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This is a python program that is to predict the price of a house in Boston. It uses data from the city of Boston, Massachusetts and the Kaggle, which is a data set containing information about real estate transactions in the city.
The main function of this program is to get the data from Kaggle and import it into pandas and numpy. Pandas allows you to easily manipulate dataframe objects like lists or dictionaries using Python’s built-in functions, like map() or filter(). In this case, we are using it to get all the fields in our dataset and then write them out to files on disk. We then use these files as input into our model.
The next step is to create a model using scikit-learn fit method. This method takes your model and measures how well it fits with the data, along with an estimate of how accurate your model will be at making predictions in the future (R_2).
After creating our model, we evaluate our model by running several different k-fold cross-validation experiments on our data set (there are more than 100 folds). This helps us determine which parameters are most important for making accurate predictions
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!
