This repository contains a simple machine learning model that predicts property prices based on a dataset of real estate features. The model was developed using Linear Regression and implemented in Jupyter Notebook.
The objective of this project is to predict the price of a property based on key features provided in the dataset. This serves as an introduction to regression modeling and machine learning.
- Model: Linear Regression
- Dataset: Includes features such as Building_class , zoning_class, Lot_extent and many more.
- Preprocessing: Data cleaning, handling missing values, and feature scaling.
- Performance: Evaluated using metrics like Mean Absolute Percentage Error (MAPE) , R-squared , Adj_Rsquare and MSE.