Data Modeling for Machine Learning
Overview
Data Collection and Preparation - CORRECT ANSWER โโโ Gather and preprocess
raw data for analysis.
Feature Selection and Engineering - CORRECT ANSWER โโโ Identify and modify
key variables influencing outcomes.
Model Selection - CORRECT ANSWER โโโ Choose appropriate machine learning
algorithms for tasks.
Training the Model - CORRECT ANSWER โโโ Feed data to model for learning and
error minimization.
Evaluation and Validation - CORRECT ANSWER โโโ Measure model accuracy
using various performance metrics.
Model Tuning - CORRECT ANSWER โโโ Adjust hyperparameters to improve model
accuracy.
Deployment and Monitoring - CORRECT ANSWER โโโ Implement model in
production and track performance.
Data Relationships - CORRECT ANSWER โโโ Uncover dependencies and patterns
in data.
Data Quality - CORRECT ANSWER โโโ Address issues like missing values and
outliers.
Feature Engineering - CORRECT ANSWER โโโ Create new features to enhance
model performance.
Reducing Complexity - CORRECT ANSWER โโโ Simplify datasets to facilitate
analysis and visualization.
Model Interpretability - CORRECT ANSWER โโโ Understand how input data affects
model outcomes.
Scalability and Efficiency - CORRECT ANSWER โโโ Blueprint for data flow to
support larger datasets.
Consistent Data Preparation - CORRECT ANSWER โโโ Standardized models
support reuse across projects.
Descriptive Models - CORRECT ANSWER โโโ Analyze historical data to uncover
patterns.
Clustering - CORRECT ANSWER โโโ Group similar data points based on features.
Association Rule Mining - CORRECT ANSWER โโโ Identify correlations between
variables in datasets.
