Linear Regression Calculator

Build simple linear regression models client-side to predict outcomes like salary based on experience. Input data points, visualize the trend line, compute key metrics such as R-squared, and export results. Powered by TensorFlow.js for instant, privacy-focused calculations.
Real-time calculations Trend line visualization Key stats like R-squared Prediction input CSV/JSON export
Live Calculator
1337 × 42
56,154
Data: Input: 1,100 | 2,200 | 3,300
Prediction: For x=4: ~400
Stats: R-squared: 0.99

Linear Regression Calculator

Build simple linear regression models client-side to predict outcomes like salary based on experience. Input data points, visualize the trend line, compute key metrics such as R-squared, and export results. Powered by TensorFlow.js for instant, privacy-focused calculations.

Settings
Enter x,y pairs, one per line.
0.01
100 epochs

X =
Range
Model Information

No model trained yet.

Regression Graph
Statistics & Metrics
-
Slope (m)
-
Intercept (b)
-
-
RMSE
-
MAE
-
Pearson r
-
p-value

Equation:
y = mx + b
Prediction Result:
No prediction made yet.
Residual Analysis
Export Options

Help & Related Tools

Everything you need to know

FAQ Frequently Asked Questions

What features does the Linear Regression Calculator offer?
This tool provides real-time linear regression calculations, trend line visualization with Chart.js, key statistics like R-squared and slope, prediction capabilities, and exports to CSV or JSON. Ideal for quick ML predictions without any setup.
How do I use the Linear Regression Calculator?
Paste your data points in CSV format (e.g., "x,y" pairs) into the input field or add them manually. Adjust settings like learning rate, click Calculate, and view the graph and stats instantly. Enter a new x-value to predict y.
Is the Linear Regression Calculator compatible with all devices?
Yes, it works on all modern browsers and devices, including mobiles. All processing is client-side using TensorFlow.js, ensuring privacy and no installation required.
Are there any limitations I should know about?
The tool handles up to 1000 data points for optimal performance; larger datasets may slow down due to browser constraints. Accuracy depends on data quality, and it uses simple gradient descent for training.
Can I use this for business predictions like sales forecasting?
Absolutely! Input historical data (e.g., time vs. sales), train the model, and predict future values. Export results for further analysis in tools like Excel or BI software.

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