# Applied Data Science Coding with Python: Linear Regression Algorithm

Linear Regression is a statistical method for predicting a continuous variable from one or more variables. Linear regression is one of the simplest and most widely used predictive models in machine learning. It assumes that the relationship between the independent variables and the dependent variable is linear.

The Linear Regression algorithm starts by defining a linear equation, where the independent variables are represented by the x’s and the dependent variable is represented by the y. The algorithm finds the best coefficients for the x’s (also called weights or parameters) such that the equation best fits the data by minimizing the difference between the predicted values and the actual values.

In order to use the Linear Regression algorithm in Python, you need to have a dataset that includes both the input data and the labels or outcomes. You also need to decide on the type of Linear Regression algorithm that will be used, such as simple linear regression, multiple linear regression, etc.

There are several libraries available in Python to implement the Linear Regression algorithm, such as scikit-learn, NumPy, and Pandas. These libraries provide pre-built functions and methods to build, train and evaluate a Linear Regression model.

It is important to note that Linear Regression algorithm assumes that the relationship between the independent variables and the dependent variable is linear, which might not always be true in real-world problems. Therefore, the performance of Linear Regression algorithm might not be as good as other algorithms if the relationship is non-linear.

In summary, Linear Regression is a statistical method for predicting a continuous variable from one or more variables. Linear regression is one of the simplest and most widely used predictive models in machine learning. It assumes that the relationship between the independent variables and the dependent variable is linear. There are several libraries available in Python to implement the Linear Regression algorithm, and it’s important to note that Linear Regression algorithm assumes that the relationship between the independent variables and the dependent variable is linear, which might not always be true in real-world problems.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to apply Linear Regression Algorithm in regression problems.

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