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.

MachineLearning #AI #LearningJourney