Real estate agents get asked nearly everyday about land prices. Hence, predicting the expected average prices for the next years would help people to know the future market trends for their decision making.
In this project, a predictive model was built to predict the agricultural land price given other factors such as whether it is residential or commercial land -Property Classification-, deal date, area in square meters, and the lands region.
The -Agricultural Land Deals- datasets provided by the Ministry of Justice were used to train and test the predictive model. The deals available in the dateasets have occurred in different regions around the Kingdom of Saudi Arabia during 7 months period - From January 2019 till July 2019-.
This project went through several stages that included analyzing and visualizing data, feature engineering, detecting and removing outliers, and finally building the algorithm and evaluating using the evaluation metrics.