House Price Prediction using Random Forest Regressor

This project builds a machine learning model to predict house prices based on various features such as area, number of rooms, location, and building age.

The dataset is first cleaned and prepared using Pandas, where categorical data (like city names) are converted into numerical values for model processing.

Then, the data is split into training and testing sets. A Random Forest Regressor model from Scikit-learn is trained on the training data to learn the relationship between house features and their prices.

After training, the model’s performance is evaluated using Mean Absolute Error (MAE) and R² score to measure accuracy. Finally, the model can predict the price of a new house given its specifications.