/California_House_Prices

This project uses Linear Regression to determine house prices in California.

Primary LanguageJupyter Notebook

California_House_Prices

Introduction

  • This is a project that trains a model to predict the prices of houses in California using linear regression.
  • This model uses real data collected during census which was conducted in the state.
  • Real Estate has been one of the rising investments in the world and having such a model will help home buyers to be financially aware of price range as well as factors to settle on while buying a house.

Technologies Used

Python Libraries

  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Scikit-learn
  • Python 3.6+
  • Jupyter Notebook
  • Other packages such as Basemap and pyplot are used alongside matplotlib.

Installation

  • The packages and libraries can be installed in the Anaconda cmd using

    $ python -m pip install
    

or in the IDE directly mostly if you're using PyCharm.

Machine Learning Algorithm

  • Linear Regression.