House Price Predictor - Poland

Overview

The House Price Predictor for Poland is a machine learning project designed to predict house prices based on various features. This repository contains the source code, data files, and trained models used in the project.

Project Structure

• data/: Contains the raw dataset (Houses.csv) and preprocessed data files (X_train.csv, X_test.csv, y_train.csv, y_test.csv).

• models/: Holds the trained models saved as pickle files (linear_regression_model.pkl, knn_model.pkl).

• src/: Contains the source code for data preprocessing, model training, and evaluation (preprocessing.py, linear_regression.py, knn.py, main.py).

• notebooks/: Includes Jupyter notebooks for Exploratory Data Analysis (EDA.ipynb) and modeling (Modeling.ipynb).

Usage

1. Dependencies:

   • Install project dependencies using pip install -r requirements.txt.

2. Run the Project:

   • Execute src/main.py to run the entire pipeline.

Models

1. Linear Regression:

   • Trained using scikit-learn's LinearRegression.
   • Model saved as models/linear_regression_model.pkl.
   • Evaluation metrics include Mean Squared Error, R2 Score, and Cross-Validation Scores.

2. K-Nearest Neighbors (KNN):

   • Trained using scikit-learn's KNeighborsRegressor.
   • Model saved as models/knn_model.pkl.
   • Evaluation metrics include Mean Squared Error, R2 Score, and Cross-Validation Scores.

Future Improvements

• Hyperparameter Tuning: Experiment with different hyperparameter configurations for improved model performance.

• Feature Engineering: Explore additional features or transformations to enhance model predictions.

• Visualizations: Enhance visualizations for better interpretation of results.

Contribution

Feel free to contribute to the project by opening issues, suggesting improvements, or submitting pull requests.

License

This project is licensed under the MIT License.