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.…