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Sometimes, we all need help to get around. Whether you're trying to find your way around a new city or just looking for a new car, sometimes you need a little help from the machine.

So what if we told you that there's some really cool stuff going on with car pricing right now?

Price prediction is a difficult problem. If you're buying a car, you want it to be within your budget, and you don't want to be taken advantage of by the dealer. A machine learning model (a statistical model) is able to make predictions based on past data.

There are many ways to predict prices, but one of the simplest is using a linear regression model. This model takes all of your prices and compares them with other cars that have similar features. The result is a predicted price based on those features.

We built a neural network model that could predict how much a car would cost based on its make and model, as well as how many miles it had been driven. This model proved that it could accurately predict the prices of cars better than any other method we had tried up until that point.

To Install this project, follow the steps above:

# Clone this repository
$ git clone https://github.com/dharmesh-kashyap/Boston-House-Price-Prediction
                        
# Create a virtual environment (optional but recommended):
$ python -m venv venv
                        
# Activating the virtual environment:
$ source venv/bin/activate
                        
# Install requirements:
$ pip install -r requirements.txt"

To use this project, follow the steps above:

# Running
python app.py"

Made with ❤️ by Dharmesh Kashyap, get in touch!