House Price Prediction
Machine learning project to predict house prices using Random Forest with a large dataset.
1. Project Overview
Machine learning project to predict house prices using Random Forest with a large dataset.
2. Problem / Challenge
Predicting accurate house prices using historical data and property features to assist buyers, sellers and real estate agents.
3. Approach / Methodology
Built a machine learning pipeline using Python and Pandas for data cleaning and feature engineering, then trained a Random Forest model to predict prices.
4. My Contribution
Performed data preprocessing, feature selection, model training and evaluation, and visualised results using Python libraries.
5. Results / Outcome
The project shows how Random Forest regression can be applied to structured housing data to produce price estimates from property attributes. It covers the full workflow from loading and cleaning the dataset through to encoding categorical features, training the model and reviewing which features carried the most weight.
6. What I Learned
This project reinforced the importance of separating training and test data properly, and of choosing regression metrics that can be explained in real terms rather than relying on a single score. It also provided practical experience with pandas for preparation and with interpreting feature importance as a sanity check against data leakage.