In this tutorial, we will delve into a practical case study on regression analysis. Regression is a powerful statistical method used to predict outcomes based on historical data. Let's get started!
Introduction to Regression
Regression analysis is widely used in various fields such as economics, finance, and engineering. It helps us understand the relationship between variables and make predictions. There are different types of regression models, such as linear regression, logistic regression, and polynomial regression.
Case Study: Predicting House Prices
For this case study, we will use a dataset containing information about houses, including their size, location, and price. Our goal is to build a regression model that can predict the price of a house based on its features.
Data Preparation
Before building the model, we need to preprocess the data. This involves cleaning the data, handling missing values, and encoding categorical variables.
Model Building
We will use a linear regression model to predict house prices. The model will be trained on the preprocessed dataset.
Model Evaluation
Once the model is trained, we need to evaluate its performance. This involves calculating metrics such as mean squared error and R-squared.
Model Deployment
After the model is evaluated and deemed satisfactory, we can deploy it to predict house prices for new data.
Further Reading
To learn more about regression analysis, you can visit our Introduction to Regression tutorial.