In this project, we use existing housing price data to develop machine learning models for predicting future home prices.
- Developed regression models using real estate data from the 2011 Ames Iowa Housing Data on Kaggle containing 2930 observations and 81 fields (features)
- Performed exploratory data analysis (EDA) with NumPy, Pandas, and Seaborn to understand the missing data and observe which features have strong correlations with the final sale price
- Completed full data preprocessing by systematically handling all missing values in the raw data set: removed outlier observations (3) and filled all null values with either "0", "None", or a statistical estimate (e.g., mean), depending on the appropriate choice for each feature.
- Used Scikit-learn to develop regression models using an elastic net model, ridge and lasso regularization, ordinary linear regression, and a random forest regressor
- Performed grid search cross validation to find optimal values of hyperparameters
- Employed lasso regression and achieved mean absolute error (MAE) of $14,191 and RMS error of $20,554, where the average house price in the data set was $180,815 (relative MAE = 7.8%)
