Singapore Resale Flat Prices Predicting

The objective of this project is to develop a machine learning model and deploy it as a user-friendly web application that predicts the resale prices of flats in Singapore. This predictive model will be based on historical data of resale flat transactions, and it aims to assist both potential buyers and sellers in estimating the resale value of a flat.

• Predictive Model Development: Build a machine learning model to predict resale prices of Singapore flats, using historical data on transactions from 1990 to present. Model accuracy will be evaluated with metrics such as MAE, MSE, RMSE, and R² Score.
• Feature Engineering and Training: Extract and preprocess features like town, flat type, floor area, storey range, and lease date to improve prediction accuracy. Train a suitable regression model on 70-80% of the dataset for reliable predictions.
• Web Application and Deployment: Create a Streamlit app allowing users to input flat details and view resale price predictions. Deploy the app on Render for public access, ensuring thorough testing for functional accuracy.
• Technologies used in this project Python — Visual Studio — Library required in IDE