In this project, I have used linear regression algorithms to build a model that predicts the price of a house based on features such as availability(ready to move in or not), location, size of the house in square feet, and number of bathrooms. It is a supervised learning algorithm, since the price of the houses are already mentioned in the dataset. A lot of data cleaning was needed since there were many different values in the data and there were many outliers also. After removing the outliers and fitting the data to the model, I got an r2 score of 91.65%.
Email the author at bdvinod788@gmail.com