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README.md

Housing Price Prediction: Ensemble Stacking Regressor and Regression Models

A comprehensive case study on predicting housing prices using the Stacking Regressor from sklearn.ensemble alongside individual regression models such as linear regression, k-nearest neighbors (KNN) regressor, and decision tree regressor. The project focuses on leveraging ensemble learning techniques and statistical modeling for effective prediction.

Housing Price Prediction Using Ensemble

🎯 Purpose

The purpose of this project is to:

  • Build an accurate predictive model for housing price estimation using ensemble techniques.
  • Understand the contribution of various features to housing prices.
  • Enhance skills in regression modeling, ensemble learning, and performance evaluation.

📂 Dataset

Housing Prices Dataset

  • Description: This dataset contains features influencing housing prices, providing a foundation for regression and machine learning tasks.
  • Use Cases: Predictive modeling, feature engineering, and exploratory data analysis.
  • Data Dictionary:
    • price: Description not provided yet. (Data Type: int64)
    • area: Description not provided yet. (Data Type: int64)
    • bedrooms: Description not provided yet. (Data Type: int64)
    • bathrooms: Description not provided yet. (Data Type: int64)
    • stories: Description not provided yet. (Data Type: int64)
    • mainroad: Description not provided yet. (Data Type: object)
    • guestroom: Description not provided yet. (Data Type: object)
    • basement: Description not provided yet. (Data Type: object)
    • hotwaterheating: Description not provided yet. (Data Type: object)
    • airconditioning: Description not provided yet. (Data Type: object)
    • parking: Description not provided yet. (Data Type: int64)
    • prefarea: Description not provided yet. (Data Type: object)
    • furnishingstatus: Description not provided yet. (Data Type: object)
  • File Reference: housing_prices.csv 
    import pandas as pd
url = 'https://github.com/vmahawar/data-science-datasets-collection/raw/main/housing_prices.csv'
df = pd.read_csv(url)
print(df.head())

📊 Jupyter Notebook

Explore the complete analysis and model building process for predicting housing prices using Ensemble in the following notebook:

🛠️ Tools and Libraries Used

This project leverages the following tools and libraries:

  • Pandas: For data manipulation and preprocessing.
  • Numpy: For numerical computations.
  • Scikit-learn: For building regression models and implementing the Stacking Regressor.
  • Matplotlib & Seaborn: For data visualization to explore feature relationships and data distributions.
  • Statsmodels: For statistical analysis and calculating R-squared scores.
  • Jupyter Notebook: For developing and presenting the project in an interactive environment.

🌟 Key Learnings

  1. Feature Significance:

    • Analyzed the influence of features such as area, bedrooms, and stories on housing prices.
    • Utilized visualizations and statistical methods to highlight significant predictors.

  2. Model Performance:

    • Compared the performance of individual models, including Linear Regression, K-Nearest Neighbors (KNN), and Decision Tree Regressors.
    • Observed improved accuracy by combining models through the Stacking Regressor.

  3. Evaluation Metrics:

    • Assessed model quality using the R-squared metric to explain variance in housing prices.
    • Used the OLS module from statsmodels for detailed statistical insights.

  4. Ensemble Techniques:

    • Highlighted how the Stacking Regressor combines the strengths of multiple models to achieve better predictive performance.

📜 License

This project is licensed under the MIT License, permitting free usage for educational and non-commercial purposes.

🌐 Connect with Me

If you'd like to connect, collaborate, or provide feedback, feel free to reach out: