Cohdhed/Nigeria-House-Price-Prediction-DSN-Hackathon-

This project aims to build a robust machine learning model that predicts house prices in Nigeria based on various features such as location, property type, number of bedrooms, bathrooms, parking spaces, and more. # House Price Prediction Project This project aims to build a robust machine learning model that predicts house prices in Nigeria based on various features such as location, property type, number of bedrooms, bathrooms, parking spaces, and more. By leveraging advanced regression algorithms, including XGBoost and Support Vector Regression, we seek to develop an accurate and reliable predictor for real estate prices. ## Key Features: Data Preprocessing: Cleaning and handling missing values to ensure high-quality input for modeling. Exploratory Data Analysis: Visualizing data trends and relationships to gain insights into the factors influencing house prices. Feature Engineering: Creating new features such as total_area and bathroom-to-bedroom ratios. Feature Scaling and Encoding. Model Selection: Support Vector Regressor. Feature Importance: Analyzing feature importance to understand the variables that impact house prices the most. Through this project, we aim to provide a valuable resource for anyone interested in predicting real estate prices based on property attributes. The comprehensive analysis and predictive modeling showcased here can serve as a foundation for similar data-driven applications in the real estate domain.