This repository presents a case study on predicting housing prices in Ames, Iowa, using machine learning models. The project leverages the Ames Housing dataset, which contains detailed data on residential property sales between 2006 and 2010. The dataset offers a richer alternative to the Boston Housing dataset and is widely used in real estate price prediction studies.
The Ames Housing dataset, developed by Dean De Cock, includes 2930 residential property sales observations and 80 explanatory variables, encompassing nominal, ordinal, discrete, and continuous data types. These features cover physical property characteristics like lot size, dwelling square footage, and neighborhood, among others. The dataset is publicly available on Kaggle.
The project uses a CRISP-DM implementation methodology and the workflow is divided into several stages:
- Data Cleaning: Handling missing values, outliers, and data transformations.
- Feature Correlation: Identifying relationships between features and sale prices.
- Visual Exploration: Creating visualizations to understand key variables.
- Model Training and Evaluation: Building machine learning models to predict housing prices.
The primary goal of this project is to predict house sale prices based on various features using advanced regression models. This serves as a hands-on example for applying machine learning to a real-world problem, with a focus on regression analysis and features that correlate highly with one another
This project is designed for individuals with intermediate Python knowledge and some experience in data science. Understanding of regression techniques and libraries like Scikit-learn and Pandas is beneficial.
Required skills:
- Python programming
- Data analysis and manipulation with Pandas
- Machine learning techniques with Scikit-learn
- Data visualization using Matplotlib/Seaborn
For any queries or support, please contact:
- Developer: Daniel Akoko
- Email: [email protected]
© 2024 Daniel Akoko. All Rights Reserved.