ML Data Preprocessing

This project shows a simple way to prep data before using any machine learning model.
It’s meant to be easy to follow and learn from.

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What it does

Here’s what the code does:

• Handles missing values
• Encodes categorical (non-numeric) data
• Splits the dataset into training and testing sets
• Scales numerical columns so big numbers don’t mess up the model

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Dataset

The file Data.csv has:

• Country (France, Spain, Germany)
• Age
• Salary
• Purchased (Yes / No)

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How to use it

1. Put Data.csv in the same folder as any of workflow file
2. Run the Python file
3. After running, you’ll get:
   • x_train and x_test: prepped features
   • y_train and y_test: encoded target labels

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Notes

• Some steps, like scaling, aren’t needed for certain models (like Decision Trees or Random Forest).
• The code has comments explaining each step in a simple way.