Machine Learning for Excel Users: MATLAB Crash Course

Transitioning from Excel to MATLAB for machine learning (ML) involves moving from spreadsheet-based workflows to code-driven analysis. This guide bridges the gap by comparing Excel tasks to MATLAB equivalents and provides a step-by-step crash course.

Key Concepts Comparison

Task	Excel Approach	MATLAB Approach
Data Import	Open `.xlsx` file directly.	`data = readtable('data.xlsx');`
Handling Missing Data	Manually delete rows or use `IF()` formulas.	`data = rmmissing(data);` or `fillmissing(data, 'constant', 0);`
Feature Scaling	Use formulas: `=(X - AVERAGE(X))/STDEV(X)`	`data.Normalized = normalize(data.Value);`
Model Training	Limited to linear regression (Analysis ToolPak).	Use ML functions: `fitlm`, `fitcsvm`, `fitctree`
Predictions	Manually apply formulas to new data.	`predictions = predict(model, new_data);`
Evaluation	Calculate metrics with `COUNTIF`/`STDEV`.	`confusionmat(true_labels, predicted_labels);`

Crash Course: ML Workflow in MATLAB

Import Data
- Excel: Open file directly.
- MATLAB:
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```
data = readtable('your_data.xlsx');
```
Preprocess Data
- Remove missing values:
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```
data = rmmissing(data);
```
- Normalize features:
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```
data.Age = normalize(data.Age);
```

Train-Test Split

Excel: Manually copy rows.

MATLAB:

cv = cvpartition(size(data,1), 'HoldOut', 0.3);
train_data = data(cv.training,:);
test_data = data(cv.test,:);

Train a Model
- Example: Decision Tree
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```
model = fitctree(train_data, 'Label');
```

Evaluate Performance

Predict and calculate accuracy:

predictions = predict(model, test_data);
accuracy = sum(predictions == test_data.Label)/numel(predictions);

Visualize Results
- Plot confusion matrix:
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```
confusionchart(test_data.Label, predictions);
```

Example: Iris Classification

% Load data
load fisheriris;
data = array2table(meas, 'VariableNames', {'Length','Width','PetalLength','PetalWidth'});
data.Species = species;

% Split data
cv = cvpartition(data.Species, 'HoldOut', 0.3);
train_data = data(cv.training,:);
test_data = data(cv.test,:);

% Train model
model = fitctree(train_data, 'Species');

% Evaluate
predictions = predict(model, test_data);
accuracy = sum(predictions == test_data.Species)/numel(predictions);
disp(['Accuracy: ', num2str(accuracy)]);

Tips for Excel Users

Use MATLAB Apps (e.g., Classification Learner) for a GUI experience similar to Excel.
Vectorize Code instead of using loops (like Excel array formulas).
Leverage Tables for spreadsheet-like data structures.
Plot Liberally: Use plot(), scatter(), and histogram() for visual analysis.

Conclusion

MATLAB streamlines ML workflows with automated functions and powerful scripting, while still offering GUI tools for Excel-like interactions. Start with simple models (e.g., linear regression) and gradually explore advanced algorithms. Use MATLAB’s documentation and community forums for support.