House Price Prediction using R language

The purpose of this project is to use the R language to predict house prices based on various features, such as location, size, number of rooms, and other characteristics, using a dataset from Kaggle. In order to gain a better understanding of the data, we conducted exploratory data analysis, which included assessing the distribution of variables, identifying missing values, and detecting potential outliers. Additionally, we examined the correlation between the features and the target variable After performing data cleaning and pre-processing, we employed multiple machine learning algorithms, such as linear regression, random forests, and XGBoost, to construct predictive models. We evaluated the performance of these models using various metrics, such as mean absolute error, mean squared error, and root mean squared error. Our findings revealed that XGBoost had the best performance, achieving a root mean squared error of 0.13, indicating high predictive accuracy. We also performed a feature importance analysis to identify the most critical variables for predicting house prices. In summary, our project highlights the effectiveness of using R language and machine learning techniques to predict house prices while providing valuable insights into the key factors that influence house prices.