From Data to Decisions
Discover how raw data transforms into intelligent predictions through an interactive journey across every stage of the ML lifecycle.
A structured process that transforms raw data into a trained, deployable model
Gather raw data from various sources
Clean and transform data
Extract meaningful features
Choose the right algorithm
Teach the model patterns
Measure model performance
Put model into production
Track & retrain as needed
Gathering raw data from multiple sources
Data collection is the foundation of any ML project. Quality data from diverse sources leads to better model performance. Toggle the data sources below to see how data volume changes!
25,000 records
50,000 records
15,000 records
10,000 records
records collected
Organized in tables with rows and columns (CSV, SQL databases). Easy to analyze and process.
No predefined format (images, text, audio). Requires more preprocessing but contains rich information.
Cleaning and transforming raw data
Raw data is often messy. Preprocessing transforms it into a format suitable for machine learning. Toggle the options below to see the transformation!
| Name | Age | Salary | City |
|---|---|---|---|
| John | 25 | NULL | NYC |
| NULL | 32 | 75000 | LA |
| Alice | 999 | 55000 | Chicago |
| Bob | 28 | 62000 | NYC |
| Name | Age | Salary | City |
|---|---|---|---|
| John | 25 | NULL | NYC |
| NULL | 32 | 75000 | LA |
| Alice | 999 | 55000 | Chicago |
| Bob | 28 | 62000 | NYC |
Extracting and selecting meaningful features
Feature engineering transforms raw data into features that better represent the underlying patterns. Click on features to select the most important ones!
Create new features from existing ones (e.g., age groups from age)
age โ age_group
Keep only the most predictive features
Normalize features to similar scales
0 โ 1 range
Choosing the right algorithm for your problem
Different algorithms work better for different problems. Click on each model to learn about its strengths and best use cases!
Predicts continuous values using linear relationships
Binary classification with probability outputs
Makes decisions through branching logic
Ensemble of decision trees for robust predictions
Classifies based on nearest neighbors
Deep learning for complex patterns
Teaching the model to recognize patterns
Training is where the model learns from data. Adjust the parameters below and watch the model learn in real-time!
Measuring model performance
Evaluation tells us how well our model performs. Adjust the threshold slider to see how it affects different metrics!
Overall correctness
Positive predictions
Found positives
Balanced measure
Putting the model into production
Deployment makes your model accessible to users. Try the live prediction simulation below!
Click "Get Prediction" to see the result
Instant responses for individual requests. Best for interactive applications.
Process large datasets at once. Best for periodic reports and bulk scoring.
Keeping your model performing well over time
Models can degrade over time as data changes. Monitoring helps detect issues before they impact users. Click "Simulate Drift" to see how performance degrades!
Last retrained
Click on any stage to learn more about it
Gather data from multiple sources
Clean and transform data
Extract meaningful features
Choose the right algorithm
Teach patterns to the model
Measure performance
Put into production
Track & retrain