Predicting Housing Prices in King County using Data Analytics

Project Overview

This project utilizes the King County housing data set to predict housing prices using various machine learning models. It includes data preprocessing, feature analysis, and model evaluation techniques to achieve predictions using regression models.

Files

• Predicting Housing Prices in King County.ipynb: Jupyter notebook with all steps, including data cleaning, visualization, and modeling.
• Dataset: Includes housing data with features like square footage, number of bedrooms, number of floors, etc.

Steps Performed

1. Data loading and exploration
2. Data cleaning (removing missing values, handling categorical variables)
3. Visualization of relationships between features and prices
4. Building and evaluating Linear Regression, Ridge Regression, and Polynomial Regression models
5. Hyperparameter tuning using Grid Search
6. Performance evaluation using R² and MSE

Technologies Used

• Python (Pandas, Seaborn, Matplotlib, Scikit-learn)
• Jupyter Notebook for data analysis and visualization

How to Run

1. Clone or download the repository
2. Open the Jupyter Notebook (Predicting Housing Prices in King County.ipynb) in a local Jupyter environment or JupyterLab.
3. Ensure the required libraries are installed (Pandas, Seaborn, Matplotlib, Scikit-learn).
4. Run the notebook cells in sequence to complete the analysis and predictions.

License

This project is licensed under the MIT License.