Project Summary
Ask a home buyer to describe their dream house, and they probably won’t begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground project proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
Project Overview: Utilized Python to develop an advanced regression model to predict house prices using machine learning techniques. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this project focused on analyzing and forecasting real estate values based on various property features, with the goal of providing accurate price estimates for buyers and sellers.
- DELIVERABLES
- Utilized Python to develop a machine learning model to predict house prices, leveraging advanced algorithms to provide accurate and reliable valuation estimates.
- Collected and cleaned a diverse dataset of housing features, including location, size, number of rooms, and amenities, to build a robust training foundation.
- Applied feature engineering techniques to create meaningful predictors from raw data, such as creating new features from categorical variables and handling missing values.
- Utilized regression algorithms, including logistic regression, decision trees, and ensemble methods like Random Forests, to assess model performance.
- Implemented cross-validation techniques to evaluate model generalization and prevent over-fitting, achieving high accuracy and predictive power.
- Created visualizations such as scatter plots, residual plots, and feature importance charts to provide insights into model predictions and feature impacts.
- Conducted sensitivity analysis to understand how different features impact house prices, guiding feature selection and model refinement.
- ANALYSIS IMPACT
- Contributed to better market understanding and enhanced property valuation strategies.
- Enabled more accurate pricing assessments in the real estate market, assisting buyers and sellers in making informed decisions.