The House Price Prediction Model is a machine learning project aimed at predicting house prices based on various features such as location, size, number of rooms, and other relevant factors. This project utilizes regression techniques to provide accurate price estimates, assisting potential buyers and sellers in making informed decisions.
- Data Preprocessing: Cleaning and preparing the dataset for analysis.
- Exploratory Data Analysis (EDA): Visualizing data trends and relationships to gain insights.
- Feature Engineering: Selecting and transforming features to improve model performance.
- Model Training: Training various regression models (e.g., Linear Regression, Decision Trees, Random Forest) to predict house prices.
- Model Evaluation: Assessing model performance using metrics such as RMSE, R², and MAE.
- Predictions: Making predictions on new data and evaluating results.
- Python: Core programming language for development.
- Pandas: For data manipulation and analysis.
- NumPy: For numerical computations.
- Matplotlib/Seaborn: For data visualization.
- Scikit-learn: For machine learning algorithms and model evaluation.
- Jupyter Notebook: For interactive development and documentation.
Make sure you have Python installed on your machine. You can download it from python.org.
- Clone the repository to your local machine:
git clone https://github.com/your-username/HousePricePrediction.git
- Navigate to the project directory:
cd HousePricePrediction - Install the required Python packages using pip:
Install the required Python packages using pip:
pip install -r requirements.txt
- Open the Jupyter Notebook in the project directory:
jupyter notebook
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Open the House_Price_Prediction.ipynb notebook.
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Follow the steps outlined in the notebook to perform EDA, train models, and make predictions.
Example Usage To make a prediction, input the features of the house as specified in the notebook. The model will output the estimated price based on the trained data.
The performance of the model is evaluated using the following metrics:
- Root Mean Square Error (RMSE): Measures the average error of the predictions.
- R² Score: Indicates how well the model explains the variance in the data.
- Mean Absolute Error (MAE): Represents the average magnitude of the errors in a set of predictions.
- Future Enhancements
- Implement more advanced models (e.g., XGBoost, LightGBM).
- Include additional features such as economic indicators or local amenities.
- Deploy the model as a web application for easier access to predictions.
- Incorporate real-time data fetching for up-to-date predictions.