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Learn how to manage data normalization and denormalization using `fit_transform` and `inverse_transform` across separate scripts in Python.
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How to Efficiently fit_transform and inverse_transform Across Different Scripts in Python

In the world of data science and machine learning, normalizing data is a common practice, especially when dealing with regression tasks. However, when working across different scripts, you may encounter challenges in maintaining the normalization parameters, which can lead to discrepancies in your predictions. This guide will explore the problem of performing fit_transform and inverse_transform on different scripts and provide a comprehensive solution to ensure consistency in your data transformations.

The Problem: Discrepancies in Predictions

The main issue arises when you normalize your numerical targets in one script and then attempt to predict in real time using another script. If the scaling parameters (like min and max values) used in normalization are not properly maintained, the predictions may not align with the original data range, leading to frustrating errors. Let's summarize the situation:

First Script: Normalizes the numerical targets (integers).

Second Script: Used for real-time predictions.

Third Script: Contains the definitions for fit_transform and inverse_transform methods.

Result: The predicted values come out as small float numbers, rather than being in the same range as the original targets.

The Solution: Manual Normalization and Denormalization

Through experimentation and assistance from the community, the solution to this problem involves manual calculations for normalization and denormalization, rather than relying solely on the MinMaxScaler or similar tools. Here’s how you can implement this:

Step 1: Normalize your Data

In your first script, instead of using scaler.fit_transform, you can manually apply the normalization formula. This requires knowing the minimum and maximum values of your original data.

[[See Video to Reveal this Text or Code Snippet]]

myArray: Your original data array.

myArrayMin: The minimum value of your original data.

myArrayMax: The maximum value of your original data.

This line effectively scales the values in myArray to a range between 0 and 1.

Step 2: Denormalize your Predictions

Once you have the normalized predictions from your second script, you will use the same min and max values to bring the predictions back to the original scale. Here’s how it looks in code:

[[See Video to Reveal this Text or Code Snippet]]

myPredicedArray: Your predicted values that are in the normalized range.

myDenormalizedArray: The resulting array, rescaled back to the original range.

Summary

By using manual normalization and denormalization formulas, you can easily manage and maintain the integrity of your predictions across different scripts. The steps can be summarized as follows:

Normalize your original data using the formula:

[[See Video to Reveal this Text or Code Snippet]]

Denormalize your predicted data with the formula:

[[See Video to Reveal this Text or Code Snippet]]

This approach ensures your results remain consistent and accurately reflect the original data scale.

Conclusion

Data normalization and denormalization are crucial steps in the data preprocessing phase, especially when predictions are generated across different scripts. By following the outlined procedures, you can mitigate issues related to scaling and ensure that your predictions are meaningful and accurate. If you have any further questions or need additional clarifications, feel free to leave a comment below!