This report outlines the process of building a prediction model using linear regression for the sale price of a property. The project involves several steps including data preprocessing, data exploration, feature selection, model training, and evaluation. The preprocessing step involved handling missing values and encoding categorical variables in the dataset. The data exploration phase included an overview of the columns and a statistical analysis of the target variable (sale price). The visualization of the data was used to identify the most relevant features for the model. The linear regression model was trained on the selected features and evaluated for its performance. The model was then used to make predictions on unseen data, which can be useful for real estate appraisals or property valuations.