The project utilized numerical and graphical summaries to explore the given housing market dataset, classifying homes based on their condition. Key objectives included predicting house prices and assessing house conditions using various machine learning models. Techniques such as logistic regression, random forest, linear regression, and resampling methods were employed.
Successfully categorized homes into poor, average, and good conditions.
Developed predictive models for house prices and conditions with significant accuracy.
Implemented robust resampling methods to estimate test errors and prevent overfitting.
To explore and preprocess the housing dataset.
To classify houses based on overall condition.
To predict house prices using machine learning models.
To evaluate the impact of lot size on house prices.
Programming Languages: R
Libraries/Packages: MICE (for multiple imputations), RandomForest, caret (for cross-validation)
Techniques: Logistic regression, Random Forest, Linear regression, k-fold cross-validation, Bootstrap, K-means clustering
Logistic regression and random forest models were effective in classifying house conditions.
The multiple linear regression model showed a high adjusted R-squared value, indicating strong predictive power.
Cross-validation techniques highlighted some overfitting in the initial models.
Logistic Regression: Used for classifying houses into different condition categories.
Random Forest: Applied for both classification and regression tasks, showing significant predictive accuracy.
Multiple Linear Regression: Used to predict house prices, demonstrating strong model performance with an adjusted R-squared value of 0.9085.
The findings suggest that machine learning models can significantly enhance decision-making processes in the housing market by providing accurate predictions of house conditions and prices. These models can be further refined and applied to other real estate datasets for broader applications.
The study demonstrated the effectiveness of machine learning models in predicting house conditions and prices. The combination of logistic regression, random forest, and multiple imputations for missing data provided robust predictions. Cross-validation helped in fine-tuning the models to reduce overfitting.