[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122765-en":3,"doc-seo-122765-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122765,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models","The study examines how house prices in Lagos relate to key features including the number of bedrooms, parking space, and house type. Machine learning techniques are applied to build and evaluate predictive models using a train-test split, with mean absolute error used as a baseline performance metric. Variance Inflation Factor (VIF) helps address multicollinearity, while correlation and regression analyses clarify feature relationships. Interactive Streamlit dashboards support communication of model outputs.","Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models  \nMmesoma Peace Nwankwo   \nDepartment of Statistics, Faculty of Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nNdukaku Macdonald Onyeizu   \nDepartment of Computer Science, Faculty of Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nEmmanuel Chibuogu Asogwa   \nDepartment of Computer Science, Faculty of Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nChukwuogo Okwuchukwu Ejike   \nDepartment of Computer Science, Faculty of Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \nOkechukwu J. Obulezi 􀀍   \nDepartment of Statistics, Faculty of Physical Sciences, NnamdiAzikiwe University, Awka, Nigeria  \n\n| Suggested Citation |\n| --- |\n| Nwankwo, M.P., Onyeizu, N.M., Asogwa, E.C., Ejike, C.O. & Obulezi, O.J. (2023) . Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models. European Journal ofTheoretical and Applied Sciences, 1(5), 313-326. DOI: 10.59324/ejtas.2023.1(5).22 |\n\nAbstract:  \nThis 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  \ncommunicate 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.  \nKeywords: Train-test split, Variance Inflation Factor (VIF), Correlation, Ridge regression, Machine learning.  \nIntroduction  \nDifferent factors affect the price of houses in Nigeria, states, towns, and localities depending on choice. An increasing factor in a particular state might be a decreasing factor in another state. Lagos known as the commercial capital of  \nNigeria is known for its expensive house price, according to [forbes.com](forbes.com) it is ranked the 55 th most expensive city to live in the world. Ajah a suburb in Lagos state, is considered one of the best places to start a family. Unlike other areas such as Lekki, Ikoyi, and Victoria Island, Ajah is  \na mix of both upper-class and middle-class citizens.  \nMachine learning (ML) is the subset of artificial intelligence (AI) that focuses on building systems that learn or improve performance based on the data they consume. There are basically three types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised learning is effective for a variety of business purposes, including sales forecasting, inventory optimization, and fraud detection. Some examples of use cases include:  \n• Predicting real estate prices  \n• Classifying whether bank transactions are fraudulent or not  \n• Finding disease risk factors  \n• Determining whether loan applicants are low-risk or high-risk  \n• Predicting the failure of industrial equipment’s mechanical parts  \nRegression is a supervised learning technique that aims to find the relationships between the dependent and independent variables. Ridge regression is a method of estimating the coefficients of multiple regression models in scenarios where the independent variables are highly correlated (Hilt, & Seegrist, 1977) . It","cbCaipzLFbfyJpoL","https://ap.wps.com/l/cbCaipzLFbfyJpoL","pdf",1715264,1,14,"English","en",105,"# Introduction\n## Machine learning background\n## Regression and ridge regression\n# Literature review\n## House-price prediction approaches\n## Key influencing variables","[{\"question\":\"Which house features are used to predict house prices in Lagos?\",\"answer\":\"The study uses the number of bedrooms, parking space, and different house types as input features to predict house prices.\"},{\"question\":\"How is the predictive model evaluated in the study?\",\"answer\":\"A train-test split separates data for training and testing, and mean absolute error is used as a baseline metric to assess model performance.\"},{\"question\":\"Why is Variance Inflation Factor (VIF) included?\",\"answer\":\"VIF is used to reduce multicollinearity among features, helping make the regression modeling more reliable.\"}]","Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models | PDF",1785812785,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-house-prices-in-lagos-nigeria-using-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-house-prices-in-lagos-nigeria-using-machine-learning-models/122765/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which house features are used to predict house prices in Lagos?","Question",{"text":75,"@type":76},"The study uses the number of bedrooms, parking space, and different house types as input features to predict house prices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the predictive model evaluated in the study?",{"text":80,"@type":76},"A train-test split separates data for training and testing, and mean absolute error is used as a baseline metric to assess model performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is Variance Inflation Factor (VIF) included?",{"text":84,"@type":76},"VIF is used to reduce multicollinearity among features, helping make the regression modeling more reliable.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]