This repository contains markdown files with examples and interpretations of basic regression models and their XGBoost counterparts. The primary objective is to provide an accessible introduction to these models, emphasizing their interpretability and offering basic code examples to demonstrate their use. Please run the code in your IDE and you will see all the outputs for interpretation.
The models showcased in this repository serve as a foundation for understanding predictive modeling in the context of multifamily housing. They offer insights into how regression techniques can be applied to real-world scenarios, such as predicting the use of damage deposits and the likelihood of rental lead conversion.
classification.md: Provides an example and interpretation of a logistic regression model used for binary classification tasks.regression.md: Demonstrates a linear regression model for predicting a continuous outcome variable.xgboost_classification.md: Offers an XGBoost implementation for classification, alongside model interpretation.xgboost_regression.md: Details an XGBoost approach to regression, with focus on interpretation of results.
Each markdown file includes:
- A theoretical overview of the model type.
- Practical use cases in the housing industry.
- Code examples in Python for implementing the model.
- Detailed explanations of the output and model interpretation.
To explore these examples, you can view the markdown files directly on GitHub or clone this repository to your local machine using the following command:
git clone https://github.com/carterrees-entrata/dsProject_entrata_hack_2024.gitThe interpretations provided in each markdown file aim to demystify the output of both traditional statistical models and more complex machine learning models. They guide the reader through understanding the significance of model coefficients, feature importances, and performance metrics.