https://github.com/cintia0528/data_science-supervised_machine_learning_regression

Predict home prices using regression, optimizing for R2 and RMSE metrics to create a robust model with minimal prediction deviation.
https://github.com/cintia0528/data_science-supervised_machine_learning_regression

ensemble-machine-learning lazypredict metrics r2 regressor-models sklearn supervised-machine-learning

Last synced: about 1 year ago
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Predict home prices using regression, optimizing for R2 and RMSE metrics to create a robust model with minimal prediction deviation.

• Host: GitHub
• URL: https://github.com/cintia0528/data_science-supervised_machine_learning_regression
• Owner: Cintia0528
• Created: 2023-12-14T11:13:48.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-14T11:35:15.000Z (over 2 years ago)
• Last Synced: 2025-03-31T05:35:17.184Z (about 1 year ago)
• Topics: ensemble-machine-learning, lazypredict, metrics, r2, regressor-models, sklearn, supervised-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 837 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **Predicting Home Prices with Regression**

## **Goal:**

The project aims to predict the prices of homes using *regression techniques*, evaluating the model's performance based on **R2** and **RMSE** metrics. The goal is to create a reliable model that **minimizes the deviation between predicted and actual home prices**.

## **Overview:**

In this machine learning journey, I explored regression to predict home prices, moving beyond a binary classification whether a home is expensive or not. The focus shifted to evaluating the model's ability to approximate actual home values. I experimented with **different models, hyperparameter tuning, feature selection, and preprocessing techniques** to enhance predictive accuracy.

## **Approach:**

1. **Baseline Model:** Implemented a *DecisionTreeRegressor* with *RandomSearch* hyperparameter tuning as a baseline, achieving an initial R2 of 0.79.

2. **Troubleshooting:** Explored options for improvement, including revisiting preprocessing choices and experimenting with more robust scalers, imputers, and models.

3. **Feature Importances:** Utilized *LazyPredict* to identify top-performing models and aggregated their feature importances. Explored the impact of removing features with less than 1% predictive power.

4. **Data Exploration:** Analyzed missing values, data types, scale, skewness, and correlation of features. Explored the significance of remaining features.

5. **Transforming, Scaling, and Imputing:** Implemented a *LogTransformer*, *MinMaxScaler*, and *KNN imputer* to enhance the model's ability to handle diverse numerical features.

6. **Final Model:** Employed a *VotingRegressor* on the top predictive features identified by the ensemble of models, achieving a final R2 of close to 0.95.

## **Deliverables:**

The project includes a **Google Colab notebook** containing the code for the regression analysis [here](https://github.com/Cintia0528/Project-8-Supervised-Machine-Learning-Regression/blob/ab4134a446ae810915e383f547aa33ae935c2efa/2_b_Housing_Feature_Selection.ipynb). The findings and methodology are further explained in a detailed **Medium article** [here](https://medium.com/@ubp0528/how-close-can-i-get-to-your-homes-true-value-with-regression-4e708ba1662f).

## **Skills and Tools:**

- Regression modeling

- Hyperparameter tuning

- Feature Engineering

- Data Exploration

- Preprocessing

- Python

- scikit-learn

- XGBoost

- LightGBM

- LazyPredict

## **Further Analysis:**

To enhance the model's performance:

- **Feature Engineering:** Continue exploring feature engineering techniques to create new meaningful features.

- **Ensemble Techniques:** Experiment with different ensemble models or stacking approaches to leverage the strengths of multiple models.

- **Advanced Hyperparameter Tuning:** Explore more advanced hyperparameter tuning techniques to fine-tune model parameters for better performance.

- **External Data:** Consider incorporating external datasets to provide additional information and improve predictive accuracy.

By focusing on these areas, the model's **R2** score and **RMSE** could potentially be further improved, providing more accurate predictions of home prices.