the UCI Abalone Dataset
Display Dataset Features
Display Dataset Descriptive Features
Display More Dataset Entries
Lesson 2: Navigating Practical Challenges in Feature Engineering
Implementing Median Imputation for Numeric Features
Applying Label Encoding to Categorical Data
Debugging the Feature Engineering Pipeline
Applying Categorical Encoding and Median Imputation on the Abalone Dataset
Lesson 3: Unlocking the Secrets of Feature Extraction with the Abalone Dataset
Calculating the Area
Volume As a Feature?
What's Their Density?
Calculate Relative Height
Lesson 4: Strategies for Effective Feature Selection in Machine Learning
Playing With Wrapper Method
Implementing Embedded Feature Selection Method using Lasso
Refining Feature Selection with f_classif
Lesson 5: Harnessing Feature Combinations for Enhanced Machine Learning Models
Create a New Feature by Multiplying Length and Diameter
Debugging Feature Combinations Analysis Code
Exploring New Feature Combinations in Abalone Dataset
Lesson 6: Unveiling the Power of Feature Interaction in Machine Learning Model Accuracy
Assessing the Impact of the 'Viscera_Shell' Feature on Model Performance
Alter the Linear Regression Model for a Different Feature Combination
Debugging 'Viscera_Shell' Feature
Effect on Model Performance by Engineering a New Feature
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.