Michelle Wambaya

Real Estate Data Analytics A case study on estimating property prices using a synthetic dataset. Introduction 01 Project Overview Objective of the case study This case study aims to extract insights from a fictional dataset simulating the Kenyan real estate market. It focuses on understanding the main factors influencing property prices and develops predictive models to estimate these prices utilizing machine learning techniques. The project analyzes trends in the Kenyan real estate market by examining how key features such as property size, proximity to central business districts, and house types relate to pricing. Insights derived from the data will inform stakeholders about market dynamics and help potential buyers and sellers make informed decisions. Analysis of housing market trends The project utilizes machine learning techniques to build predictive models that accurately estimate property prices based on various features. By analyzing the synthetic dataset, the models can derive relationships between independent variables, such as the number of bedrooms and proximity to the city center, to forecast property values effectively. Predictive modeling for property prices 02 Machine Learning Models Overview of Linear Regression Linear regression serves as the foundational model for predicting property prices. It assumes a linear relationship between the independent variables (features) and the dependent variable (price). This model is straightforward to interpret, providing coefficients that represent the impact of each feature on the predicted price. Linear regression is evaluated based on metrics such as Mean Absolute Error (MAE) and R² Score. The Random Forest Regressor is an ensemble learning method that combines multiple decision trees to improve prediction accuracy and robustness. Unlike linear regression, it can capture complex, non-linear patterns in the data. This model assesses the importance of various features in predicting property prices and significantly reduces the risk of overfitting, making it a reliable choice for such analyses. Functionality of Random Forest Regressor To measure model performance, several key metrics are employed: Mean Absolute Error (MAE), which quantifies the average magnitude of errors in predictions, and R² Score, which indicates the proportion of variance in the property prices explained by the model. These metrics help in comparing model efficacy and guiding enhancements during the modeling process. Evaluation metrics for model performance The project demonstrates the practical application of machine learning in predicting property prices in the Kenyan real estate market. By leveraging synthetic data, insights regarding feature importance and market trends can significantly aid stakeholders in making informed decisions. The combination of Linear Regression and Random Forest Regression provides robust methodologies for estimating property values. Conclusions Thank you! Do you have any questions? https://michellewambaya.github.io -