To explore how educational factors, such as the quality of nearby schools or proximity to universities, impact real estate prices.
To develop and implement a machine learning model that can accurately predict real estate prices based on these educational factors.
To evaluate the effectiveness of the model in predicting real estate prices in comparison to traditional methods.
Collect and analyze relevant data on real estate prices, educational factors, and other variables that may influence real estate prices.
Preprocess the data, including handling missing values, encoding categorical variables, and scaling numeric features.
Implement a machine learning model (e.g., linear regression, decision tree, random forest) to predict real estate prices based on the educational factors identified.
Evaluate the model's performance using metrics such as mean squared error, mean absolute error, and R-squared.
Compare the model's predictions to actual real estate prices and assess the impact of education on real estate pricing.
Write a detailed report summarizing the project, methodology, results, and conclusions.
