Machine Learning Inside Google Sheets and Excel: Top Add-Ons and Packages for Everyday Users

How to run real decision-forest AI models, fill missing spreadsheet cells, detect unusual rows, and forecast future sales without ever leaving Google Sheets or Excel.

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For over a billion people around the world, the spreadsheet is the ultimate workspace. Whether you are tracking household budgets, managing school grades, analyzing e-commerce inventory, or auditing monthly accounting ledgers, your data already lives inside Google Sheets or Microsoft Excel.

Normally, applying machine learning to a spreadsheet requires exporting the sheet to a .csv file, loading it into an external AI tool or Python environment, running a model, and importing the results back.

What if the machine learning engine lived right inside a sidebar in your spreadsheet?

Thanks to browser-based WebAssembly technology and modern spreadsheet add-ons—most notably Simple ML for Sheets (created by Google's TensorFlow Decision Forests team)—you can now train machine learning models, predict missing values, and spot abnormal errors in your rows with three clicks, right inside your sheet.

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Tool 1: Simple ML for Sheets (by the TensorFlow Team)

Simple ML for Sheets is a free Google Workspace add-on built by the engineers behind TensorFlow. What makes it remarkable is not just that it requires zero coding or machine learning knowledge, but also how it protects your privacy:

100% In-Browser Training: Simple ML runs TensorFlow Decision Forests compiled to WebAssembly directly inside your web browser. Your spreadsheet data is not sent to third-party servers to train models, and training a model on thousands of rows takes only 3 to 5 seconds.

The 4 Everyday Tasks Simple ML Solves in 2 Clicks

Once you open Extensions $\rightarrow$ Simple ML for Sheets $\rightarrow$ Start, a clean sidebar appears on the right side of your spreadsheet asking what you want to do:

[Google Sheet Table] ──► [Simple ML Sidebar]
                               ├──► 1. Predict Missing Values (Fill blank cells with AI)
                               ├──► 2. Spot Abnormal Values (Flag typos & outliers)
                               ├──► 3. Forecast Future Values (Time-series projections)
                               └──► 4. Train / Understand a Model (Advanced inspection)

1. Predict Missing Values

Suppose you have a table of 500 apartments or products, and 40 rows are missing their Category or EstimatedPrice cells.

• Leave the blank cells empty in your column.
• Select Predict Missing Values in the sidebar, pick the column with the blanks, and click Predict.
• Behind the scenes, Simple ML trains a Gradient Boosted Decision Tree on the 460 complete rows and fills in the 40 missing cells—alongside a second column showing the AI's Confidence % for each prediction!

2. Spot Abnormal Values (Outlier & Error Detection)

Have you ever wondered if a human data-entry mistake or a strange transaction is hiding in a 3,000-row spreadsheet?

• Select Spot Abnormal Values, pick the target column you want to audit (e.g., OrderTotal or ShippingWeight), and click Spot.
• Simple ML trains a model to understand what a "normal" row looks like based on all the other columns, and assigns an Abnormality Score (0.0 to 1.0) to every row. A $500 charge might be completely normal for a corporate bulk order, but highly abnormal (0.98) for a single-item student order—and Simple ML catches that context automatically!

3. Forecast Future Values

If your spreadsheet has a Date column and a numerical column like DailyVisitors or WeeklySales, the Forecast Future Values task automatically extracts day-of-week, monthly, and yearly patterns to project future dates directly into new rows.

4. Explain a Model ("Understand a Model")

Want to know which columns in your spreadsheet actually drive outcomes? Training a model and clicking Understand a Model generates summary charts right in your sidebar showing variable importance and how individual columns influence predictions.

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Tool 2: Built-In Forecasting and ML Add-Ins in Microsoft Excel

If your workflow centers on Microsoft Excel, you have several no-code and low-code ways to run predictive analytics directly on .xlsx workbooks. And if you ever need to convert raw web data into an .xlsx workbook before opening Excel or Google Sheets, you can use our client-side JSON ↔ Excel Converter.

Excel Feature / Add-On	Best Use Case	Coding Required?
Forecast Sheet (Data -> Forecast Sheet)	Built into Excel: uses the AAA version of Exponential Smoothing (ETS) to automatically detect seasonality and generate a forecast chart with upper/lower confidence bounds in one click.	No (100% point-and-click)
Analysis ToolPak (Built-In Add-In)	Run multiple Linear Regression, moving averages, exponential smoothing, and ANOVA tables directly across cell ranges.	No (Dialog box selections)
XLSTAT	A comprehensive statistical and machine learning add-in for Excel supporting Decision Trees, Random Forests, k-Means Clustering, and Logistic Regression via menus.	No (Menu-driven GUI)
Python in Excel (=PY())	Built into modern Microsoft 365: type =PY() in a cell to run scikit-learn or statsmodels directly on Excel tables.	Low-Code (Short snippets)

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Quick Pre-Check Before Running Spreadsheet ML

Even when an add-on handles the math automatically, spending two minutes checking your spreadsheet layout prevents the most common beginner mistakes:

1. One Header Row Only: Machine learning add-ons expect Row 1 to contain unique column titles (Date, Product, Price, Region) and Row 2 onward to contain data records. Remove decorative title banners or merged header cells before running the add-on.
2. Exclude "Total" and "Average" Summary Rows: If Row 501 of your table is a =SUM(D2:D500) grand total row, do not include Row 501 in your training selection—otherwise the AI will think that giant sum is a single massive customer order! For quick checks on column averages, medians, or category breakdowns without adding extra summary rows to your training table, you can paste a column into our Statistics Calculator or visualize category shares in a Pie Chart.
3. Keep ID Columns Unchecked: When Simple ML or XLSTAT asks which columns to use as input features, uncheck unique ID columns like InvoiceNumber, RowID, or PhoneNumber. Unique IDs carry no predictive pattern and can cause the model to memorize individual rows.

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Conclusion

The easiest place to start using machine learning is inside the tool you already open every day. Whether you use Simple ML for Sheets to intelligently fill missing cells and catch data-entry anomalies in Google Sheets, or Forecast Sheet and XLSTAT inside Excel, you can bring predictive AI to your everyday spreadsheets in minutes—no coding required.