Project Overview The Bengaluru House Price Prediction project aims to predict house prices in Bengaluru by utilizing various house features. The goal is to provide users with an accurate estimation of house prices based on input parameters.
The data used in this project is sourced from Kaggle, a platform for predictive modeling and analytics competitions.
Python: The primary programming language used for the project. Numpy and Pandas: Utilized for data cleaning and manipulation. Matplotlib: Employed for data visualization and generating plots. Scikit-learn (Sklearn): Used for building the house price prediction model. Jupyter Notebook, Visual Studio Code, and PyCharm: Integrated Development Environments (IDEs) for coding and analysis. Python Flask: Employed as the HTTP server for the project. HTML/CSS/JavaScript: Utilized for creating the user interface.
Make sure you have the following dependencies installed before running the project:
pip install numpy pandas matplotlib scikit-learn flask
Clone the repository to your local machine. Install the project dependencies. Open the project in your preferred IDE (Jupyter Notebook, Visual Studio Code, or PyCharm). Run the necessary scripts or notebooks.
Once the project is set up, follow these steps for usage:
Execute the necessary scripts or notebooks to train the model. Start the Flask server for the web interface. Access the UI through a web browser. Model Evaluation The accuracy of the house price prediction model is assessed based on standard metrics, ensuring reliable and precise predictions.
Contact Information For any questions, feedback, or issues, feel free to reach out to Venu Gopal at [[email protected]].