House Price Prediction

House Price Prediction is a Flask-based web application used to predict the prices of houses in Bangalore based on various factors such as location, BHK (bedrooms), number of bathrooms, and total square feet. Various regression algorithms were used, including Linear Regression, Decision Tree, K-Nearest Neighbors (KNN), and Random Forest, and the best-performing algorithm was selected. Linear Regression outshoned other algorithms by achieving an accuracy of 86.81%.

Demo:

The project is deployed using Render and can be viewed here.

Screenshot:

How to Run:

1. Clone this repository to your local machine using the following command:

   git clone <repository_url>
   

2. Install the required Python packages by running the following command:

   pip install -r requirements.txt
   

3. Start the Flask application by executing the following command:

   python app.py
   

4. Open your browser and go to

   http://127.0.0.1:5000
   

Tech Stack:

• Frontend: HTML, CSS, Bootstrap
• Backend: Flask
• Machine Learning: Scikit-learn
• Data Manipulation: NumPy, pandas, Matplotlib

Dataset:

The Dataset used in this project can be acquired from - Kaggle.

License:

This project is licensed under the MIT License. See the LICENSE file for more details.