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Real Estate Price Prediction with Machine Learning

This repository hosts an end-to-end machine learning project dedicated to predicting real estate prices. The project involves various stages, including data loading, data analysis, preprocessing, model training, model evaluation, and utilizing the best model for price prediction.

Project Workflow

  1. Data Loading and Analysis:

    • Load the real estate dataset using Pandas.
    • Perform data analysis to understand the dataset's characteristics and relationships.

  2. Data Preprocessing:

    • Split the dataset into training and test sets.
    • Handle missing values, explore correlations between features, and perform necessary data transformations.

  3. Model Training:

    • Train machine learning models to predict real estate prices (e.g., Random Forest Regressor, Linear Regressor, Decision Tree Regressor).

  4. Model Evaluation:

    • Evaluate the trained models' performance using appropriate metrics.
    • Save the evaluation results in the Output from different models.txt file.

  5. Model Selection:

    • Select the best-performing model for real estate price prediction based on evaluation metrics.

  6. Price Prediction:

    • Use the selected model to predict real estate prices on new data.

Usage

To utilize or replicate this project:

  1. Clone the repository: 
    git clone https://github.com/wahabh7ck4r/Real-Estate-Price-Prediction.git
  2. Download the real estate dataset
  3. Explore the Jupyter notebooks to understand the project's workflow and code

Contributors

Contributions, issues, and suggestions are welcome!

License

This project is licensed under the MIT License.