Johnnysnipes90/Nigeria-Housing-Price-Prediction

Predicting Housing Prices in Nigeria Analyzing Traditional Functional Forms and Machine Learning Techniques . This study investigates the effectiveness of various functional forms and machine learning techniques for predicting housing prices in Nigeria, using property features and location data. # Nigeria-Housing-Price-Prediction Predicting Housing Prices in Nigeria Analyzing Traditional Functional Forms and Machine Learning Techniques . This study investigates the effectiveness of various functional forms and machine learning techniques for predicting housing prices in Nigeria, using property features and location data. ### Predicting Housing Prices in Nigeria with Traditional Functional Forms and Machine Learning --- ## Project Description This project aims to develop a predictive model for housing prices in Nigeria using a combination of traditional regression models and machine learning techniques. By leveraging property features and location data, we investigate the accuracy and practicality of different methods, including functional transformations (e.g., linear, quadratic) and advanced machine learning models, to determine which approach best predicts housing prices in Nigeria. The project will culminate in a user-friendly Streamlit app that allows users to input housing features to obtain price predictions based on the selected model. --- ## Project Objectives 1. **Predict Housing Prices**: Use various traditional and machine learning models to accurately predict housing prices based on Nigerian market data. 2. **Analyze Effectiveness of Models**: Compare traditional functional forms (e.g., linear, quadratic) against machine learning models like Random Forest and Gradient Boosting to assess the most effective approach. 3. **Develop a Deployment-Ready App**: Build a Streamlit application to allow users to interact with the model and predict housing prices based on inputted features. --- ## Data Overview - **Source**: Housing data includes information on property features and location details, capturing attributes like property type, number of rooms, size, age, and location. - **Data Processing**: The data will be cleaned, preprocessed, and transformed for effective model training and validation. - **Features**: - **Property Characteristics …