Welcome to the Zillow Home Value Prediction project repository! This project aims to predict home values using machine learning techniques. By leveraging various features of homes and surrounding data, the model predicts the future values of homes listed on Zillow.
This project uses historical home sale prices and other relevant data to build a predictive model for estimating the current value of homes. The primary goal is to provide accurate and reliable home value predictions to assist homeowners, real estate agents, and buyers in making informed decisions.
- Data Preprocessing: Cleaning and preparing data for modeling.
- Exploratory Data Analysis (EDA): Understanding the data through visualization and statistical analysis.
- Feature Engineering: Creating new features to improve model performance.
- Model Training: Using various machine learning algorithms to train models.
- Model Evaluation: Assessing model performance using appropriate metrics.
- Prediction: Predicting home values based on trained models.
The dataset used in this project includes historical home sale prices and other related features. It can be obtained from Zillow's public dataset or any other relevant sources.
To get a local copy up and running, follow these steps:
- Clone the repository:
git clone https://github.com/ML-Project-Zillow-Home-Value-Prediction/Zillow-Home-Value-Prediction.git
- Navigate to the project directory:
cd Zillow-Home-Value-Prediction - Create and activate a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Install the required dependencies:
pip install -r requirements.txt
To run the project and make predictions, follow these steps:
Open the project in Jupyter Notebook or VS Code and run all the files.
The model used in this project is Linear Regression.
Thank you for checking out the Zillow Home Value Prediction project! We hope you find it useful and informative. Happy predicting!