Housing Price Prediction with Linear Regression
This repository contains code for predicting housing prices using linear regression. The dataset includes features like location coordinates, housing characteristics, and demographic information. Key steps include data preprocessing, exploratory data analysis, feature engineering, and model building. Two linear regression models were trained and evaluated using statsmodels.OLS and sklearn.LinearRegression, achieving an RMSE of approximately 59,392 on the test dataset.
Key Features:
Data preprocessing to handle missing values and outliers.
Exploratory data analysis for insights into the data distribution.
Feature engineering, including the creation of dummy variables for categorical features.
Model building and evaluation using MSE and RMSE metrics.
Conclusion: This project demonstrates the application of linear regression in predicting housing prices. The provided code and analysis can serve as a reference for similar regression tasks in real estate or related domains.