Machine Learning - Building a regression model to predict prices of Houses
Welcome to my house price prediction notebook! In this project, I will be using the powerful machine learning algorithm called XGBoost to predict the sale prices of different houses.
As a homeowner or a real estate investor, it's essential to have a good understanding of the current and future trends in the housing market. One way to gain insight into the market is to predict the prices of houses based on different features, such as the location, size, number of rooms, and other attributes.
XGBoost is a popular algorithm for regression problems, and it's known for its high accuracy and speed. It's a type of gradient boosting algorithm that combines several weak models to create a robust and accurate predictive model.
In this notebook, I will be using a dataset of house prices and various features to train a model that can predict the sale price of a house accurately. I will start by exploring the dataset, visualizing the data, and cleaning the data. Then, I will use XGBoost to create a model that can predict the sale price of a house. I will evaluate the performance of the model and optimize it by tuning the hyperparameters.
Overall, this project is an exciting opportunity to apply machine learning techniques to a real-world problem and gain valuable insights into the housing market. I hope you enjoy following along as I explore and analyze the data and build a predictive model using XGBoost
By: Judith Okon