Project-3-HDB-Resale-Price-Prediction

Overview

This project aims to predict the resale prices of HDB flats in Singapore using machine learning models. The models used in this project are:

• Gradient Boosting Regressor
• Neural Network
• Random Forest Regressor

The goal is to provide accurate predictions to assist buyers, sellers, and policymakers in making informed decisions.

Requirements

• pandas
• numpy
• scikit-learn
• tensorflow (for neural network)
• matplotlib (for visualization)

Data Preprocessing

Load the Dataset:

Load the HDB resale transaction data into a pandas DataFrame.

Convert 'month' to Datetime Format:

Convert the 'month' column to datetime format to enable time-based analysis and feature extraction.

Calculate Total Remaining Lease:

Transform the 'remaining_lease' column into a single numerical format representing the total number of months.

Extract 'Year' and 'Month_num' from 'month':

Extract the year and month from the 'month' column to use as features.

Identify and Preprocess Categorical and Numerical Columns:

Use ColumnTransformer to apply standard scaling to numerical columns and one-hot encoding to categorical columns.