Abstract
This paper considers the relationship between the price of houses and the features namely the number of bedrooms, parking space, and different house types. In this study, a machine learning approach was used to develop prediction models that predicted house prices in Lagos. Different machine learning techniques were used, train-test split to split the data into training sets for training and building the model and test data to test the accuracy of the model, performance metric mean absolute error to set the baseline for the model, Variance Inflation Factor (VIF) to help remove multicollinearity between features and Streamlit interactive dashboards to communicate with the model. Correlation and regression methods were used to examine the relationship and build the model. It is observed that there is a strong positive correlation between the number of bedrooms and the number of toilets, likewise the number of bedrooms and the number of bathrooms. It also shows that there is a moderate positive correlation between the number of bedrooms and price. The model shows that the number of bedrooms, parking spaces, and house types play an important role in determining the price of houses.