Today I continued my Machine Learning journey and learned about Multiple Linear Regression.
After understanding linear regression with a single input, this concept made more sense because it extends the same idea to multiple features.
📌 What is Multiple Linear Regression?
Multiple Linear Regression is used to predict a numerical value using more than one input.
For example, predicting the price of a house depends on several factors such as:
- Size
- Number of rooms
- Location
Instead of relying on one feature, the model combines all of them to make a more accurate prediction.
🧠 How it Works
Each input has a weight that represents its importance. The model adjusts these weights to minimize prediction error.
This means the model learns how much each factor contributes to the final result.
💡 Key Insight
This concept shows how machine learning models handle real-world problems where multiple factors influence outcomes.
🚀 Reflection
Today’s lesson felt more practical and closer to real-world applications. It helped me see how machine learning can be used in more complex scenarios.
✨ Step by step, I’m building a strong foundation in Machine Learning.
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