# Introduction to Linear Regression: Exploring the Secrets of Prediction

Linear regression is a fundamental machine learning algorithm used for predicting a continuous outcome based on one or more input features. It assumes a linear relationship between the input features and the target variable, making it easy to interpret and implement. Visit the detailed tutorial here.

### Types of Linear Regression

Contents

There are two main types of linear regression:

#### Simple Linear Regression

Simple linear regression models the relationship between one independent variable and the dependent variable using a linear equation. For example, predicting house prices based on square footage.

#### Multiple Linear Regression

Multiple linear regression models the relationship between multiple independent variables and the dependent variable using a linear equation. Predicting house prices based on square footage, number of bedrooms, and location.

### Example: Predicting House Prices

Let’s consider a real estate scenario where we want to predict house prices based on various features. We have a dataset containing the following features: square footage, number of bedrooms, and location (represented as dummy variables for different neighbourhoods), along with the corresponding house prices.

Square Footage (X1) | Number of Bedrooms (X2) | Location (X3, X4, X5) | Price (y) |
---|---|---|---|

1500 | 3 | 1 0 0 | 250,000 |

2000 | 4 | 0 1 0 | 300,000 |

1800 | 3 | 0 0 1 | 280,000 |

2200 | 4 | 1 0 0 | 320,000 |

1600 | 3 | 0 1 0 | 270,000 |

### Steps to Build a Linear Regression Model

Here are the steps to build a linear regression model for this example:

#### Data Visualization

Plot the data points on scatter plots to visualize the relationships between the independent variables and the target variable (house prices).

#### Model Training

For simple linear regression, fit a model to predict house prices based on square footage. For multiple linear regression, fit a model to predict house prices based on square footage, number of bedrooms, and location.

#### Model Evaluation

Evaluate the performance of each model using metrics like Mean Squared Error (MSE) or R-squared. Compare the performance of the simple and multiple regression models.

#### Prediction

Use the trained models to make predictions on new, unseen data. For example, predict the price of a house with 1800 square feet, 3 bedrooms, and located in the first neighbourhood.

Linear regression is a versatile algorithm for predicting continuous outcomes based on input features. By understanding the differences between simple and multiple linear regression and applying them to real-world scenarios, students can effectively use linear regression for various prediction tasks, such as predicting house prices based on square footage, number of bedrooms, and location.

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